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1. ath Function 1 umerical real complex Inherited Not applicable Dimension of input parameter Single number vector matrix Z P ath Function 2 umerical real complex nherited upported Dimension of input parameter ingle number vector matrix Numerical real complex Yes Inherited Not applicable Matrix umerical real complex Dimension of input parameter herited ot applicable atrix P Z umerical real only nherited upported ingle number vector matrix a umerical real complex Direct feedthrough Sample time nherited Expansion of input parameter upported Dimension of input parameter ingle number vector matrix a Real Imaginary to Complex Expansion of input parameter Supported Dimension of input parameter Single number vector matrix Dimension of input parameter Single number vector matrix Resolve Complex Numerical real complex Yes Inherited Not applicable Dimension of input parameter Single number vector matrix ounding Function Numerical real complex Y erited Not applicable Dimension of input parameter ingle number vector matrix umerical real only nherited ot applicable Dimension of input parameter ingle number vector matrix 43 Z
2. Numerical real complex Yes Inherited Supported Dimension of input parameter Single number vector matrix umerical real complex es nherited ot applicable Dimension of input parameter ingle number vector matrix rigonometric Function KIZ A 5 Non Linear Node Set Backlash Numerical real only Yes Inherited Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Input single number Tables single number vector matrix Dimension of input parameter Node name Data type supported Direct feedthroug Sample time Expansion of input parameter Dimension of input parameter anual Switch umerical real complex boolean Node name Data ype sup ported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Inherited Not applicable Single number vector matrix Multi port Switch Numerical real complex boolean Yes Inherited Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Quantizer Input nu
3. 13 World Wide Web The Internet is a collection of interconnected computer networks operating under a common communications protocol The World Wide Web WWW currently the fastest growing segment of the Internet is a collection of electronic documents called pages which reside on computers called WWW servers These documents contain a combination of text images audio and video The pages on the Internet are public and anyone can generate a page and put it on a WWW server The pages and the associated files must meet a standard protocol The text portions of the document are written in a mark up language called HTML Hypertext Markup Language 1 Hypertext is text with links such that the user can follow a non linear path through a document or set of documents The unique characteristics of WWW hypertext documents is that the links can point to positions in the same document positions in the other documents on the same server or to WWW pages on any server anywhere on the Internet They can also link to images audio or video The links are indicated by highlighted text and the link is established by pointing and clicking on the highlighted text A person group or organization creates a page because they have certain information which they wish to make available to other people Usually this information is related to and hence references information others have decided to make available Hence the pages point to one another and as the
4. oriented fashion 20 There are some packages like VisSim Simulink Maple available in the market for dynamic system simulation Both VisSim and Simulink support modelling and simulation of complex continuous non linear dynamic systems They combine an intuitive drag and drop block diagram interface with powerful simulation engine that provides fast and accurate solutions for linear non linear continuous time discrete time time varying and hybrid system designs The visual block diagram interface offers a simple method for constructing modifying and maintaining system models 2 3 Web Based Simulation Research on distributed interactive simulation its feasibility and application began in the mid 1970 s In the 1980 s Miller and Thorpe 20 in partnership with the US Army created SIMulation NETworking SIMNET a networked system of computers running a single simulation programme SIMNET was sponsored by ARPA then called DARPA and it was an attempt to make the use of simulators and simulation techniques more feasible for military defense operations This programme demonstrated the feasibility of linking together hundreds or thousands of simulators representing tanks infantry fighting vehicles helicopters fixed wing aircraft etc to create a consistent virtual world in which all participants experience a coherent logical sequence of events In SIMNET standard Ethernet networks were used for all local area network LAN conn
5. 34 35 36 42 45 46 47 Chapter 4 4 4 2 4 3 4 4 4 5 Chapter 5 oli 3 33 5 4 e 5 6 I 5 8 Chapter 6 6 1 6 2 6 3 6 4 0 3 6 6 Chapter 7 Fal Tee References The Design of SimDynamic UlPack NodePack Anim3D Solution of Ordinary Differential Equation How SimDynamic Works Functional Description of Nodes Continuous Node Set Discrete Node Set Tables Node Set Math and Logic Node Set Non linear Node Set Miscellaneous Node Set Sinks Node Set Sources Node Set Application of SimDynamic Solving Ordinary Differential Equation Simple Damped Pendulum Bouncing Ball Bus Suspension Qualitative Decision Making Discussion on SimDynamic Conclusions and Suggested Future Works Conclusions Suggested Future Work yi 49 50 54 63 65 69 12 72 77 8l 85 92 97 104 106 113 113 118 124 128 133 136 141 141 142 143 Appendices Appendix A Node Properties Appendix B Runge Kutta Co efficient Appendix C Bus Suspension System Appendix D SimDynamic Installation Instruction Appendix E SimDynamic Source Code electronic format vii 2 1 22 Zn 2 4 25 2 6 2 2 8 3 1 32 3 3 3 4 3 5 3 6 auf 3 8 3 9 3 10 3 11 S42 313 3 14 3 15 3 16 3 17 3 18 3 T9 4 1 4 2 4 3 List of Figures Legend Page Ammated nand lOr SDIT GIOVE isrener nEri e Esni ia Emea Wve asi 15 VION Glasses tite mem O i ar ER emda iouaraa
6. Dynamic System Simulation on the Web By Khaled Mahbub B Sc Eng This thesis is submitted as the fulfilment of the Requirement for the award of degree of Master of Engineering M Eng Dublin City University September 2002 Research Supervisor Professor M S J Hashmi School of Mechanical amp Manufacturing Engineering REFERENC DECLARATION I hereby certify that this material which I now submit for assessment on the programme of study leading to the award of Master of Engineering is entirely my own work and has not been taken from the work of others save to the extent that such work has been cited and acknowledged within the text of my work Signed khaled mahkuk Candidate ID No 50162551 Date SEPkembey 25 2092 Acknowledgements First I extend my deepest gratitude to my supervisor Professor M S J Hashmi for his constant encouragement and assistance He is a patient and nurturing mentor who could always boost my confidence in times of self doubt or frustration His insistence on perfection was relentless and sometimes painful but undoubtedly this will serve as a guide for the rest of my life Without his guidance and continuous cooperation throughout this research it would not have been possible to finish this dissertation I am honoured to have his name on this work I wish to express my sincere thanks to Keith Hicky school system administrator for his tireless services Without his ass
7. Parameters e Coefficient Matrices A B C and D matrix of the state space Default values are all one e Initial Condition Initial state value that will be used to solve the differential equation Default value is 0 5 1 5 Transfer Function The transfer function node finds the value of a transfer function 52 of following form at the current simulation time G s Lat d s where n s ni ngs aiaa n d s ds dps ices dm The user has to set the coefficients for the numerator and denominator on the parameter dialog box The order of denominator must be greater than or equal to the order of numerator Numerator can be a vector or matrix but denominator must be a vector Width of the output from the node depends on the dimension of the numerator If the numerator is a vector output will be single number If the numerator is a matrix output will be a vector with width equal to the number of rows in the numerator 74 Parameters e Numerator Numerator coefficients of the transfer function Default is 1 e Denominator Denominator coefficients of the transfer function Default values is 1 5 1 6 Transport Delay The transport delay node can be used to delay its input by a specified amount of time The node outputs the initial output parameter until the simulation time exceeds the time delay parameter From this point the node starts generating delayed input The node stores its input and simulation
8. machine specifications and automated conveyance mechanisms may already be located somewhere on the Web These sorts of devices are common in simulation programs built for manufacturing analysis The Web encourages this sort of global view with a reuse of knowledge and information The most profound affect the Web may have on the method of teaching simulation lies with the use of multimedia The Web encourages distance learning more so than the typical simulation textbook On any web page it is possible to include images and video of the instructors along with synchronized slides or overheads This immerse the student in a synthetic learning environment that is more congenial than one they would get simply by reading a book or watching a videotape Publications Additionally he discusses the timesaving aspects of using WWW tools in reading publishing research articles The WWW is valuable in all stages of producing an article from accessing documents via electronic database or URLs cited in bibliographies to transmitting electronic copies of the publication to reviewers and for the final publication Simulation models can be embedded in web documents that can contain videos images and audio in addition to the usual text that traditional documents contain Simulation Programmes Typically scripting languages such as Perl Java Script or Java are used to build the model The paper gives an example of a 23 simulation model using Per
9. possible to enhance computing ability of SimDynamic Another area of interest for future works should be the 3D animation provided by SimDynamic which can be considered in its embryonic stage This version provides 139 only four basic 3D shapes box cylinder cone and sphere for all sorts of 3D modelling that are simply not sufficient to model realistic complex models Future version of SimDynamic should include more and more 3D shapes to provide 3D modelling flexibility Also at present 3D animation is not available in the Web version of SimDynamic because of the security restrictions imposed by Java on Applets In this implementation the simulation results are saved in data files and 3D animator reads the results from data files 3D animation can be made available in the Web version also by avoiding this use of files to communicate between the main simulation engine and the 3D animator 140 Chapter Seven 7 Conclusions and Suggested Future Work 7 1 Conclusions SimDynamic a simulation package has been developed for modelling simulating and analysing dynamic systems on the Web and it has achieved most of the design goals Simulation results from SimDynamic were compared to the results from Simulink and in all cases results from both software were identical Developed entirely in Java it offers most of the advantages of Web based simulation described in chapter one Major advantages are Wide Availability e allow
10. Nodename Gromd _ _ _ Sample time Dimension of parameter Numerical real only Not applicable Supported Node name Ramp _ ___ o o O Not applicable Random Number Generator Numerical real only Direct feedthrough Not applicable 13 Sample time Disorete Expansion of parameter Supported Dimension of parameter Single number vector matrix Node name Data type supported Direct feedthrough Not applicable Sample time Continuous Expansion of parameter Not applicable Dimension of parameter Vector Node name Data type supported Direct feedthrough Not applicable Sample time Expansion of parameter Dimension of parameter Node name Data type supported Direct feedthrough Sample time Continuous Expansion of parameter Dimension of parameter Node name Step o O Data type supported Direct feedthrough Not applicable Sample time Expansion of parameter Dimension of parameter Single number vector matrix Node name Data type supported Direct feedthrough Sample time Discrete O O Expansion of parameter Supported Dimension of parameter Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of parameter Dimension of parameter 14 REFERENCE Appendix B Runge Kutta Coefficients Dormand Prince Pair
11. The derivative node calculates the derivative of its input signal by using the following formula ui us 1 At where u 1s the input at current simulation time u is the input at the previous time step and 4t is the change in time since the previous simulation time step Initial at the simulation start time output from the node is always 0 Parameters e None 5 1 2 Integrator The integrator node integrates its input signal and outputs the integral SimDynamic provides a good number of fixed step integrators as described in section 4 2 5 Integrator node uses those integrators to find the integral of its input signal User can define the initial condition on the node dialog box internal initial condition or as input to the node external initial condition By selecting one of the external reset choices from the node dialog box user can reset the current state of the node to 12 specified initial condition This implementation provides following three types of reset Rising current state of the node will be set to the initial condition if the signal to the reset port changes from negative to positive Falling current state of the node will be set to the initial condition if the signal to the reset port changes from positive to negative Either current state of the node will be set to the initial condition if the signal to the reset port changes from positive to negative or vice versa Parameters
12. figure D1 Model Sheet window will appear ynamic SIMULATION Sy rat tee Integrator State Space 5 2 5 3 Variable Transport Delay Transport Delay Zero Pole Fig D1 2 Select File gt Open from model sheet window figure D2 file open dialog box will appear Fig D2 3 Select pendulum sdm and click OK or double click on pendulum sdm Pendulum model will be loaded figure D3 Maximize window as needed 4 To change Simulation parameters select Simulation gt Parameter from model sheet window figure D4 Current settings are RK DP integrator step size 0 2 and simulation time from 0 to 10 seconds Enter new parameters and then click OK 5 To change any model parameter torque Mass Length double click on appropriate node Parameter dialog box will appear set new value and click OK 6 To run the simulation select Simulation gt Run Simulation will continue When Ready appears on the status bar simulation is finshed 7 To view result double click on Time vs Input node 8 To start 3D animation select Simualtion gt 3D animation 3D Model editor will open To load the pendulum 3D model click on load in the model editor a dialog box will appear enter pendu s3d and click on load on the dialog box Pendulum 3D model will be loaded figure DS SimDynam 4 Cylinder 0 0 0 5 0 0 0 02 1 0 Sphere 0 0 0 00 0 0 2 Fig D5 9 Before starting 3D
13. this method is responsible for node drag operation The method checkSampleT double time it returns true if the time 1s a valid time point with respect to the sample time of the node 60 The method initDialog String title int w int h initialises the dialog box that will be used as parameter input dialog for the node The method showDialog this method displays the parameter dialog box for this node when user double click on the node The method resolveDimension expands the input or parameter of the node It is mentioned in section 3 3 4 that most of the nodes support auto expansion of node input or parameter This method handles this expansion process It uses the canBeResolved method from the Basic class to check which one parameter or input should be expanded then takes apposite steps for expansion The method isOpOK double simT checks that output at the simulation time simT is valid Sometimes a node may produce invalid infinity or not a number output So at each time step this method checks the validity of the output produced by the node The method upDateOutput double simT updates the output of the node at simulation time simT The method initialize initialises the parameters of the node before a simulation run starts The method makeDialog prepares the node specific dialog box for parameter input The method initDialog method initialises a general dialog box for parameters Every method implements this method to c
14. Engineering Education Invited paper Proceedings of American Control Conference ACC 2000 Chicago June 2000 C Schmid Remote Experimentation in Control Engineering 45 International Scientific Colloquium Ilmenau Technical University October 2000 Guru A Savory P Williams R A Web Based Interface for Storing and Executing Simulation Models Winter Simulation Conference Orlando Florida pp 1810 1814 2000 Marr C Storey C Biles W E Kleijnen J P C A Java Based Simulation Manager for Web Based Simulation Winter Simulation Conference Orlando Florida pp 1825 1822 2000 Dhananjai Madhava Rao Victoria Chernyakhovsky and Philip A Wilsey WESE A Web based Environment for Systems Engineering Proceedings of the 2000 International Conference on Web based Modelling and Simulation WEBSIM 2000 San Diego California October 2000 Patrick Naughton and Herbert Schildt 2000 Java 2 The Complete Reference McGraw Hill Companies Inc USA John Zukowski 1997 Java AWT Reference O Reilly amp Associates Inc USA Henry Sowizral Kevin Rushforth and Michael Deering 1998 The Java 3D API Specification Addison Wesley Publishing Company USA 145 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Shepley L Ross 1989 Introduction to Ordinary Differential Equations JohnWiley amp Sons USA http www ae
15. North Holland Amsterdam 1985 S 159 180 Fishwick P A SIMPACK Getting Started with Simulation Programming in C and C In 1992 Winter Simulation Conference December Arlington VA 154 162 Martin Otter The ANDECS Simulation Environment DSSIM DLR FF DR ER Technical report TR R101 93 March 1993 DLR Institute of Robotics and Mechatronics http www robotic dlr de control andecs 143 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Hilding Elmqvist Francois E Celler and Martin Otter Object Oriented Modeling of Hybrid Systems Keynote Address ESS 93 European Simulation Symposium Delft The Netherlands Oct 25 28 1993 pp 1 11 Dynasim AB 1997 2002 http www dynasim se Miller D C Thorpe J A SIMNET The Advent of Simulator Networking Proceedings of the IEEE Vol 83 No 8 pp 1114 1123 1995 IEEE Standard for Inform Tech Protocols for Distributed Simulation Applications Entity Information and Interaction IEEE Standard 1278 1993 New York IEEE Computer Soc 1993 Thomas R Neilson I Hamessing Simulations in the Service of Education The Interact Simulation Environment Computer Educ Vol 25 pp 21 29 1995 Cole R Tooker S Physics To Go Web based Tutorials for CoLoS Physics Simulations Proceeding of the Frontiers on Education FIE 96 26 Annual Conference Fishw
16. Numerical real only Inherited Not supported Input single number vector matrix Dimension of input parameter Co efficient single number vector Sample time merited o o i Input single number vector matrix Dimension of input parameter Table vector Maths and Logic Node Set Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Single number vector matrix Bitwise Logical Operator Numerical real only Inherited Supported Dimension of input parameter ingle number vector matrix l an ombinatorial Logic umerical real only nherited Not supported Input single number vector Dimension of in ter ension of input parame Table matrix a ot Product umerical real complex Sample time Inherited ot supported ingle number vector a A Numerical real complex Direct feedthrough Yes Inherited Expansion of input parameter upported Dimension of input parameter ingle number vector matrix nherited upported Dimension of input parameter ingle number vector matrix ogical Operator umerical real only boolean zZ F agnitude Angle to Com umerical real onl Inherited Supported Dimension of input parameter ingle number vector matrix plex
17. a return on investment ROI improvement General Dynamics Electrical Boat Division for the US navy developed prototypes of a nuclear attack submarine which was designed evaluated and optimized im a virtual environment Electric Boat demonstrated the feasibility of state of the art simulation based design SBD The objective was to implement an accurate efficient and dynamic environment for design rapid prototyping concurrent product and process development mission planning operation maintenance and training By simulating the kinematic dynamic mechanical and other characteristics of the submarine its components and subsystems engineers can create a multi disciplinary environment in which to evaluate a wide range of parameters and optimize the design based on the results Electric Boat selected simulation software ENVISION 5 to visualize design concepts and integrate components and subsystems Virtual engineering allows the parties involved with design manufacturing operations and maintenance to jointly contribute to the design process early in the cycle Electric Boat engineers often experience communication difficulties when trying to describe innovative ideas to people not trained in their particular discipline The physically accurate 3D models simulated in ENVISION help engineers and technicians from various disciplines to comprehend new ideas and concepts In addition virtual reality technologies such as immersion enable engine
18. a unique combination of java applets and query driven databases JSIM provides the user with a simulation and animation environment in which she he can build simulation execute the models watch the animation of a static design diagram embellish the animation with special icons and generate statistics A model is constructed by building a graph with nodes and edges Using the built in features of JSIM the design diagram may be animated when the simulation is run For the purpose of storing and retrieving simulation models and results JSIM incorporates database connectivity with simulation classes When a user queries a simulation system the system first tries to locate the required information in the database since it might have stored as the result of an earlier model execution If the required data is present it is simply retrieved and presented to the user If it is not present the system instantiates the relevant model executes it and shows the result of the execution to the user 24 SimJava presented by McNab and Howell 27 is a process based discrete event simulation package for building working models of complex systems with animation facilities It is actually a collection of three packages simjava simanim and simdiag simjava is a package for building stand alone text only java simulations which produce a trace file as the output by default simanim is tightly integrated with the text only simulation package and pro
19. address http student dcu 1e mahbubk2 simdynamic html in the address bar of the browser Browser will load the SimDynamic homepage Click the start button in the middle of the page SimDynamic will start now use the help file if required D2 Non Web based Version Although SimDynamic can be installed on several operating systems like Windows Unix or Solaries this section provides instructions only for Windows Requirements Pentium III 650 MHz or higher 128 MB RAM or higher Java 2 Runtime Environment Installed Can be downloaded from http java sun com products jdk 1 2 java2 html Java 3D API installed Can be downloaded from http java sun com products java media 3D Installation SimDynamic installer comes with a single self extracting exe file sd_installer exe Run this file double click in windows explorer This will install SimDynamic in C SimDynamic folder You can change the drive such as D or E etc but do not change the default folder name and do not put the default folder inside other folder like programme files After installation SimDynamic icon will appear on the desktop Running SimDynamic Double click on the icon on the desktop It will open command prompt window a small black window do not close it and then start SimDynamic Now use the online help file to proceed D3 Handling Examples Only for non Web based Version Pendulum Model 1 Select File gt New from main window
20. animation save the simulation result in data files To do so double click on xcor node a dialog box will appear in the file name field type xcor and click on save Similarly double click on ycor and enter ycor and click on save 10 Now click on Run button at the bottom of 3D editor 3D animation will start 11 Whenever you change any parameter like torque Mass Length in the simulation model you have to save the data files xcor and ycor as in step 9 12 If you change the length of the pendulum then the cylinder length of the 3D model should also be changed before 3D animation To do so select the cylinder and click on Edit button Cylinder parameter dialog box will appear figure D6 On the parameter dialog box set the new length in the height field Set the Center Y field to minus half of the length i e if the length is 1 0 then set Center Y to 0 5 DO not change any other value on the Cylinder parameter dialog box Click OK 13 Now Click on Run button on the 3D model Editor 3D animation will start with new length 14 To close pendulum model close the model sheet window by clicking the cross at the top right of the window or by selecting File gt Exit Bouncing Ball Model 2 2 Follow steps 1 3 of the previous example to open the file bounce sdm To change simulation parameter follow step 4 This model doesn t need an integrator Default simulation time 0 to 20 seconds and step length is 0 005 Keep i
21. communication volume The proposed new architecture overcomes these problems of existing systems by taking advantages of the features of the object oriented programming paradigm As an object oriented simulation system the basic system elements in the proposed architecture are objects located at different machines These machines are called nodes and are connected by the network At any single node there are three major objects a The message handling object controls the messages passed between local objects and the network Unlike current approaches which broadcast all messages on the network it uses both broadcast and point to point approaches to send the messages to keep the number of redundant messages as low as possible 13 b With update messages the situation delay object is used to display the states of all entity objects distributed at the nodes The display updates with simulation time advance c The simulation object at a node contains a series of entity objects which represent physical entities in the real world It is responsible for creating and deleting its derivative entity objects It transfers the messages from its derivative entity objects to its other derivative entity objects or to the other objects located at other nodes via the computer network Another important task of the simulation object is to control the local simulation time advance In short this prototype object oriented architecture is c
22. computational programs as they would with traditional non Internet based programs and there is a growing interest in using the Web as a new platform for computer programs The disciplines concerned with computer simulation are no exception to this phenomenon the concept of web based simulation has been introduced and is currently the subject of much interest to both simulation researchers and simulation practitioners Web based simulation is an attempt to exploit Web technology to support the future of computer simulation Apart from providing other services the World Wide Web is being looked upon as an environment for hosting modelling and simulation applications Existing computer simulation support is either language based or a library approach In either case they suffer from lack of portability to other environments Also markets in educational software are small There are thus little commercial interests in the production of simulation software for the educational market Any simulation software which is produced is aimed at the industrial market and is often too expensive for the educational purchaser Moreover the demand for consulting in modelling and simulation has grown faster than the consulting companies can offer Use of the Internet and its supporting tools such as the virtual environment and the interactive distributed simulation has the potential to overcome these factors limiting the wider use of simulation The web s ability to s
23. di double d2 double d2 double d2 double d2 double d2 static boolean static boolean grTegt double dl lsTegt double dl lessTn double d1 greatTn double dl static boolean static boolean static boolean static boolean checkRegion int x int y int ex int y Int r i static void VecToArray double R double C Vector M static void VecToArray double M Vector V static void VecToArray double R Vector V static byte setOpDataType Vector opMat static void dblVect2cmp1x Vector cmp1xV Vector dblV static void assignVect Vector Vl Vector V2 static void assignMat Vector Matl Vector Mat2 static boolean isNegative Vector Mat Static boolean hasGreater Vector Matl Vector Mat2 static Vector matMultByVec Vector Mat1 Vector Mat2 static void getDim Vector Mat MyDim d static boolean between_include lower double n double h double 1 static double boolean between include higher double n hy double 1 static boolean between_include both double n double h double 1l static Double elementAt Vector Mat inte LOW inte col static Boolean elementAt Vector Mat int row inte scol byte pb static boolean elementAt Vector Mat int row int col Gomplexscmpie static void initMat Vector Mat double dbl static void initMat Vector Mat double rel double img static boolean canBeResolved MyDim diml MyDim dim2 boolean expand M
24. e External Reset By selecting one of the reset mode described above states of the node can be reset to the initial condition Default is none e Initial Condition Initial value that will be used to solve the IVP Default value is 0 If user selects external initial condition then the node will accept initial condition as an input 5 1 3 Memory The memory node stores the input at current simulation time step and outputs it to the next simulation time step So this node applies a one simulation time step delay to its input signal At the start of the simulation memory node outputs the initial condition parameter value given on the node dialog box Parameters e Initial Value This will be the output at the initial simulation time step Default is Q 5 1 4 State Space This node implements a linear state space 52 system defined by the following equations Ax t Bu t dt y Cx t Du t 73 where x is the state vector of the node u is the system input and y is the system output The matrix coefficients must have the following dimensions e A must be ann xn matrix where n is the number of states e B must be ann x m matrix where m is the number of inputs e C must be anr x n matrix where r is the number of outputs e D must be anrx m matrix User can define the initial condition on the node parameter dialog box and initial condition must maintain the dimension provided in the matrix coefficients
25. multiplication etc These nodes serve as the basic building blocks of a model These are classified into following eight node sets e Continuous e Nonlinear e Discrete e Miscellaneous e Tables e Sinks e Math amp Logic e Source The package has been designed with an aim to achieve as much modelling ease as possible and to provide maximum user flexibility in model execution For modelling the package provides a graphical user interface GUI for building models as block diagrams using simple mouse operations A block diagram is a pictorial model of a dynamic system It consists of a set of nodes interconnected by lines Each node produces an output either continuously a continuous node or at specific points in time a discrete node The lines represent connections of node inputs to node outputs A block diagram can contain any number of instances of any type of node needed to model a system This interface will enable the user to draw a block diagram just as to draw with pencil and paper 34 Section 3 2 contains a brief description of the GUI section 3 3 focuses on the manipulation of nodes provided by SimDynamic section 3 4 deals with lines section 3 5 introduces the simulation parameters section 3 6 gives a bnef description of how to build a model using the package and section 3 7 presents how to make 3D animation 3 2 Graphical User Interface GUI When SimDynamic is started it opens the node set window figure 3 1 T
26. node either directly or by a feedback path through other nodes with direct feedthrough 3 3 6 Sample Time The Sample time parameter sets the time interval at which node s states output are updated There are some nodes that have continuous all the nodes in continuous node set and some other sample time some nodes have discrete sample time all the nodes in discrete node set and some other There are some nodes which derive its sample time from its driving node the node from which it receives its input these nodes are said to have inherited sample time When a node has inherited sample time and it receives its input from more than one node then it sets its sample time to the sample time of the driving node that has the fastest sample time When setting sample time of 42 a node it should be ensured that the sample time of each node in a model should be a multiple of step size 3 4 Lines 3 4 1 Draw Lines Place the mouse cursor near the input output port of a node It is not necessary to position the cursor precisely on the port Cursor will be turned into a cross hair cursor figure 3 10 Integrator integrator Fig 3 10 Line drawing step Dlx Integrator Memory Fig 3 12 Line drawing step 3 Fig 3 13 Line drawing step 4 43 Press the left mouse button and drag the mouse to draw line figure 3 11 To finish the line extend it near the output input por
27. node assumes this behaviour by introducing a parameter dead band which is equal to the width of the space between the gear teeth figure 5 2 The dead band is centred about the output The output from the node will be one of the following e Ifthe input is increasing has a positive slope output input dead band 2 e Ifthe input is decreasing has a negative slope output input dead band 2 e If input slope is unchanged output remains constant 972 Parameters e Dead Band Width Width of the dead band Default is 1 e Initial Output Value of initial output Default is 0 5 5 2 Coulomb and Viscous Friction This node can be used to model static coulomb and dynamic viscous friction 55 If u is the input the node calculates its output using the following formula y sign u magnitude of viscous friction u magnitude of coulomb friction where sign u is defined as follows 0 u 0 l u lt 0 sign u User has to provide the values for coulomb and viscous friction on the parameter dialog box Parameters e Coulomb Friction Value Magnitude of coulomb friction Default is 2 1 4 e Coefficients of Viscous Friction Magnitude of viscous friction Default is 1 5 5 3 Dead Zone The dead zone node generates zero output within a specified region The lower limit start of dead zone and the upper limit end of dead zone of the dead zone should be specified on the dialog box Depe
28. of dynamic systems and for the design of control systems The multi purpose real time suite has highly modular structure and provides a high degree of portability which results from an interface of CADACS to the operating system and to hardware dependent functions Some basic routines build an interface to the world outside of the programs and modules The user can both monitor and store the real time behavior of any external or internal signal At the same time or at a later date s he can evaluate and present results Fishwick 15 develops SimPack a collection of C and C libraries and executable programs for computer simulation The purpose of the SimPack toolkit is to provide the user with a set of utilities that illustrate the basics of building a working simulation from model description Special purpose simulation programming languages can be easily constructed using language translation software with the SimPack utilities which act as the assembly language Most of the existing simulation packages cover one of two areas discrete event or continuous Some available software can perform both types of simulation however bulk support is usually available in only one form SimPack has the ability to overcome this problem It is designed e To support the variety of available model types e To create template algorithms for many cases e To avoid learning a special language syntax e To illustrate the relationship between even
29. order Then comes the direct feedthrough nodes in the order required to supply valid input to the nodes they drive and the list ends with the sink nodes Sets sample time for each node Nodes in the continuous node set and some other nodes have continuous sample time Some nodes nodes in discrete node set have sample times that are explicitly specified by the user on the node dialog boxes All other nodes have implicitly defined sample times that are based on the sample times of their inputs For nodes whose inputs have different sample times the node is assigned the sample time of the fastest input Simulator also checks that sample time of each node is an integer multiple of the simulation step size Resolve dimension for node inputs and parameters It is mentioned in section 3 3 4 that most of the nodes support auto expansion of inputs and parameters At this stage simulator checks each node in the model for the possibility of input parameter expansion If it fails to resolve input parameter dimension for any node error message is generated 4 5 2 Execution Phase In this phase Simulator successively computes the states and outputs of the system at intervals from the simulation start time to the finish time using information provided by the model Simulation starts at the starting time specified by the user on the simulation parameter dialog box and Simulator advances its clock by adding step length to the current clock time until i
30. prototype of a software architecture that would support PDES The main objective of the proposed system is to plan parallel simulation experiments and dynamically manages their execution Via the Internet the system has access to a large number of sites a PC or Workstation from where it can dynamically selects one to execute part parallel replication of an experiment The system uses OCBA 31 to implement the experiment planner The system has also data collection and analysis capabilities to collect and analyse output data from each individual processor Finally at the system contains a reporting mechanism which compiles and presents the result in a multimedia format consisting of tabular and graphical summaries Veith et al 32 presents Netsim a discrete event simulation package based on the event graph approach Netsim provides a maximum amount of user interaction with the simulation model A programming interface provides a blank template with text fields for the various parameters of a simulation model such as event name and state variables A second interface allows user interaction with running the simulation model This interface not only provides start pause and stop capabilities and data output but also an animation of the model The object oriented structure offered by Java and maintained in Netsim allows easy expansion of the package as well as compatibility with other Java based tools Elmaghraby et al 33
31. real only Yes Inherited Not applicable Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Node name Data Type Conversion Data type supported Numerical real only boolean Direct feedthrough nherited ot applicable ingle number vector matrix Expansion of input parameter Dimension of input parameter Node name Data ype sup ported Direct feedthrough S Sample time Inherited Expansion of input parameter Supported Dimension of input parameter ingle number vector matrix ero Crossing umerical real only e lt M Initial Condition Numerical real only Yes Inherited Supported Single number vector matrix Node name Data ype supp orted Direct feedthroug Sample time Expansion of input parameter Dimension of input parameter Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Node name Merge Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Single number vector Node name Ve eee o Data type supported Direct feedthrough Sample time Expansion of input parameter Not applicable Dimension of input parameter ingle number vector Node n
32. respectively 3 4 5 21 7 64 12 9 4i 2 56 13 11 32 2 3 2 3 then output from the node will be 9 4i ia o Parameters e Input Type Specifies whether the input is a Vector or a Matrix Default is Vector 5 6 12 Signal Specification This node can be used to halt the simulation if a signal does not have desired attributes On the parameter dialog box user can choose an expected attribute dimension data type etc If the input signal does not have this attribute then the node will generate an error message and simulation will be halted otherwise the node outputs its input signal unchanged 103 Parameters e Dimension Expected dimension of the input signal Default is 1 A 1 value for dimension indicates that the node will not check for dimension e Data Type Expected data type of the input signal Default 1s booelan 5 7 Sinks Node Set 5 7 1 Display The display node accepts all types numerical boolean data as input and shows the value of its input in a separate window Parameters e None 5 7 2 Terminator The terminator node can be used to manage floating output lines from a node If any model contains some floating output line then SimDynamic halts the simulation To avoid such situation terminator node has been provided Parameters e None 5 7 3 Time Vs Input This node enables user to view the change in a signal with respect to the simulation time The node accep
33. routine based on the following familiar equations ko f y t h k f y 0 5k t 0 5h h k f y 0 5k t 0 5h h k f y k t h h and yay k k 6 k k 3 Besides using simulation it is possible to study the system response using frequency domain techniques such as Fourier transform and the Laplace transform Fundamental to frequency techniques is the notion of the transfer function For multi variable time invariant linear systems the transfer functions are obtained by taking the Laplace transform of 16 OX _ acs Bu dt y Cx which yields y C sI A Bu H su An APL function is also provided to generate H s All the algorithms presented in the paper can analyze with reasonable response systems with up to 40 state variables while running on a mainframe and systems with up to 20 state variables while running on a microcomputer This capacity is sufficient for many real world control problems and is more than enough for exploring the features of control engineering Schmid 13 presents CADACS PC Real Time Toolbox an open software implementation of the KEDDC 14 real time suite which runs on personal computer and provides special real time environments for developing microprocessor based industrial control devices Such control devices can be connected directly to the plant to be controlled and support on line identification of the plant dynamics as well as evaluation of prototype c
34. simulation on the Web This implementation of SimDynamic contains a small number of limitations and these are programming issues that can be remedied with more complex programming techniques than undertaken for the creation of SimDynamic The Current version provides 83 functional nodes to facilitate modelling but it is extendable because of the object oriented paradigm utilized to develop SimDynamic Future additions should include nodes for handling more advanced and various type simulation models particularly discrete event simulation models Although SimDynamic provides a good number of integrators to solve ordinary differential equations they are all fixed step integrators and integration of stiff problems has not been considered In realistic applications the solution is often changing rapidly at certain critical times and it is difficult to approximate accurately At this particular time short integration steps are needed to maintain accuracy At other times for the same problem computing time can be saved by using large integration steps and still get sufficient accuracy It is usually impossible to predict the precise time the small steps are needed So the software should have the mechanism to handle this step size automatically And this is not very painstaking to achieve All the algorithms used to implement SimDynamic fixed step integrators have their adaptive variable step counterparts By incorporating variable step integrators it is
35. the bottom of the node To rotate the node choose rotate from the menu the node will be rotated by 180 degree 3 3 3 Signals and Data Types Signals are the streams of values that appear at the outputs of a node when a model is simulated It is useful to think of signals as travelling along the lines that connect the nodes in a block diagram But it should be kept in mind that the lines in a model represent logical not physical connections among nodes The term data type refers to the way in which a computer represents numbers in memory Different machines with different architectures allocate different amount of storage and uses special internal codes to keep track of the different types of data it processes On the other hand a data type in a programming language is a set of data with values having predefined characteristics Examples of data types are integer floating point character string and pointer It is a collection of values and operations Operations do things to values and produce values as a result SimDynamic supports two types of signal 1 Numerical and ii Boolean The primitive data type used by SimDynamic for numerical values is Java double which is a 32 bit signed number So it ranges from 1 7e to 1 7e SimDynamic supports both real and complex numbers and for logical operations it internally converts the double value to integer Boolean value is used for some logical operations There are some
36. the first and second equation by m and m respectively b Oy ae w x2 mah sat i m To be a valid state space representation the derivative of all states must be in terms of inputs and the states themselves For this system first three states will be x dx dt and y X X2 Substituting y in the above equations 2 Oa 2A BB Pp 2 IS ne w 2 ae dt m dt m m dt d m 2 2 2 Subtracting the second equation from the first equation to get an expression for d y dt b MORRER k l p a 22 ye is a S ue m dt at E 2 m Mm Integrate this equation to avoid the second order derivative dy l aww or e 2 dt m m m m m m m k LREN ernie fek y w x uddt This equation contains no derivative of the input and dy dt is expressed in terms of states and inputs only except for the integral Let the integral is the fourth state of the system and C het iw X ES m m Then state equation for y2 is d 2 b 9 w x y u dt m m m And final state equations for y and dx dt are dy b CE E x y Vy at m m m d x _ bb epte bb ky bw bb HAA 4 Ay Ly Ly 4 hw dt mm m m m m m m m mm The matrix form of the above equations is x 0 l 0 0 b b b 0 b IA Pip rhs See b S A d x m m Wom A M mM m dt b b _ b b dx m al U l dt dy m m mM m 0 dt ky 0 c
37. two one dimensional tables one for input values and one for output values It tries to map its input to the elements of the input table and outputs the corresponding element from the output table If the node fails to find a direct mapping then it performs linear interpolation extrapolation to calculate the desired output Both tables must be of same size 81 For example let following values are given as the input and output table on the node parameter dialog box as 450 var 50 a Suara se 320 931 a0 If the node receives 7 22 as input then output will be 3 20 If it receives 3 0 as input then output will be 6 755205 for input value 19 output will be 10 506406 for input 5 0 output will be 5 070274 Parameters e Input Values Holds the possible node input values Default input values are C ERENER e Output Values Holds the possible output values from the node corresponding to the values in the input values Default values are CEE 5 3 3 Look Up Table 2D The look up table 2D holds the possible output values as a 2D table It also maintains a row table and a column table that corresponds to the rows and columns of the output table The node accepts two inputs a row index and a column index It then tries to map the row index value to the elements of the row table and column index value to the elements of the column table respectively and outputs the corresponding element from the output table If the n
38. user of the following logical operations between the input signal and the second operand provided in the dialog box e AND e NOT e OR e SHIFT RIGHT e XOR e SHIFT LEFT User can use hexadecimal or decimal value for the second operand Since SimDynamic considers all real value signals as double user has to specify in the parameter dialog box how to treat the input signal as an integer Parameters 85 e Input Data Type User can use this option to specify input data type Input can be a 8 bit integer 16 bit integer 32 bit integer or 64 bit integer Default is 64 bit integer e Second Operand This value will be used as the second operand for the bit wise operation with the node input User has the option to set this value as a decimal number or hexadecimal number Default value is decimal 10 e Operator Specifies one of the 6 operator applied to the input signal Default is AND 5 4 3 Dot Product The dot product node generates the dot product of its two input vectors using the following formula y 2 conjugate u v where u and v are the input vectors To have element wise multiplication between two vectors without summing user has to use product node For example let the node receives u 2 31 5 61 and v 1 21 3 1 as input then output from the node will be 5 161 Parameters e None 5 4 4 Gain The gain node multiplies its input signal by a specified gain factor provided in the parameter dialog b
39. value of transfer function expressed in zero pole gain form see below at the current simulation time Z s EES where G is the gain value and Z S S Z1 8 22 0 S Zn P s S pi S P2 S Di The user should set the poles p p2 and zeros z z2 on the parameter dialog box The number of poles must be greater than or equal to the number of zeros Zeros may be vector or matrix but poles must be a vector Width of the output from the node is determined by the dimension of the zeros If zeros is a vector then output will be a single number If zeros is a matrix then output will be a vector with width equal to the number of columns of the matrix The zero pole node converts the given zeros and poles into polynomial form and then carries out the calculation like the transfer function node Parameters e Zeros Values for the zeros Default is 1 e Poles Values for the poles Default is 0 1 e Gain Zeros will be multiplied by gain values Default 1s 1 76 5 2 Discrete Node Set 5 2 1 Discrete Integrator This node computes discrete time integration 53 of its input signal For an initial value problem of the following form this node provides following three methods of integration d f y t y 0 b at Forward Euler Method Yn 1 Yn h fn ty Backward Euler Method Yn 17 Yn as h E th D Trapezoidal Method Yn 1 Yn h 2 fin ty This node allows the user to set
40. visualized with any available graphics module All input data of an experiment e g integration method or length of communication interval are stored on database as well Therefore every simulation run is completely documented and reproducible Elmqvist et al 18 present a new methodology for object oriented modeling of hybrid systems Hybrid models contain both continuous and discrete parts In simulation programs the continuous parts are described by sets of differential equations and algebraic equations in either explicit form or implicit form The discrete parts are expressed with event descriptions Object oriented programming has evolved to support the reuse of software components This programming paradigm was first developed in the context of discrete event simulation and carried over to continuous systems modeling Dymola 19 an object oriented modeling language for continuous systems was designed for this purpose It represented an important step forward towards the reuse of continuous systems models in a truly environment independent fashion A continuous system modeling methodology that doesn t allow for descriptions of discontinuities is not generally useful since all but the most trivial engineering models of dynamic systems contain some sort of discontinuities The paper discusses an extension of Dymola language definition to allow descriptions of models of dynamic systems with discontinuous behavior in a truly reusable object
41. 0 Reshape Dimensionality of a signal can be changed using reshape node Selecting the appropriate output dimension on the parameter dialog box user can change the 101 dimension of a matrix The node provides four possible output dimensions For example let following matrix is the input to the node 2 41 4 55 6 3i 12 6 91 Depending on the selected output dimension output from the node will be e 1D array 2 41 6 12 4 55 3i 6 91 e Column vector 2 41 12 4 55 31 6 91 e Row vector 2 4i 6 12 4 55 3i 6 91 e Custom say custom dimension is 2 x 3 then output 2 41 12 3i 6 4 55 6 91 Parameters e Output Dimension Specifies required output dimension Default is 1D Array 5 6 11 Selector This node enables the user to select elements from a vector or from a matrix The node has two inputs first input can be a vector or a matrix from where elements will be selected and second input is the indices of the elements to be selected For example 102 e Vector input let input to the node is 3 4 5 21 7 64 12 9 and index input is 2 3 5 then output will be 5 21 7 64 9 e Matrix input for matrix input index input must be a matrix with 2 rows Elements of the first row of index input will select a row vector from the matrix and elements of the second row of index input will select elements from the selected vector For example let following two matrices are input and index input
42. 1 a Fig 6 11 b SimDynamic 3D Anim Ol x SimDynamic 3D Anin Fig 6 11 c Fig 6 11 d Fig 6 11 3D animation for pendulum model 123 6 3 Bouncing Ball A rubber ball is thrown from a height of yo metre with the velocity vo meter second figure 6 12 Elasticity of the ball is e 0 lt e lt 1 Yo Vo Fig 6 12 A ball is thrown downward with velocity vo Considering the downward direction as the positive direction velocity v and height from the floor y of the ball at time can be found from the equations v v gl 6 5 Ga gt 6 6 where g is the acceleration due to gravity At every y position if the ball is above the floor i e y is greater than or equal to zero then it will continue to fall If the calculated y value is negative indicating the ball hits the floor then velocity is recalculated using the following formula Vp E Vp 6 7 where v is the rebound velocity v is the velocity with which the ball hits the floor and minus sign is used since the ball will change direction 6 3 1 Building the Bouncing Ball Model From the above equations and assumptions the following figure 6 13 model was constructed using SimDynamic This model will calculate v and y at every simulation time step Inputs to the Swm node are vp and gt so the output from this node is the 124 velocity v v vp gf at time step t Inputs to the Sum2 nodes are vt and 1 2gt so the output from this node
43. Sheet whenever user clicks the new menu item 4 1 6 ParamFrame Class ParamFrame enables the user to set simulation parameters Its signature is as follows public class ParamFrame extends JFrame implements ActionListener public ParamFrame Simulator smltr public void initParams public void actionPerformed ActionEvent ae Constructor ParamFrame instantiate all the components needed for the input such as TextField ComboBox etc The initParams method assign the current values of different simulation parameters to the respective input fields before displaying the frame The actionPerform method is invoked when user clicks any of the buttons ok cancel in the frame This method parses the input entered by user and checks legitimacy of input Invalid input will be reported by appropriate error message 4 1 7 Other Classes in the Package UIPack also contains following classes All of these classes except CNST class extends JFrame CNST this class holds some constant values that are used in various places of the software All members of this class have been declared static So they are directly accessible by using the class name only 53 ColorFrame ColorFrame provides a colour chooser using that user can change the colour of a line ErrorFrame SimDynamic uses ErrorFrame to display the appropriate error message whenever it encounters some error during the runtime of a simulation InfoFrame this clas
44. Vector mat public static Complex DMinor Vector a boolean crossed int rowv r intocoli public static Vector hermitian Vector mat public static Vector transpose Vector mat public static Vector conjugate Vector mat public static Vector mult Vector Matl Vector Mat2 publae static Vector add sub divivector Matl Veetor Mat2 Ineetunct ron public static Complex trace Vector mat Once again the methods in this class have self explanatory names So functional description has been skipped 4 2 4 Nodelcon The Nodelcon class has been defined as an abstract base class that encapsulates the features common to the classes that appear as nodes in a model All the functional nodes total 83 nodes in this implementation have been derived from this class This class looks like as follows public abstract class NodeIcon extends JComponent implements ActionListener public NodeIcon Simulator smltr public void setIpOpxX public void adjustName public void alert public veld adjust r ipri public void actionPerformed ActionEvent ae public void reloadImage public void paintComponent Graphics g public void processMouseEvent MouseEvent me public void processMouseMotionEvent MouseEvent me 59 public boolean checkSampleT double time p blic void initDialeg String title it w int h public void showDialog public boolean resolveDimension public boolean isOpOK double simT pub
45. a 0 1 5 1 5 3 1 3 40 4 5 44 45 E 8 9 19372 6561 36 15 l oryaies 25360 2187 64448 6561 212 729 ae 355 33 46732 5247 49 176 5103 18656 0 500 1113 125 192 2187 6784 11 84 M 35 384 0 500 1113 125 192 2187 6784 11 84 0 5179 57600 0 7571 16695 393 640 92097 339200 187 2100 1 40 Fourth Order Runge Kutta w 1 6 1 3 1 3 6 Bogacki Shampine w 29 13 49 0 724 14 1 3 18 Heun s Method Wz WZ Appendix C Bus Suspension System State Space Controller A bus suspension model can be simplified considering just one wheel of the bus to a one dimensional spring damper system A diagram of this system is shown below fig 6 19 Bus suspension system 1 4 bus where m1 is body mass e m2 is suspension mass e kl is spring constant of suspension system e k2 is spring constant of wheel and tire e bl is damping constant of the suspension system e 2 is the damping constant of the wheel and tire e wis the disturbance from the road e uis the control force When the bus is experiencing any road disturbance the distance xl w will be very difficult to measure and the deformation of the tire x2 w is negligible So the distance x1 x2 will be output from the model State Space Equation From the above figure and Newton s law dynamic equations for this model are as follows d x dx dx mi Ht u b ky 2 d x dw ax Ps ap 6 St 2 4 x 12 0 a u Dividing
46. akes 15 VOF Glasses TaN E lt tr an E E ONNE TTET A I5 Remote S amp A and dala transier o i icoanei in a E a TE tina 26 Client Site simulation with loaded applets ccccceeeeeeeceneaeeeneeens 26 Remote simulation and local visualization ccccceeeeeeeeeeeeeneneeeeneeees 27 W COeen alin SOM ARE MOGE eas cin ooe cas E aan eearicrwaseN cha cness 28 Hardware and software layout Aa ieee IAA ORS 31 Nodese tiny indov oa ee E ua eee een 35 Mode IRS Hee tema WW eos eer E a eee nee ans 36 STUDIO EC on ine NE MI ect ee accel eee ie trance eas 37 Logical representation of a NOdE c ccecesseecscsececeseeeescesaseuses 37 Bodenmeer shetlneswcnasan acd ite aessaaimaMeiere een is Mine nicanias 38 Parameter dialog box for Discrete State Space cece cece cece eee eee ne ne ens 39 Ow vecilor aS parameter inchs iewerie ter er aretastumian ni Veil iiwenhahanininenncdinles 40 Column eeiorasipakamMetelsc lt oaa eiaa n E E nee ore 40 M E TBM r a A neler N A T 4l Ene CA PIT MS 1S Pb anana ee tele steric a 43 Ene dronn E A user seater en a ae a Siero ere ee nue 43 Weiner E o gt E E E te nines 43 EE PRS ety sant ioscan a vienna an oma E aaa 43 Line mie e Meese a a a th1a lane meee Rats 44 Branchline drawina step 10 2 eedeece caer nac n T a a 44 Banen linedra nine step a on a seas sine E 44 Simulation parameters dialog Box no m n e wee eee 45 SIO Ode edKOrWINdO Waern a e E a E a a A 47 Dalos tordar MBean aa era as Coe ec
47. ame Data type supported Direct feedthrough Sample time Expansion of input parameter Not applicable 10 A 7 Dimension of input parameter Single number vector matrix Expansion of input parameter Dimension of input parameter Node name sd Selector __ O Dimension of input parameter Single number vector matrix Dimension of input parameter Sink Node Set Nodename pispay Dimension of input Dimension of input Numerical real complex boolean 11 Dimension of input Dimension of input A 8 Source Node Set Node name Data type supported Direct feedthrough Sample time Continuous step size dependent Expansion of parameter Dimension of parameter Node name Data type supported Direct feedthrough Sample time Expansion of parameter Dimension of parameter Node name Data type supported Direct feedthrough ea ae Dimension of parameter 12 Constant Sample time Continuous step size dependent Digital Clock Not applicable N N Direct feedthrough ot applicable Sample time Discrete ___ O O OO O O OOOO O ot applicable Dimension of parameter Not applicable Discrete Pulse Generator Numerical real only Direct feedthrough Not applicable Single number vector matrix
48. ard aoe 48 SimDynamic package NChatChY 02 2ccscessdizedssdanasiale sesshaonvevusantauausievercss 49 Class hierarchy in UIPack package sccscsccccssceceeecsssecesscaeetenenens 50 Class hierarchy in NodePack package ccc cc0cceceecececeeeeseeeeeeeeuas 54 vill 4 4 Dal 52 6 1 6 2 6 3 6 4 6 5 6 6 6 7 6 8 6 9 6 10 6 11 6 12 6 13 6 14 6 15 6 16 6 17 6 18 6 19 6 20 6 21 6 22 6 23 Glass hicrarchy in Anim3D pokape iro Taie a EE E oven 64 Bac kiasi IT AEOR S n a T a T EE T ET 92 Dead Damona A Me ee a 92 SEDEL OT ODESSIE cir aio even ioe nce hte een rie eae A 114 Siep 2 andor ODE wmode lees 2ccs rae in esas vs espn 115 Completed ODE model nT ATTA TA OTTOS 116 Graphical result for ODE mod l trr noi Tnn annaa E EE E EE 117 N mencalresuk for ODE model Ia na Piss IE E E OE a k 117 APS IEE PERDUTI a Ne A a a a A naa vs 118 Penduhim model im SiImMmDYNaMI C aeei rasa E E eee 119 Graphical result for pendulum model rod length 1 meter 008 120 Graphical result for pendulum model rod length 0 5 meter 06 121 Poduke tiie Ol ae tek ee eee eee oot AP 123 3D aninralion for pemauulinmmtodeliy cs 3 cae eel eet ss hee ae 123 A ball is thrown downward with velocity Vo c0ccceeeeeceeeeeeeeceeneeeeeues 124 Bouncing ballimodel an Sim Dynami I ice A e E aes ee 125 Graphical result for bouncing ball model 0 8 cece eee e sense enon
49. ated signal Default is 1 Hertz 5 8 14 Sine Wave It generates a sine wave using the following formula y amplitude sin frequency simulation time phase angle The user has to specify amplitude frequency and phase angle on the parameter dialog box This node can be used to generate a sine wave either in continuous or discrete mode If the sample time on the parameter dialog box is left 0 then a continuous wave will be generated For any positive value for sample time will generate a wave at specified interval Parameters e Amplitude Amplitude of the generated signal Default is 1 e Frequency Frequency of the generated signal Default is 1 rad sec Phase Phase shift of the signal Default is 0 rad e Sample Time The time interval at which output will be updated Default is 0 which means it will generate a continuous sine wave 5 8 15 Step It provides a step 56 between two defined values at a particular time Step time initial output and final output should be specified on the parameter dialog box The 111 node continues to produce the initial output until the simulation time reaches the step time From this point the node continues to produce the final value provided on the parameter dialog box Parameters e Step Time Time at which output will switch to final value Default is 1 sec e Initial Value Output from the node before it switches to the final value Default is 0 e Final Value Outp
50. ation Despite these advantages of WWW based simulation traditional simulation packages may be useful over Web based simulation in many cases Traditional packages are specialized to idiosyncrasies of a single platform making maximum use of its capabilities This may increase efficiency of simulation runs Loading times for traditional programmes are dependent on the computer and not on the current volume of Internet usage During heavy Internet traffic models with complex or extensive amounts of code may initially take large amount of time to download 1 6 Objective of the Project The objective of the project is to develop a virtual reality based simulation software SimDynamic Proposed simulation engine will be powerful enough to handle both discrete event simulation and dynamic system simulation In case of dynamic systems it will support linear and non linear systems modelled in continuous time sampled time or a hybrid of the two Systems can also be multi rate i e have different parts that are sampled or updated at different rates Java will be used to develop the package so it will be accessible over the Internet Using any java enabled browser a simulation problem can be modelled in SimDynamic and such java based models allow the user the same degree of interactive multimedia capability as do non Internet based programs SimDynamic can also be used as a non Web based application on a PC For modeling the packa
51. ault is 0 5 5 5 7 Rate Limiter Rate limiter restricts the rate of change of a signal within a specified range The node calculates the rate of change of its input signal using the following formula ui U 1 At rate where u is the input at current simulation time u is the input at the previous time step and 4f is the change in time since the previous simulation time step A rising slew rate and a falling slew rate should be specified on the parameter dialog box If r is the rising rate and f is the falling rate then e If rate is greater than r then output y t At r y t 1 e Ifrate is less than f then output y t At f y t 1 e Iff lt rate lt r then output y t u t Parameters e Rising Slew Rate Upper limit for the rate Default is 1 e Falling Slew Rate Lower limit for the rate Default is 1 5 5 8 Relay Depending on the input signal relay node switches its output between two constant values A switch on value a switch off value switch on output and switch off output should be specified on the parameter dialog box Let son is the switch on value sof is the switch off value opn is the switch on output value and opf is the switch off output value If u is the input to the node e Ifu gt son then output y opn 95 e Ifu lt sof then output y opf e Ifsof lt u lt son then output y y 1 Parameters e Switch on Point Threshold for switch on Defaul
52. cessary to build a SimDynamic model Similar steps were followed in the subsequent examples but for brevity the model building steps are omitted from the latter examples It 1s recommended that reader must go through chapters 3 and 5 before reading this chapter 6 1 Solving Ordinary Differential Equation Consider the following second order differential equation where coefficients are chosen arbitrarily d y t dt 052 11y 9 sin 3 77 6 1 This can be written as two first order differential equations if two new variables x t and x2 t are introduced Let x t y t then the above equation reduces to the following two first order equations dxi _ n 2 0 6 2 poe L _ 0 5x2 t 1 1xl 0 9 sin 3 778 6 3 So the solution of this equation can be found by using two integrator nodes to integrate the first order derivatives 113 6 1 1 Building the Model Step 1 Add two ntegrator nodes to the empty model sheet from the Continuous node set Rename using the node menu the nodes as x1 and x2 respectively Connect the output port of x2 to the input port of x1 figure 6 1 The input to the left most Integrator is the derivative of x2 and its output is the variable x2 The input to the rightmost ntegrator is the derivative of x1 i e x2 and its output will be the variable xl i e y which is the final output SimDyna nnk Fig 6 1 Step 1 for ODE model Step 2 Add two Gain
53. completed line Method adjustA and adjustR are used to adjust a line connected to a node when the user moves the node 4 1 2 HelpFrame HelpFrame provides the framework for displaying SimDynamic online help HelpFrame extends DefaultHelpBroker public HelpFrame HelpSet hs HelpFrame simply extends java s DefaultHelpBroker where the HelpBroker is the default presentation of a HelpSet And a HelpSet is a collection of help information consisting of a HelpSet file table of contents TOC index topic files and Map file The constructor also customizes the default presentation by enabling and disabling some buttons 4 1 3 DrawArea Class DrawArea provides the free area on the model sheet window where user can draw a model Its signature is as follows class DrawArea extends JPanel implements ActionListener public DrawArea Simulator smltr public void painComponent Graphics g public void actionPerformed ActionEvent ae public NodeIcon createNode 51 Constructor DrawArea initialises the drawing area and it also implements the ActionListener interface to perform the menu actions for a line This class also includes two inner classes to capture all the user mouse operations The paintComponent method repaints the draw area when ever it is needed The actionPerformed method handles all menu actions for the line menu The createNode method creates a new node when ever user clicks on the draw area after sel
54. converts a 1D vector of Double objects to a Id array of double The setOpDataType Vector opMat checks each element of the vector opMat If it has a Complex number then it returns data type complex otherwise it returns data type real The dblVect2cmplx Vector cmplxV Vector dblV converts a vector of Double objects into a vector of Complex objects where each complex number s imaginary part is zero The assignVect Vector V1 Vector V2 assigns the elements of vector V2 to vector V1 56 The assignMat Vector Matl Vector Mat2 assigns elements of vector 2D Mat2 to vector Matl The isNegative Vector Mat returns true if any element of the vector Mat is negative The hasGreater Vector Matl Vector Mat2 returns true if any element of vector Mat is greater than the corresponding element in vector Matz2 The matMultByVec Vector Matl Vector Mat2 multiply a matrix Matl by a row matrix Mat2 and retum the resultant row matrix The getDim Vector Mat MyDim d finds the dimension of a matrix Mat and assign the dimension to d The between_include_lower between_include_higher between_include_both these three methods were used to find whether a number is within a given range The elementAt Vector Mat int row int col returns a Double object from the matrix Mat at r c position The initMat Vector Mat double dbl and initMat Vector Mat double rel double img were used to initialise a matrix with real number and complex number respe
55. ct a simulation of the wheel assembly area of Derby Cycle a producer of recreational and sport bicycles in Kent Washington was constructed Production and manufacturing personnel were used as subjects for evaluating the interface Five subjects from Derby Cycle participated and were shown either the virtual environment or the AutoView animation on a computer screen Subjects were asked questions to determine their understanding of the virtual factory and follow up written survey and group discussion was also conducted One notable conclusion from the survey and group discussion was that the virtual factory captured the imagination of the Derby employees Participants who viewed the virtual factory were much more talkative and involved in the exploration of the simulation than the subjects who experienced AutoView Marcedie et al 8 cite many recent examples of virtual environments that can be used as effective tool for training and education The paper also introduces a novel technology to support distributed virtual environment Existing VR systems lack distributed access they have no central computer for event scheduling or conflict resolution Computers or autonomous simulation nodes on which the system is running are responsible for maintaining the state of one or more simulation entities Simulation nodes communicate primarily changes of their state and they are responsible for determining what is perceived Such a system suffers from high
56. ctively The updateParam String str Vector mV byte type String err_msg parses the string str for numerical parameters and assign the values to the vector mV If this method encounters error during parsing it returns false and assign appropriate error message to the string err_msg The tf2ssAB Vector A Vector B Vector num Vector den int n partially converts a transfer function to a state space This method calculates the matrices A and B of a state space from the numerator and denominator of its corresponding transfer function The tf2ssCD Vector C Vector D Vector num Vector den int n calculates the matrices C and D of a state space from the numerator and denominator of its corresponding transfer function The factToPoly Vector Mat Vector gV boolean g finds the polynomial from its factorial form 4 2 2 CMath This class can be considered as a replica of Java s Math class for complex variable SimDynamic supports both real and complex variables This class provides all the 3 complex mathematics All methods of this class have been defined as static as follows public class CMath extends Object public static Complex add Complex Cl Complex C2 public static Complex sub Complex Cl Complex C2 public public public public Public public public pup lic public Public public public public PUBIC public public public public public public pupie public public public public public public p
57. ctor B Vector Y Vector PRV int Ss p e int row double step Public vord ini ialize int solver int new int col Constructor ntegrator creates a new integrator and allocates memory for co effiecient tables The method integrate Vector A Vector X Vector B Vector Y Vector PRV ints p c int row double step calculates integral value of a differential equation of the form a AX BY dt where A and B may be matrices or one dimensional arrays X and Y must be one dimensional arrays at the current simulation time step using the integral values from the previous time step This method extensively uses various explicit Runge Kutta formulas to solve the equation Runge Kutta formulas used by this method are briefly described in section 4 4 initialize int solver int row int col method initializes the tables for the selected Runge Kutta method 4 2 6 Simulator This is the main simulation engine and it controls the whole simulation process of a model The simulator class makes the nodes to interact as a system and generate desired output The way it accomplishes this is described in section 4 5 62 public class Simulator extends Object public Vector nodeV public Vector lineV public int nodeID public int lineID public Simulator BlankSheet blsh public void inst public boolean simulate public void removeline int id public void removeNode int id Constructor Simulator creates a new simulat
58. dated Default is 1 5 8 7 From Data File The from data file node reads data from a file that has been save by the to data file node This node provides the mechanism of using previously generated simulation result The node compares step size saved in the file with the current simulation step size If they do not match it will generate error message Parameters e File Name Name of the file that contains the data Default is None 5 8 8 Ground SimDynamic halts simulation if any node contains a floating input line Ground node can be used to connect such floating input line This node produces a 0 all the time Parameters e None 5 8 9 Pulse Generator It generates a continuous train of pulses Amplitude period duty cycle and start time of the pulse should be specified on the parameter dialog box Period and start time should be expressed in second and duty cycle should be expressed as a percentage of period Parameters e Amplitude Amplitude of the pulse Default is 1 e Period Period of the pulse expressed in seconds Default is 1 e Duty Cycle Percentage of the pulse period that the signal is high Default is 50 108 e Start Time Delay before each pulse is generated Expressed in seconds Default is 0 5 8 10 Ramp The ramp node can be used to generate a signal that starts at a specified time and changes by a given rate 56 The rate of change slope start time and initial output should b
59. de facilities for developing graphical user interfaces e g the java awt and javax swing awt packages 45 network application support java net package a number of utility functions java util package remote method invocation java rmi package and 3D animation facility Java 3D API 46 The facilities provided by the Java programming language to support Web technology is the power behind Web based simulation Java benefits from being simple secure object oriented multi threaded and architecture neutral More and more researchers develop their simulation packages based on Java But most of this research is only devoted to discrete event simulation SimDynamic is an attempt to introduce dynamic system simulation on the Web and provides a positive unique contribution to Web based simulation 33 Chapter Three 3 Overview of SimDynamic 3 1 What is SimDynamic SimDynamic is a java based simulation package for modelling simulating and analysing dynamic systems on the Web A model of moderate complexity can be constructed with minimum of effort and without writing any code It supports linear and non linear systems modelled in continuous time sampled time or a hybrid of the two Finally the package will provide both numerical graphical results and also 3D animation SimDynamic is actually a collection of several functional nodes where each node carries out a specific function such as integration generation of sine wave
60. ded to make model creation an easy task It is also intended that the model user would be able to understand the model as well as use the simulation easily This is achieved through providing a user friendly modelling interface and an easy to view graphical numerical results mechanism It is believed that SimDynamic is an easy to use package and that even a fairly complex simulation model can be constructed without writing any additional code The software consists of three packages each package containing a set of related classes that work together to provide functionality in a key area to support a simulation process The package hierarchy of this software is shown in figure 4 1 Fig 4 1 SimDynamic package hierarchy A discussion of each of these packages is presented in this chapter in greater detail In the class hierarchy diagrams in this chapter foundation classes java built in classes are represented by dashed rectangle and software classes are shown by regular rectangles The signatures for the public methods of major classes for each package will be given For brevity all the classes of each package and all the methods of classes are not discussed here Complete source code of SimDynamic is provided in appendix E 49 4 1 UlIPack This package contains all the necessary classes to provide the graphical user interface GUI of the software The class hierarchy of this package is as follows ARPA shew eeenaQpetnneae
61. demonstrate a software model that can be used for transforming a legacy simulation system into Web accessible application The paper describes the initial results from the conversion effort on an existing distributed simulation visualization system There are two potential methodologies to run application from the World Wide Web Environment Setup Listening Daemons and Application Launcher Legacy Application Layer Operating System Layer Fig 2 7 Web enabling software model a to develop the application to be web aware using Web development techniques such as HTML Java CGI etc 28 b to transform an already existing application which was not designed for the web into a Web accessible application But there are a number of problems to convert an existing application to be web accessible such as limited environment security risks concurrency etc To answer all this problems a Web enabling software model has been introduced The proposed layered model figure 2 7 has several layers to link an existing application to a web browser The Web browser layer is the interface layer where users can interact with the application from the Web Authentication layer authenticates the user and validates the login name and password The environment set up layer will control concurrency create run profile and talk to the listening daemons layer which is the heart of the model Listening Daemons layer receives user requ
62. depends on the type of node of which the node is an instance To have functional description of each node see chapter 5 This section describes the nodes from GUI perspective 37 3 3 1 Adding a Node To add a node in the model sheet click on the node in the node set window The mouse cursor will be turned into a hand cursor indicating that the node has been selected Now click on the draw area of model sheet window SimDynamic will add the selected node in the model sheet Each node will have one or more input output port 3 3 2 Manipulating Nodes To move a node within the model place the mouse cursor on it and press the left mouse button Mouse cursor will turn into a crossed cursor Now drag the mouse and the node will be moving with the mouse movement If any line is connected to the node it will be adjusted automatically A node can be deleted rotated or renamed by using node menu To activate the node menu position the mouse cursor on the node and click the right mouse button The node menu will appear like figure 3 5 Now choose appropriate menu item to manipulate the node Fig 3 5 Node menu To delete the node select delete from the menu the node will be deleted and the connected lines if any to the node will be adjusted 38 To rename the node choose rename from the menu SimDynamic will open a dilaog box enter the new name of the node and click ok on the dialog box The new name will appear at
63. e eee 126 Graphical result for bouncing ball model e 1 0 eee c eee e eee eee ere ee en ees 126 3D animation for beuncing ball meme 022 25 2 dese ccee eee ss ees ees von 127 Bus suspension sVStemii ly 41 DUS oso eE T a E 128 Bus suspension model open loop in SimDynamic 0 2 scce eee e eee ences 129 Bus suspension model closed loop in SimDynamic cesses ee eee eee 130 Graphical result for bus suspension model open loop ecceeeee eee ees 131 Graphical result for bus suspension model closed loop 0eseeeeeeeees 131 Discrete pulses applied to closed loop bus suspension model 66 132 3D animation for bus suspension model cccecee cee cee eeeeeeeeeeaeentees 133 iX No 6 1 6 2 6 3 Numerical result for the Numerical result for the Results for Q DM model List of Tables Legend Pendulum model rod length meter Pendulum model rod length 0 5 meter Peecatep ere see ereoesaneeeoeeoztearareevnaseenseee Breese eneaees esses nen eaarngenwaeeen Chapter One 1 Introduction 1 1 Virtual Reality Virtual Reality VR is the simulation of a real or imagined environment that can be experienced visually in the three dimensions of width height and depth and that may additionally provide an interactive experience visually in full real time motion with sound and possib
64. e final point tp or at some discrete list of points The general form of an IVP is as follows u T FOAM yO b where b is a constant and the function f x y is given Integrator class uses Euler s method and Runge Kutta methods to solve this sort of IVPs A short description of each methods used in SimDynamic is presented in the following sections 4 4 1 Euler s Method In Euler s 48 method yn is calculated from yn using the formula Yn Yn h f Yn tn where h is the step size This method is called a one step method since the information from the single previous step is used to calculate the current approximation Since the error in Euler s method is of O h one has to choose a very small step size to get desired accuracy So this method is not preferred over the Runge Kutta method as the errors in Runge Kutta methods are much smaller than those of Euler s method 4 4 2 Heun s Method In this 48 method yn 1 is calculated using the following steps k f Ynsln k f y th k t h Y nsi sat h k Ae _ 66 where h is the step size Here k is the slope at the point Yn tn K2 is the slope at the point y h kj tn h The average of the two slopes were used to calculate the next approximation y from Yn The use of mid point slope cancels out the first order error term making the system second order It provides much better accuracy over Euler s method 4 4 3 Run
65. e function list on the Trigonometric Function node parameter dialog box So the input to the Trigonometric Function node is now 3 77t and output from this node will be sin 3 77t Add an Absolute node from the Math and Logic node set and rotate it Connect the output of the Trigonometric Function node to the input of the Absolute node Add another Gain node from the Math and Logic node set rotate it rename it as 9 and set the gain parameter to 9 on the parameter dialog box Connect the output of the Absolute node to the input of the 9 Gain node And connect the output of the 9 Gain node to the first input of the sum2 node The model is complete now To view output add a Time vs Input node from the Sink node set and draw branch line from the 115 output line of x1 Integrator and connect it to the input of the Time vs Input node The completed model will look like as figure 6 3 Fig 6 3 Completed ODE model 6 1 2 Running Simulation and Viewing Result In this example simulation will be carried out for 10 seconds So click the parameter button from the toolbar or select parameter from simulation menu and set simulation start time to 0 and stop time to 10 on the simulation parameter dialog box Choose RK_DP integrator on the parameter dialog box set simulation step size to 0 2 and click ok Now click the run button from the toolbar or choose run from simulation menu When simulation is finished Ready appears on the
66. e model creation user has to choose an integration method and then can use SimDynamic to simulate the behaviour of the system for a specified period of time To start a simulation run choose run from the simulation menu or click on the run button from the toolbar First SimDyanmic will initialise the model and then it will execute the model If the model does contain error simulation will be halted with appropriate error message To view result of a simulation run use the nodes except terminator node from Sink node set To view numerical result use Display node for graphical results Time vs input or X vs Y Graph nodes can be used To save simulation results in a text file use To file node When simulation run is finished simply double click on any of the sink nodes to view results To Dat File node from the sink node set can be used to save simulation results in a data file for later use by SimDynamic 3 7 3D Animation To start the 3D model editor choose 3D animation from the simulation menu SimDynamic will open the following 3D model editor 47 SimDynanit D Model Editor f i Fig 3 18 3D model editor window This editor provides some basic 3D shapes Box cone etc on the left side It is assumed that the 3D model will be rendered in a 3D space At each simulation time step objects of a 3D model will move to a new position in the space User has to add 3D shapes to the space to construct a model To add any
67. e rows one by one separated by commas Following dialog box shows how to enter the following matrix as Gain parameter Fig 3 9 Matrix parameter Most of the nodes support auto expansion of inputs parameters wherever applicable For example 2 3 4 has been entered as gain parameter and the node receives 5 as input Then Gain node will consider the input as 5 5 5 and output will be 10 15 20 For matrix parameter the node will try to expand the input to a matrix of same dimension as the parameter For example above matrix has been entered as gain parameter and the node receives 7 as input Then gain node will expand the input to the following matrix 4 And the corresponding output from the node will be the following matrix l4 21 28 35 42 49 T aun 35 When using matrix input or parameter it should be made sure that all the rows of the matrix must have same number of columns 3 3 5 Direct Feedthrough There are some nodes whose current outputs depend on their current inputs These nodes are defined as direct feedthrough nodes All other nodes are called non direct feedthrough nodes Examples of direct feedthrough nodes are Sum Gain etc Examples of non direct feedthrough nodes are Integrator Memory etc When using direct feedthrough nodes it should be made sure that the model does not contain any algebraic loop An algebraic loop generally occurs when an input port with direct feedthrough is driven by the output of the same
68. e set on the parameter dialog box The node continues to output the initial output given on the dialog box as long as simulation time does not exceed start time then it starts to produce the ramp signal Parameters e Slope The rate of change of the generated signal Default is 1 e Start Time Time at which the node will start generating the signal Default is 0 e Initial Output Output from the node before the ramp signal starts Default is 0 5 8 11 Random Number This node generates normally distributed random variates Mean variance of the distribution and an initial seed should be specified on the parameter dialog box This node uses java s built in random number generator to generate two random numbers R and R2 using the initial seed From those two random numbers random variates are generated through following steps 57 e Generate two independent standard normal variates Z 2InR cos 2nR gt Z 2InR sin 2nR2 e If is the mean and is the variance then normal variate X can be found from the formula Aj pet oZ 109 Parameters e Mean Mean of the normal distribution Default is 0 e Variance Variance of the distribution Default is 1 e Initial Seed Starting seed for the random number generator Default is 0 e Sample Time The time interval at which output will be updated Default is 1 5 8 12 Repeating Sequence The repeating sequence node generates a periodic
69. e simulation monitor that is residing on the same machine as the web server The monitor passes object information to the client The simulation viewer can then render the simulation based on this information 29 Schmid 35 40 presents a remote laboratory approach using virtual reality on the Web where an audio visual interface together with a collaboration tool gives the feeling of being in the laboratory Most existing virtual labs offered across the Web include several fully interactive experiments completely written as Java applets but can t claim to be an equivalent substitute of the real experiment To solve the problems of this close to reality environment and to take the full advantage of a remote virtual laboratory preference is given to an architecture where all computational work is performed locally using some existing computational machines like MATLAB SIMULINK MAPLE and the output is linked to the objects of VR scene One main task to accomplish this is to tie together the computational machines of MATLAB SIMULINK MAPLE with Web components to a hypertext based system which runs on the Web browser The proposed client server architecture uses the following plug ins for this purpose MATLAB plug in integrates MATLAB operation into the Web browser the Graphics plug in extends the MATLAB plug in to view MATLAB or Metafile graphics the DDE server plug in together with the VRML plug in is for viewing and acting with VR scene
70. e that all the lines in the model is connected to nodes If there is any dangling line in the model error message will be generated At this stage source node for a branch line is also determined Sets input output nodes of each node It is mentioned in section 3 3 3 that the lines in a model represent logical not physical connections among nodes And during a simulation run a node receives its input directly from another node So every node needs to know the address id of its predecessor and successor nodes To find the predecessor successor nodes of a node Simulator traces each line connected to the node and retrieves the id of the node connected to the other end of the line It reports error message if fails to resolve any node Determines the execution order of nodes During a simulation SimDynamic updates the states and outputs of a model s nodes once per time step The order in 69 which the nodes are updated is therefore critical to the validity of the results In particular if a node s outputs are a function of its inputs at the current time step the node must be updated after the nodes from which it receives its inputs Otherwise the node s outputs will be invalid The order in which nodes are added in a model is not necessarily the order in which they need to be updated during a simulation Consequently SimDynamic sorts the nodes into the correct order during the initialization phase To facilitate this sorting pr
71. ect was actually two components of a suite of virtual reality software developed at HITL VEOS Virtual Environment Operation System a distributed database and data transport package created for research and rapid prototyping of virtual environments and Image Library created for research into VR related rendering issues The objective of the project was to create virtual factory that would allow a user to experience a three dimensional playback of a simulation The user could move around in the factory but would not be able to make changes to the simulation other than starting and stopping simulation time The user has to don a head mounted display HMD which conveys visual information to each eye via two separate video screens A tracking device allows the VR system to monitor the user s head position and orientation at all times A computer with special graphics hardware continually renders for each eye a perspective image of the virtual environment from the eye s instantaneous viewpoint based on the user s head location When the user sees these images through the HMD the images fuse to form a single stereoscopic view of the virtual environment The user can turn in any direction and see objects in the environment in the same way that one can turn one s head and discover what is behind him Additionally the user can move or fly through the environment by 12 means of a hand held joystick For testing the proje
72. ecting a node from the node set browser and returns the node 4 1 4 BlankSheet Class BlankSheet holds the DrawArea and provides the framework for simulation model sheet window It looks like as follows public class BlankSheet extends JFrame implements ActionListener public BlankSheet public void processComponent ComponentEvent ce public void actionPerformed ActionEvent ae Constructor BlankSheet performs all the necessary steps to set the UI It instantiates a new DrawArea and add it to the frame also instantiates menu and menu items toolbar and status bar The processComponent method is used to store the current size and location of the frame whenever it is resized or moved by the user The actionPerformed method handles all the menu actions and toolbar icon actions Other than these methods this class has two important private methods saveFile and openFile These methods are used to save a model or to open an existing model 4 1 5 MainFrame MainFrame class is responsible for the node set browser window which is the starting window of SimDynamic public class MainFrame extends JFrame implements ActionListener public MainFrame 52 public void actionPerformed ActionEvent ae Constructor MainFrame instantiates all the components necessary for the user interface UI The actionPerformed method is responsible for all menu actions and toolbar icon actions This method also creates a new Blank
73. ection is then built to a Java server The simulation runs remotely on this server the results are transferred to the client 26 browser and visualized locally The user can interact with the model by using buttons on the HTML page Internet i Calling the WWW Server 2 Configuration and download applets 3 Server client connection 4 Simulation control and results Fig 2 6 Remote simulation and local visualization Finally the paper introduces Skopeo a Java based platform independent 2D Animation system Yiicessan et al 30 introduces a Parallel Discrete Event Simulation PDES support system to distribute simulation experiments over the Internet There are several approaches to exploiting parallelism in discrete event simulation unfortunately each of those has its own drawbacks For example a in Dedicated Execution approach where dedicated functional units execute specific sequential simulation functions the speed tends to be limited as the management of the future events list becomes the bottleneck b in Hierarchical Decomposition where a model is decomposed in a hierarchical fashion such that an event consisting of several sub events to be processed concurrently is largely model dependent c in Parallel Replication where several replications of a sequential simulation are executed independently on different processor each processor must contain enough memory to hold the entire model The paper describes a
74. ections Ethernet bridges were used via 56 kbps dial up links to connect two or more LANs to form a single logical Ethernet LAN The success of SIMNET resulted in a standardized simulation networking protocol Distributed Interactive Simulation DIS After several revisions and extensions of the SIMNET protocols in March 1993 the first standards were formally approved 21 DIS is a protocol for communicating position and other information to other entities in a simulated battlefield Each entity can see each other and interact in a virtual environment Although DIS began in the military environment it is now being used increasingly often in non military applications such as air traffic control intelligent vehicle highway systems and interactive multi user computer games Neilson and Thomas 22 discuss the use of the Interact Simulation Environment ISE created as an aid to teaching engineering students At the heart of ISE is an interactive environment in which a simulation can be integrated into a distributed 21 hypermedia system Such integration permits a simulation to be treated as just another medium of expression equivalent to media such as the text graphics and sound It allows a simulation to be treated as a resource along with other resources such as video clips graphics etc It allows a simulation to be used not only with a wide variety of supporting material but also in a wide variety of contexts thus increasing the us
75. eet window The interface incorporates on line help for user guidance and assistance The help provides all the information needed to get along with SimDynamic such as how to build a model simulation parameters functional description of nodes etc Figure 3 3 provides a screenshot of SimDynamic on line help 24 SimDynamic Help Introduction ogee HOW TO Build a Model e Runa Model Manipulate Lines Manipulate Nodes Simulation Parameters J Node References SimDynamic is a java based simulation package for modelling of moderate complexity can be constructed with minimum of effo systems modelled in continuous time sampled time or a hybrid graphical result simDynamic is actually a collection of several functional nodes integration generation of sine wave multiplication etc These nc are Classified into following eight node sets Continuous Tables Non Linear Sinks Fig 3 3 SimDynamic on line help 3 3 Nodes Nodes are the basic building block of model Each node performs a specific task at each simulation time step A node comprises one or more of the following a set of inputs a set of states and a set of outputs Fig 3 4 Logical representation of a node A node s output is a function of time and the node s inputs and states if any The specific function that relates a node s output to its inputs states and time
76. efulness of the simulation as a learning resource Some additional contexts are use as a stand alone simulation in a lecture with no supporting hyper textual material use within a laboratory class for demonstrations structured enquiries open ended enquiries and projects use in studies of parametric changes use in optimisation of system parameters and in tests of sensitivity giving students experience of modelling systems input and output datasets can be provided from which students have to deduce the model which gave rise to that output given a specification students could be asked to provide a model to achieve that specification Students could be required to produce hypertext reports of their work which include links to the simulation in various states Because of differences in student s background computer knowledge the ISE is set up to utilize simulation as a modelling tool without requiring the student to interact with the actual simulation interface Cole and Tooker 23 have developed WWW based physics tutorials to assist physics students Making these simulation models available over the WWW greatly expands the range of possible access locations Like ISE the physics tutorials allow students to see interesting cases of a given simulation model without requiring prior knowledge of the parameters defining these cases or of the background programming languages involved Additionally the use of familiar WWW browsers such as Netscape v
77. el 6 5 Qualitative Decision Making QDM This section presents an effort to model a qualitative decision making system using SimDynamic Consider the following system 133 IP IP Decision Making OP IP System IP Fig 6 24 Qualitative decision making system It has four inputs IP and one output OP and the characteristics of the system are as follows 6 5 1 All input and output values are measured on a numerical scale ranging from 0 to 1 0 If output OP value is less than 0 5 then it is unsatisfactory IP and IP have major impact on the system and IP and IP have minor impact on the system Threshold values for IP and IP2 are 0 55 and 0 6 respectively Threshold values for IP and IP are 0 4 and 0 3 respectively If either IP or IP2 is less than its threshold value then output must be unsatisfactory less than 0 5 irrespective of other input values If either IP or IP is at its threshold value all other inputs must be above or equal to their respective threshold values to produce satisfactory output If any two inputs are below their respective threshold values at the same time then output must be unsatisfactory Building the QDM Model This system can be modelled easily using logical programming But here a heuristic approach has been adopted Following differential equation is used for each of the four inputs fo a pee FO 6 12 dt dt 134 Arithmetic average of the output
78. embly or installation The tool design is imported to the model combined with the part and dynamic representations of the machines that will produce the part Machines are programmed to operate identically in the virtual environment as they would in the real environment Design problems such as collisions clearances missing manufacturing features fit and manufacturing sequence issues are quickly identified Manufacturing and design concepts are easily developed with minimal cost to the program The ability to insert people in the environment and analyze their activities minimizes ergonomic problems Factory 10 modeling allows engineers to predict the cost and schedule impact of potential design or process changes The information provided by the simulation enables what if analysis The model provides support personnel with a tool to optimize the manufacturing try new concepts without disrupting manufacturing and predict changes due to load variations The three common elements in the evolution of a product are cost schedule and information Virtual Manufacturing allows engineers designers suppliers and others to understand the cost and schedule impact of decisions and to consolidate processes Overhead costs are addressed by improving schedule performance The amount of time it takes to design plan tool and manufacture a product directly impacts overhead costs A reduction in time it takes to get a product to market equates to
79. ement of the respective object in the 3D animation Since all the behavior classes have same structure here BoxBehavior class is used to describe the structure public class BoxBehavior extends Behavior public BoxBehavior TransformGroup tg Struct3D st3d Unilnfo uniinto public void initialize public void processStimulus Enumeration criteria Constructor BoxBehavior initialises all parameters for the box object initialize method allows a Behavior object to initialise its internal state and specify its initial wakeup condition processStimulus method is the main method in the class At each simulation time step this method reads the current position x y z co ordinate of the object from the file and move the object to the new position thereby animates the 3D model 4 4 Solution of Ordinary Differential Equations An ordinary differential equation or ODE 47 for short may be thought of as a differential equality specifying the relationship between a dependent variable say y and an independent variable say x The order of the ODE is the order of the highest derivative of y with respect to x appearing in it Hence dy dx x y 0 is a first order 65 ODE whereas y dx ky 0 is a second order ODE The problem consisting of a differential equation together with an initial value is called an initial value problem IVP Here all the yp are given at some starting value tn and it is desired to find the Yn 1 at som
80. emory node in the continuous node set delays its input signal by one simulation time step but this node can be used to delay a signal by a multiple of simulation time step User has to specify the sample time on the parameter dialog box and an initial output value At the start of the simulation the node outputs the initial value after then it outputs delayed signal Parameters e Initial Condition The node outputs this value for the first sample interval Default value is 0 e Sample Time The time interval at which output will be updated Default is 1 80 5 3 Tables Node Set 5 3 1 Direct Look Up Table 2D The direct look up table enables user to find a single element or a column from a 2D table For searching a single element node accepts two inputs a row index and a column index To output a column this node needs only one input the index of the column For example following table is set on the parameter dialog box If the node is in element selection mode and it receives input 2 2 then it will output 5 9i If the node is in column selection mode and it receives input 1 then output from the node will be the second column 5 94 9 34 56 of the table Parameters e Action Type Selecting an element or column from the table Default is element selection e Table Data The table to be searched Default is the following table 2 3 56 7 21 5 3 2 Look Up Table The look up table node maintains
81. erface to a shared environment these software solutions have two main shortcomings Firstly most of these packages are system dependent all participants in a project are bound to use a system that is supported by the groupware application All participants must own the software to use The second disadvantage of any existing groupware is the lack of simulation awareness It will support the communication of project participants exchange of files but it is still detached from the actual application simulation Efficient Maintenance Web based simulation programmes enable reliable version control and frequent model modifications In most of the traditional groupware packages modifications can be tedious and time consuming Users must be made aware of the need for and existence of an updated model Then update must either be made on each copy of the model or the new model should be delivered to each participant and the old one deleted In Web based modelling there is only one model at a particular time that is residing on the Web server The existence of a single working model enables modifications with smaller degree of error Additionally the model creator has access through the Internet to the model s source code on the serving computer This allows that person to access the server from distant site Also the most up to date version of the model is instantly available through the server to all authorized users regardless of the physical loc
82. erical real only Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Z P ontinuous ot applicable Single number vector matrix P Integrator Numerical real only If reset and or external initial condition are enabled Continuous Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter umerical real complex ontinuous upported ingle number vector matrix Node name tate Space Data type supported Numerical real only Direct feedthrough If D is non zero matrix Sample time ontinuous Expansion of input parameter xpands only the initial condition Input single number Parameters Dimension of input parameter i i PeP single number vector matrix Node name Data type supported same number of columns Sample time Expansion of input parameter Input single number Numerator single vector matrix Denominator single vector Dimension of input parameter A 2 Numerical real only Sample time Continuous Dimension of input parameter Numerical
83. ers and their naval customers to walkthrough the model with visual feedback Immersion provides for evaluation other design criteria such as ergonomics before a design is built 11 The most recent development in the use of Virtual Manufacturing software is the use of the Virtual Collaborative Engineering VCE environment that links multiple users at multiple locations to discuss analyze and review simulations over a wide area network VCE users interactively evaluate design concepts manufacturing tooling processes and factory layouts even at geographically remote locations Any VCE user can assume control of a simulation make changes or view changes made by others on the VCE network Engineers manufacturing personnel system operators or other users interact within the same simulation creating a virtual conference room for design discussion evaluation and approval Jones et al 6 describe a software engineering project initiated to explore how virtual reality might impact simulations and to gain insight into bringing the technology to commercially available simulation software packages AutoSimulations Inc 7 provided licenses and source code to their AutoMod and AutoView simulation software for the project Human Interface Technology Laboratory HITL University of Washington provided the virtual interface hardware software and necessary software engineering support The VR software used in the proj
84. ervice large and diverse audiences allows the simulation community to legitimately provide models and simulations as end products Recent advances in web technology have made the web a viable mechanism for performing publishing and distributing simulation 1 5 Advantages and Disadvantages of Web based Simulation A Web based simulation program provides several beneficial features that are lacking in available non Web based packages These features include wide availability controlled access user friendliness and efficient maintenance etc On the other hand simulation over the WWW does have a few potential drawbacks These advantages and disadvantages should be considered when deciding which simulation package best meets a user s needs This section discusses some of these features provided by a Web based simulation program Wide Availability In case of non Web based simulation software a change in platform whether change in operating system or computer hardware forces the recompilation of the simulation model programme Utilizing this type of package requires access to a computer containing the proper simulation software as well as an appropriately compiled copy of the model source The portability of a Web based model onto various platforms enables the user to access and run the model from wide spread locations There is no need to transport hardware or software to these sites or recompile the code Moreover Internet is u
85. es the central system class Sim_system and this class co ordinates the activities of objects Sim_entities that are distributed across the network Given this architecture remote interfaces must be constructed for the Sim_system Sim_entity Sim_port and Sim_event classes To illustrate the concept of the architecture the paper also provides a self explanatory example Lorenz et al 29 describe three basic approaches their advantages and disadvantages for Simulation and Animation S amp A on the web 25 a Remote S amp A Through an HTML data entry form the user specifies parameters for a simulation model A Common Gateway Interface CGI transfers the data to server an appropriate CGI script starts the simulation and prints the results in anew HTML page when the simulation is finished Fig 2 4 Remote S amp A and data transfer This approach is suitable for existing S amp A software but user has no interaction with a running simulation b Local S amp A Based on Loading Applets the user loads a Java applet into the web browser runs the simulation on the client machine Internet 1 Cali for an applet 2 Download the applet 3 Run the applet and show results Fig 2 5 Client Site simulation with loaded applets It supports user interaction and animation but not suitable for the existing S amp A tools c Animation and Manipulation using a Java Data Server the user begins by loading some applets a conn
86. est and the start up settings then launches the Distributed Simulation Salisbury et al 34 discusse a browser based simulation visualizer that has the ability to display simulated entities to any number of distributed sites Legacy visualizers have a number of drawbacks such as the tool must be present at the inception of the simulation no matter the observer is only interested to observe a relatively small time slice Most existing visualization tools are dependent on the machine for which the application was written The paper introduces a tool Jview lite to solve these problems Jview lite is the successor of a modular programme Jview which supports the simulation community s diverse set of visualization requirements Jview relies on Sun s Java 3D architecture to render an image and the use of Java Beans enables it to display objects from any data source Jview lite utilizes the same display technology but relies on the Web for distribution The client server architecture proposed for Jview lite has two principal software components the Simulation Monitor and the Simulation Viewer The Jview lite simulation viewer is an applet executing within the client s web browser On receiving a request from a client s browser the web server will send the required page and the embedded viewer applet The browser then initializes the viewer applet effectively making the browser a 3D simulation viewer The viewer in turn makes contact with th
87. eter nit Delay umerical real complex Node name Data type supported Direct feedthrough Z iscrete Supported Single number vector matrix Expansion of input parameter Dimension of input parameter ero Order Hold umerical real complex Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter upported ingle number vector matrix 3 Tables Node Set Direct Look Up Table 2D Input numerical real only Table data numerical real complex Yes Inherited Not supported Single number vector matrix Node name Data type supported Direct feedthroug Expansion of input parameter Dimension of input parameter A 4 Node name Data type supported Direct feedthrough Interpolation 2D with PreLook up Numerical real only Inherited Supported only for input ingle number vector matrix Expansion of input parameter Dimension of input parameter O n Node name Data type supported Direct feedthrough Sample time Expansion of input parameter i 3 Input single number vector matrix Dimension of input parameter Tables vector Look Up Table 2D Numerical real only Yes Inherited Supported only for input Input single number vector matrix Tables vector matrix Dimension of input parameter Polynomial
88. f WESE provide necessary support to achieve the different functionalities of environment The user may employ a hypertext markup language HTML or a text based interface to interact with the environment 32 The rapid advances in Web technology most notably Java enable to execute highly interactive and dynamic simulations animations on the Web Java 44 is a portable language because it is compiled into an architecture neutral byte code format Hence a Java application can run on any system providing that system implements the Java Virtual Machine JVM It is this portability which makes Java so attractive to Internet developers because it allows compiled Java code to be sited on one computer but downloaded interpreted and executed on another computer which isn t necessarily running on the same platform without the need to recompile the software This is of significant difference to more traditional simulation languages such as C where exporting simulation code requires recompilation and installation on each different machine Although Java is a small core language it does support extensive class libraries As a result the JVM need only be able to handle the core of the language whilst applications can be developed using the class libraries These class libraries can be further refined and extended to develop application specific packages e g simulation All versions of Java come with a set of in built class libraries which inclu
89. ge Kutta Method The idea behind Runge Kutta methods is to compute the value of f t y at several strategically chosen points near the solution curve in the interval ty ta h where h is the step size and to combine these values in such a way as to get good accuracy in Ynt 1 Each step of a Runge Kutta method goes through the following calculations k h f t h y k h f t h y By k a Oe Vee a ea K The numbers aj Bj and wj are given by the inventors of the method in the form of a table see appendix B for numerical coefficients Qo AiP io Q Paha Q Pra Aoo Pri Wo W aczner W Bogacki and Shampine 49 described a third order Runge Kutta method with second order error estimate In this method y is calculated using the following steps 67 k Jy ea i 3h h ee ES 2 l h 4 3h y h f t f t k f t k Yast E Yn G fitas Yn f t 3 1 5 it 7 2 7 h l 3h l Via Y th flt Yatt t 4 Sa amp t ft h Ya where Yn 1 is a third order and y is a second order estimate of the solution This requires three function evaluations per step the term f t h Yn 1 is carried over to the next step where it becomes f tn yn The fourth order Runge Kutta 50 method is a commonly used class of Runge Kutta methods The error in this method is O h In this method Yn is calculated using the following steps ki f tas Yn k h
90. ge provides a graphical user interface GUI for building models as block diagrams using click and drag mouse operations After a model has been defined it can be simulated using a choice of integration methods Finally the results of the simulation can be viewed as both graphical and numerical output as well as 3D animation This thesis is organized into seven chapters and five appendices A literature review is presented in chapter two It mainly covers previous work carried out by different researchers in the area of virtual reality amp simulation dynamic system simulation and Web based simulation Chapter three introduces SimDynamic from a user s point of view This chapter provides all the information needed to build a model execute and handle the GUI Chapter four describes the architectural design of the software Chapter five can be considered as an extension of chapter four but they can never be merged This chapter describes the functional ability of the core components of SimDynamic Chapter six focuses on the application of the software Some real world systems have been modelled using SimDynamic and this chapter demonstrates the results obtained from the simulation Finally chapter seven outlines the conclusions and the recommendations for further work Chapter Two Literature Survey 2 1 Virtual Reality and Simulation The popular media representation of Virtual Reality has tended to emphasize the entertainme
91. h default values Click the Add button on the dialog box Step 2 Click the cylinder button On the cylinder parameter dialog box set cylinder radius to 0 02 height to 1 and cylinder centre to 0 0 0 5 0 0 During the 3D animation one end of the cylinder will remain fixed and other end will move with the bob So enter the name of the data files for x coordinate and y coordinate that are saved using the xcor and ycor nodes in the pendulum model above for the top end of the cylinder Leave other fields with default values Click the Add button on the dialog box Step 3 Click the sphere button On the parameter dialog box set the sphere radius to 0 2 Enter the data file names for x coordinate and y coordinate that are saved using the xcor and ycor nodes in the pendulum model for the centre of the sphere Leave the z coordinate filed with default value Click the Add button on the dialog box After all three steps the 3D model editor will look like as in figure 6 10 122 SimDynamic 3D Model Editor loj x Box 0 0 0 0 0 0 0 04 0 04 0 04 Fy Cylinder 0 0 0 5 0 0 0 02 1 0 Sphere 0 0 0 0 0 0 0 2 Remove Cone Cylinder Save Sphere Param Run Fig 6 10 Pendulum 3D model To start 3D animation click on the run button Figure 6 11 shows four different snap shots of the 3D animation for the pendulum model generated by SimDynamic SimDynamic 3 SimDynamic 3D Animat Fig 6 1
92. hat value at every simulation time Step Parameters e Constant Value The output value from the node Default is 1 5 8 5 Digital Clock It outputs the simulation time at the specified sampling interval User should set the desired sampling rate on the parameter dialog box For example if the simulation step size is 0 2 then output from the clock node will be 0 0 2 0 4 0 6 But with the same step size and 0 4 sample time output from the digital clock node will be 0 0 4 O 8 wee Parameters e Sample Time The time interval at which output will be updated Default is 1 5 8 6 Discrete Pulse Generator It generates a series of pulses at regular intervals Amplitude sample time period pulse width and phase delay should be specified on the parameter dialog box Period pulse width and phase delay should be expressed in terms of sample time i e period is the number of sample periods the pulse is high and low pulse width is the number of sample periods the pulse is high phase delay is the number of sample periods before the pulse starts Parameters e Amplitude Amplitude of the pulse Default is 1 e Period Period of the pulse in terms of sample interval Default is 2 Pulse Width Width of the pulse in terms of sample interval Default is 1 107 e Phase Delay Delay before each pulse is generated Expressed in number of samples Default is 0 e Sample Time The time interval at which output will be up
93. he SIMAN simulation software The SIMAN simulation software performs the requested operation and returns the results to the user in the form of an HTML document This done by piping the output of the appropriate SIMAN process that are invoked with the CGI script as a dynamically generated HTML document The arguments to the SIMAN process are ASCII text files containing the SIMAN model These files are temporarily created by CGI scripts with the data posted to them through the HTML forms Marr et al 42 discusses a Web based simulation manager program called SimManager which executes simulation study in a parallel replications mode utilizing a set of slave processors available to it Slave engine processors need not be 31 co located They can belong either to an intranet to the Internet or to a combination thereof An engine is simply known to SimManager by its IP address SimManager also knows the performance characteristics of each engine SimManager would serve as the interface between a customer seeking assistance in simulation modelling and the simulation system When the customer accesses the web page he would click on the Catalog of Simulation models and view a description of any models that appear to meet his specific needs SimManager receives this request for work and places it in the job queue After determining what engines are available for work it assigns this workload to the engine processor It also receives back a file con
94. hen math function 2 can apply any selected by the user of the following functions on n and r e Power n e Combination c e Hypotenuse y n r e Permutation p e Modulus n r e Arc tan tan n r Parameters e Function Selects one of the above functions Default is Power 5 4 9 Matrix l The matrix node accepts a matrix as its input and can apply one selected by user of the following functions on the matrix e Conjugate e Inverse e Determinant e Trace e Hermitian e Transpose Parameters e Function Selects one of the above functions Default is Conjugate 5 4 10 Matrix 2 The matrix 2 node accepts two matrices as its input and apply one selected by user of the following functions on the matrices 88 e Addition element wise Multiplication Subtraction element wise e Division element wise Parameters e Function Selects one of the above functions Default is Add 5 4 11 Min Max The min max node outputs the minimum or maximum specified by user of its input values Parameters e Function Selects either minimum or maximum Default is Maximum 5 4 12 Product The product node can be used to have element wise multiplication or division of two signals The node accepts two inputs single number vector matrix and performs the specified operation on them To perform matrix multiplication use matrix 2 node Parameters e Function Either multiplication o
95. his window is divided into three sections First section contains the name of the currently selected node set second section shows all the eight node sets provided by SimDynamic and the final section displays the nodes in the currently selected node set Name of the currently selected node set SimDynamic Math amp Logic Node Set Node Sets a C i 3 ial AN ombinatori Absolute Bitwise Logic Doae Dot Product ere currently i selected set Cain a ib powl0 Magnitude Angle to Complex Math Function zj Logic Operator Fig 3 1 Node set window 30 To start a new model click on the new icon in the toolbar or select new from the file menu SimDynamic will open the following model sheet window figure 3 2 A new model will be drawn in the draw area of this blank sheet Toolbar of this window provides short cut buttons for various menu items User can use these buttons on the toolbar at his her convenience Status bar at the bottom of the window displays the current state of the simulation model Ready on the status bar means that SimDynamic is idle at the moment and ready for a new simulation run Initialising on the status bar indicates that SimDynamic is initialising the model and Running indicates that the model is being executed Toolbar Draw Area 2S SS l i l l i t Se Statusbar p Fig 3 2 Model sh
96. hown in figure 6 20 SirvrDynarviec Time Vs X Fig 6 20 Graphical result for bus suspension model open loop For the closed loop model same parameter values were used and simulation was carried out for 2 seconds Figure 6 21 shows the result for this simulation run SirnnDynanrmn Lime Va X a Fig 6 21 Graphical result for bus suspension model closed loop 131 6 4 4 3D Animation for Bus Suspension Model To make the 3D animation more attractive instead of a single step input discrete pulses of various amplitude and sample time are applied to the closed loop model of figure 6 19 as road disturbance For four wheels of the bus four discrete pulse trains figure 6 22 are applied to the model and their responses are saved in data files SimDynamic Time Ys iof xj E aa DE Fig 6 22 Discrete pulses applied to closed loop bus suspension model 132 To construct a 3D model of a spring damper mass system two cylinders are used One cylinder will act as the spring damper and other cylinder will act as a mass on top of it In this way the 3D model for a four spring damper mass is created and placed over a rigid platform A box is used as the rigid platform Figure 6 23 presents four different snap shots of 3D animation for the bus suspension model SimDynamic 3D Anin Fig 6 23 a Fig 6 23 b SimDynamic 3D Anime lt 10 xj Fig 6 23 c Fig 6 23 d Fig 6 23 3D animation for bus suspension mod
97. ick P A Web Based Simulation Some Personal Observations Winter Simulation Conference Coronado CA pp 772 779 1996 Buss A H Stork K A Discrete Event Simulation on the World Wide Web Using Java Winter Simulation Conference Coronado CA pp 780 785 1996 Miller J A Nair R S Zhang Z Zhao H JSIM A Java Based Simulation and Animation Environment Proceedings of the 30th Annual Simulation Symposium Atlanta Georgia pp 31 42 1997 Howell F McNab R SimJava A Discrete Event Simulation Package for Java With Applications in Computer Systems Modelling Proceedings of First International Conference on Web based Modelling and Simulation San Diego CA Society for Computer Simulation Jan 1998 Page E H Moose R L Griffin S P Web Based Simulation in SimJava Using Remote Method Invocation Winter Simulation Conference Atlanta Georgia pp 468 474 1997 Lorenz P Dorwarth H Ritter K Schriber T J Towards a Web Based Simulation Environment Winter Simulation Conference Atlanta Georgia pp 1338 1344 1997 Yiicessan E Chen C H Lee I Web Based Simulation Experiments Winter Simulation Conference Washington pp 1649 1654 1998 Chen C H Chen H C Dai L A Gradient Approach for Smartly Allocating Computing Budget For Discrete Event Simulation Winter Simulation Conference San Diego CA pp 398 405 1996 Veith T L Kobza J E Koelling C P NetSim Ja
98. ies k 25 0 Y dy m m m m Y dt m mM x Ca y 0 0 1 of 0 JM Yi w My Full State Feedback Controller Assuming that all the states can be measured the schematic of the system should be as follows The characteristic polynomial for this closed loop system is the determinant of sI A B 1 0 K To eliminate the steady state error integral control is used here After adding an extra state J x X2 J Y the state space matrices A B C and D change to be the following 0 0 bb 0 E S EE k b mm mm mn m m m Le a as mM mo m mM Les 0 l 0 m 0 0 l 0 b b M mm B 0 Fa c 0 010 0 D 0 0 2 Peel Oa m m m 0 0 Applying trial and error method the matrix for the controller K is found as follows K 0 2300000 500000000 0 8000000 Appendix D Sim Dynamic Installation Instructions This appendix provides all the instructions to install and run SimDynamic And also presents a brief manual to handle the built in models that come along with the package It should be kept in mind that examples are only available to non Web based version D1 Web based Version Requirements User will need a java compatible Web browser irrespective of the operating system Windows Unix Solaries or hardware Most browsers like Internet Explorer or Netscape now come up with built in JVM so user does not need to install anything Running SimDynamic Type the
99. ific hardware is the 5DT Glove 10 and VIO I Glasses 11 respectively 14 The SDT Glove is used by SMART as the primary manual input device The electronic glove is plugged into a PC s serial port The configuration parameters of the SDT Glove such as the bending angle of each finger and pitch and roll of the wrist are constantly sampled by the PC s serial connection and sent to SMART for processing The user controls SMART using a set of gestures to cause actions to be taken when building simulation models When a recognizable gesture is detected SMART responds with the appropriate action and provides audio feedback confirming the action The glove s configuration and its relative position in the virtual world are continuously animated by a robot like hand figure 2 1 Every motion of the user s fingers is reflected by the animated hand in the virtual world Fig 2 1 Animated hand for SDT Fig 2 2 VIO I Glasses pitching Fig 2 3 VIO I Glasses yawing Glove In addition to the 5DT Glove SMART also uses the VIO I Glasses as another virtual reality hardware interface especially for simulation model immersion enhancement The device is plugged into a PC s serial port The VIO I Glasses are designed to give the user the impression that he or she is physically present in the virtual modeling world This is accomplished by providing a virtual view using the VIO I Glasses which reacts directly to two primary head motions pi
100. ified On the other hand many systems must be modelled as having at least some random input components and these give rise to stochastic simulation models Most queuing and inventory systems are modelled stochastically and these models produce output that is itself random c Continuous vs Discrete Event Simulation Models Discrete event simulation concerns the modelling of a system as it evolves over time by a representation in which the state variables change instantaneously at separate points in time These points in time are the ones at which an event occurs where an event is identified as an instantaneous occurrence that may change the state of the system Continuous simulation models concern the modelling over time of a system by a representation in which the state variables change continuously with respect to time Typically continuous simulation models involve differential equations that give relationships for the rates of change of the state variables with time If the differential equations are particularly simple they can be solved analytically to give the values of the state variables for all values of time as a function of the values of the state variables at time 0 For most continuous models analytic solutions are not possible however and numerical analysis techniques e g Runge Kutta integration are used to integrate the differential equations numerically given specific values for the state variables at time Q
101. ig 6 14 Graphical result for bouncing ball model 0 8 simulation time along x axis ball position along y axis The elasticity was changed to 1 0 and simulation was carried out for 20 seconds Graphical result for this simulation run is shown in figure 6 15 SimDynarnec Time Ys X oe ee Fig 6 15 Graphical result for bouncing ball model 1 0 simulation time along x axis ball position along y axis 126 6 3 3 3D Animation for Bouncing Ball Model A sphere is used for the bouncing ball 3D model and the data file saved in the bouncing ball model is attached to the sphere to move it along y axis Figure 6 16 shows four different snap shots of the 3D animation for bouncing ball model generated by SimDynamic SimDynamic 3D SimDynamic 3D An i Fig 6 16 a Fig 6 16 b SimDynamic 3D Al im SimDynamic 3D Anime Fig 6 16 c Fig 6 16 d Fig 6 16 3D animation for bouncing ball model PAI 6 4 Bus Suspension A bus suspension model can be simplified considering just one wheel of the bus to a one dimensional spring damper system A diagram of this system is shown in figure Onley Fig 6 17 Bus suspension system 1 4 bus where e ml is body mass e m2 is suspension mass e kl is spring constant of suspension system e k2 is spring constant of wheel and tire e bl is damping constant of the suspension system e b2 is the damping constant of the wheel and tire e wis the distu
102. ill be updated Default is 1 5 2 3 Discrete Transfer Function This node implements a z transform transfer function described by the following equation H z n z d z where n z noz n 2 n 2 d z doz dy 2 0 00 tdn Numerator can be a vector or a matrix but denominator must be a vector The order of denominator must be greater than or equal to the order of numerator Numerator can be a vector or matrix but denominator must be a vector Width of the output from the node depends on the dimension of the numerator If the numerator is a vector 78 output will be single number If the numerator is a matrix output will be a vector with width equal to the number of rows in the numerator Parameters e Numerator Numerator coefficients of the transfer function Default is 1 e Denominator Denominator coefficients of the transfer function Default values is 2 e Sample Time The time interval at which output will be updated Default is 1 5 2 4 Discrete Zero Pole The discrete zero pole node can be used to express a discrete transfer function in terms of poles zeros gain form as follows GZ nA P z where G is gain value and Z z 2 Ly 2 Lo eves Z Zm P z z P P2 z P3 The user should set the poles p p2 and zeros z z2 on the parameter dialog box The number of poles must be greater than or equal to the number of zeros Zer
103. initial condition on the parameter dialog box internal initial condition or as an input to the node external initial condition Like the continuous integrator discrete time integrator also provides three types of state reset Parameters e Integrator One of the integration methods described above Default is Forward Euler Method e External Reset By selecting one of the reset modes nsing falling either states of the node can be reset to the initial condition Default is none e Initial Condition Value that will be used to solve the IVP Default value is 0 If user selects external initial condition then the node will accept initial condition as an input e Sample Time The time interval at which output will be updated Default is 1 ce 5 2 2 Discrete State Space The discrete state space node can be used to describe a system by discrete state variable as follows x t 1 Ax t Bu t y t Cx t Du t where u t is the system input at time t x t is the state of the system at time t and y t is the system output at time t Coefficient matrices A B C D must follow the same dimension constraint as the continuous state space node Parameters e Coefficient Matrices A B C and D matrix of the state space Default values are all one e Initial Condition Initial state value that will be used to solve the differential equation Default value is 0 e Sample Time The time interval at which output w
104. ion calculation of poles and zeros pole placement Standard diagrams like 2D line Bode Nyquist and root locus diagrams Special diagrams like parallel coordinates to visualize optimization criteria DSSIM is the run time environment of ANDECS to simulate dynamic systems It uses following well tested numerical integration routines e DEABM Multi step solver of Shampine Watts for non stiff and moderately stiff ODEs e LSODE Multi step solver of Hindmarsh for stiff and non stiff ODEs e LSODAR Miulti step solver of Petzold Hindmarsh which switches automatically between a non stiff and a stiff integration algorithm along the solution LSODAR also provides a root finder e RK45 78 Runge Kutta Fehlberg solvers of Kraft Fuhrer of fixed orders 5 and 8 with variable step size using the Prince Dormand coefficients 19 e GRK4T A stable linearly implicit Rosenbrock type single step solver of fixed order 4 for stiff and oscillating ODEs of Arnold e DASSL RT Multi step solvers of Petzold for DAEs and for DAEs with root finder e ODASSL RT Multi step solvers of Fuhrer based on DASSL RT of Petzold for ODAEs and for ODAEs with root finder e MEXX Extrapolation solvers of Lubich for a restricted class of index 2 ODAEs There are a wide variety of options available to define a simulation experiment The result of simulation experiment is a set of computed signals which are automatically stored on a database and
105. irtually eliminates the amount of time necessary for distributing and learning viewing software The tutorials use Apple s OpenDoc Frameworks to provide a basic simulation environment OpenDoc is based on the idea of container documents and components that act on documents Using OpenDoc one can build software components in this case physics simulation models It can be then embedded in container documents such as a web browser document or word processing document depending upon the need of the user The main attribute of the environment is its extensibility One can extend the power of the environment by adding parts This extensibility means it is functional for a broad spectrum of users 22 Fishwick 24 focuses on three important aspects of simulation that might be affected by incorporating it in the Web a b c Education and training Using the WWW for education and training allows and encourages the reuse of knowledge by providing us with effectively infinite storage unlike the CD ROM and diskettes The storage is on the Internet and not limited to one s own machine Therefore simulation packages which include help text and information about the pieces of the model can have these pieces on the Web so that they do not require local storage It is not necessary for a company to re build every piece of information about a manufacturing process for instance from scratch Information on automated guided vehicles AVGs
106. is the distance from the throwing point at time step Sum3 node calculates the position i e y of the ball Fig 6 13 Bouncing ball model in SimDynamic from the ground at time step Saturation node is used to limit the value of y within the range 0 to 15 Time vs Input node is used to draw the position of the ball against the simulation time The position of the ball is always compared with constant value 0 using a Relational Operator node If the ball position is 0 then velocity will be calculated using equation 6 7 otherwise it will be calculated using equation 6 5 For this switching between equations a Multi Port Switch node has been used Control input to the switch node is the output from Relational Operator 1 has been added to the output for indexing purpose node Two Initial Condition nodes labelled as vg and yo are used to provide initial velocity and initial position of the ball A Gain node labelled as elasticity is used for the elasticity of the ball To save the ball position at every simulation time step for 3D animation a To Dat File node is used 6 3 2 Running Simulation and Viewing Result Simulation was carried out for 20 seconds with step size 0 005 Following parameter values were used 125 e Initial Velocity Vo 10 m s e Initial Position Y 15 meter e Elasticity 0 8 Graphical result for this simulation run is shown in figure 6 14 SimDynamic Time Ys X F
107. istance during computer hardware failure things could be dreadful to me He also provided all the necessary software for the project at the high time I like to thank my fellow graduate students for their spontaneous supports comments and particularly their joyfulness that made my hard times smoother Finally I make special mention of my parents who always motivated me for higher studies They have supported and understood me every time and everywhere Special thanks to them for everything they have done and given to me iii Dynamic System Simulation on the Web by Khaled Mahbub ABSTRACT Computer simulation is the discipline of designing a model of an actual or theoretical physical system executing the model on digital computer and analysing the execution output Of late simulation has been influenced by an increasingly popular phenomenon the World Wide Web or WWW Java is a programming language for the WWW that brings a high level of dynamism to Web applications Java makes it particularly suitable to represent applications on the Web It has created an illusion of machine independence and interoperability for many applications Therefore WWW can be considered as an environment for providing modelling and simulation applications Research in the area of Web based simulation is developing rapidly as WWW programming tools develop Bulk of this research is focused only on discrete event simulation This dissertation introduces dy
108. k f t h 2 T t 2 Y Ti h k k f t 2 y h 2 3 S t 2 Vs T ka ft h y h k Yar Yq tlk 2 K 2 k k Dormand Prince 51 method requires calculation of seven kj values ko ki ke for each integration step h From these k values two approximations can be found as follows 6 as Xi w k j 0 6 A _ A Yna X k j 0 68 Yn 1 is the more accurate approximation and has local error of order 5 yn is only order 4 and is usually less accurate 4 5 How SimDynamic Works Simulating a dynamic system is nothing but the process of computing the states and outputs of the system over a particular period of time In SimDynamic each functional node calculates its own output and state at a particular time step through the action method So the only requirement to simulate a model is to co ordinate the functional nodes to make them interact as a system and the Simulator class does this When a user clicks the Start from the Simulation menu the Simulator class takes over the control and the simulation starts It accomplishes its task in the following two phases 4 5 1 Initialisation Phase In this phase Simulator performs the following tasks in the order they are described Checks for Integrator Simulator checks whether the model needs an integrator If it does need an integrator and no integrator has been selected Simulator will generate error message Sorts Lines It should be make sur
109. l and vice versa For boolean to numerical conversion the node follows the following rules e If input is true output is 1 e If input is false output is 0 For numerical to boolean conversion following rules are followed e If input is non zero positive or negative output will be true e If input is zero output will be false Parameters e Convert to Select whether output will be boolean or real Default is real 5 6 4 Zero Crossing The zero crossing node can be used to detect whether a signal crosses a given crossing offset User has to specify the offset value and the direction of crossing on the parameter dialog box The node provides three crossing directions Output from the node may be boolean or real Let u is the current input u _ is the input at the 98 previous step and A is the crossing offset then depending on the crossing direction output from the node will be as follows e Rising if uw gt u _ and u lt h lt u then output will be true for boolean output 1 for real output otherwise output will be false or 0 e Falling if u lt u _ and u lt h lt u then output will be true for boolean output 1 for real output otherwise output will be false or 0 e Either output will be true or 1 if input crosses the zero offset either way otherwise it will be false or 0 Parameters e Output Data Type Selects the data type boolean real of the outpu
110. l script to demonstrate current workstation resource queuing sizes based on user input gathered through an HTML form Besides the value of simulation in aiding understanding Fishwick discusses the possibilities of WWW based simulation or simulation interfaces for multi user situations such as multi user dungeons MUD where users typically co operate to reach a solution and DIS discussed earlier SimKit created by Buss and Stork 25 is a set of Java classes for creating discrete event simulation models They use the event graph design approach to build models SimKit models particularly geared to military applications can easily be implemented as applets and executed in a web browser The main simulation facilitating package JavaSim is designed to allow for expansion in order to accommodate frameworks for various types of simulations JavaSim makes extensive use of Java interfaces which add defined behaviours to identified classes without imposing the hierarchical structure of class inheritance This structure allows a modeller to add customized tools into SimKit without making changes to JavaSim SimKit permits user interaction through a detailed model entry form Additionally SimKit is combined with a Java based graphic package designed by Leigh Brookshaw to allow a useful output of statistics and graphs Miller et al 26 present JSIM a java based simulation and animation environment It demonstrates WWW based simulation through
111. lic void upDateOutput double simT public void initialize abstract public void makeDialog abstract public void cancel abstract public boolean checkConsistency abstract public boolean action double simt Constructor Nodelcon initialises all common attributes sample time number of ports etc of a node It loads the default image and default name for the node The method set pOpX sets the X co ordinates for the input and output ports of the node The method adjustName adjusts the name of the node when user changes the node s default name The method alert draws a red rectangle around the node when simulation is halted because of a run time error It helps the user to identify the node in which error has been occurred The method adjust_ipY sets the Y co ordinate for the input ports of the node The method actionPerformed ActionEvent ae handles all the node popup menu actions The method reload mage reloads the image for the node whenever user change the function of a node The method paintComponent Graphics g repaints the node whenever it is required such as node has been dragged or node popup menu has been invoked etc The method processMouseEvent MouseEvent me handles all user mouse actions such as user double click on the node for the parameter dialog box or click the right mouse button for node popup menu or press the mouse to drag the node etc The method processMouseMotionEvent MouseEvent me
112. lt Simulation was carried out for 20 seconds using RK_DP integrator with an initial displacement P 3 and step size 0 2 Following parameter values were used e Mass m kg e Torque Coefficient c 0 1 N m s rad e Length L 1 meter e Acceleration due to gravity g 9 8 m s 119 Figure 6 8 shows the graphical result followed by the numerical for first 10 seconds result in table 6 1 SimDynamuc Time Ws X Fig 6 8 Graphical result for pendulum model rod length 1 meter simulation time along x axis 6 along y axis Table 6 1 Numerical result for the Pendulum model rod length 1 meter 1 04719 1 08213 0 77975 0 22108 0 40648 0 87646 1 04774 0 88515 0 43412 0 16275 0 68849 0 96670 0 92649 0 58400 0 04356 0 50209 0 85855 0 92165 0 68083 0 20929 0 32930 0 73782 0 88519 0 73560 0 33655 0 17666 0 61489 0 82847 0 75837 0 43005 0 046653 0 49669 0 76006 0 75761 0 49530 0 06085 0 38744 0 68627 0 74013 0 53770 0 14752 Time Output _ Time Output _ Time Output Time Output Time Output 0 28935 0 61154 0 71130 0 56206 0 21571 0 20322 0 53890 0 67523 0 57249 0 26804 For a what if exploration pendulum rod length was changed to 0 5 meter and simulation was carried out for 10 seconds The graphical and numerical first 10 120 seconds results for this simulation respectively run are shown in figure 6 9 and
113. ly with tactile and other forms of feedback The simplest form of virtual reality is a 3D image that can be explored interactively at a personal computer usually by manipulating keys or the mouse so that the content of the image moves in some direction or zooms in or out This type of experience is known as desktop VR or non immersive VR On the other hand the strict definition of VR involves the sense of total immersion To experience a virtual reality a user dons special gloves earphones and goggles all of which receive their input from the VR system In this way at least three of the five senses are controlled by the VR system Therefore the user is in computer generated 3D artistic renderings of real or imagined spaces From these above definitions Virtual Reality can be divided into e The simulation of real environments such as a manufacturing process or a spaceship often with the purpose of training or education e The development of an imagined environment typically for a game or educational adventure 1 1 1 Types of VR System VR systems can be classified based on the mode with which they interface to the user The most common modes used in VR systems are as follows a Window on the World Systems WoW In this system a conventional computer monitor is used to display the visual world This is sometimes called Desktop VR or a Window on the World WoW This concept traces its lineage back through the entire histor
114. m software engineering point of view some of the aspects of SimDynamic are worth mentioning here Functionality Scope The most fundamental property of a simulation tool may be its scope that is the set of simulations that it is potentially able to execute There are many tools designed for simulating a specific type of models These tools may give very good support for the purpose they are designed for but they tend to be too restrictive for simulation of complex domains Because of a rich collection of functional nodes as a whole 83 in this implementation SimDynamic offers modelling of a wide range of real world dynamic systems including mechanical electrical thermodynamic and fluid mechanic systems Not only real world systems it can be used to solve mathematical equations or to perform simple matrix operations A model of moderate complexity can be constructed with minimum of effort and without writing any code It supports linear and non linear systems modelled in continuous time sampled time or a hybrid of the two Finally it provides both numerical and graphical result and also 3D animation Maintainability Expandability The object oriented structure offered by Java and maintained in SimDynamic allows easy expansion of the package The functional nodes have been implemented as individual objects classes So extension of the package can be achieved easily by creating additional functional nodes classes and incorporating them t
115. merical real complex Table numerical real only Yes Inherited Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter A 6 ate Limiter umerical real only Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter R nherited upported ingle number vector matrix Relay Numerical real only Yes Inherited Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Satuaration Numerical real only Yes Inherited Supported Single number vector matrix Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Node name Switch _ _ _ O O Control input numerical real only Threshold value numerical real only Input numerical real complex Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Data type supported Miscellaneous Node Set Node name Data type supported Direct feedthrough Sample time Expansion of input parameter Dimension of input parameter Cumulative Function Numerical
116. meter dialog box Using these parameters and the input signals the node will find the final output Following example will clarify the matter Let initial output defined on the parameter dialog box is 2 2 5 7 3i 3 2 4 12 1 8 First input is 2 92 1 45 Second input is 3 4 71 If offsets are 0 and 4 for first and second input respectively then output will be 2 92 1 45 7 31 100 3 2 3 4 7i If offsets are 3 and 1 for first and second input respectively then output will be 2 2 3 4 7i 2 92 1 45 1 8 Parameters e Initial Output Initial value of output upon which the input signals will be superimposed Default is 1 e Input Port Offsets Offset of each input signal relative to the beginning of the output signal Default is 0 5 6 8 Mux The mux node combines two signals into one signal This node accepts two signals and simply appends the second signal to the first signal Parameters e None 5 6 9 Probe The probe node enables user to find information about a signal Using this node user can find one of the following information e Width outputs the number of elements in the signal e Sample time outputs the sample time of the signal e Complex outputs 1 if the signal contains complex number otherwise outputs 0 e Dimension outputs the number of rows and columns in the signal Parameters e Probing Select one of the information described above Default is Width 5 6 1
117. n mind that a larger step length for this example will produce inaccurate result If you change step length then set the same value in the node labelled as t in the model To change any model parameter like initial velocity node labelled as v0 initial height node labelled as y0 elasticity node labelled as elasticity click on the appropriate node Set new parameter value and click OK Follow steps 6 and 7 from previous example to run and view result To start 3D animation select Simualtion gt 3D animation 3D Model editor will open To load the bouncing ball 3D model click on load in the model editor a dialog box will appear enter bounce s3d and click on load on the dialog box Bouncing ball 3D model wil be loaded Before starting 3D animation save the simulation result in data files To do so double click on the node labelled as To Dat File a dialog box will appear in the file name field type bounce and click on save Now click on Run button at the bottom of 3D editor 3D animation will start Whenever you change any parameter like initial velocity initial height elasticity in the simulation model you have to save the data file bounce as in step 6 To close bouncing ball model close the model sheet window by clicking the cross at the top right of the window or by selecting File gt Exit
118. namic system simulation on the Web It presents and demonstrates a Web based simulation software SimDynamic entirely developed in Java for modelling simulating and analysing dynamic systems with 3D animated illustration wherever applicable SimDynamic can also be used as a non Web based application on a PC In both cases it supports complete model creation and modification capabilities along with graphical and numerical output Detail design and functional ability of SimDynamic are provided Some real world systems have been modeled using SimDynamic and results are presented Characteristic features of the software are discussed from software engineering point of view Complete source code and installation instructions are included Current SimDynamic limitations and potential customization and expansion issues are explored Table of Contents Title Declaration Acknowledgement Abstract Contents List of Figures List of Tables Chapter 1 Introduction l l Virtual Reality 1 2 Simulation 1 3 World Wide Web 1 4 Web based Simulation 15 Advantages and Disadvantages of Web based Simulation 1 6 Objective of the Project Chapter 2 Literature Survey 2 1 Virtual Reality and Simulation 22 Dynamic System Simulation Pa Web Based Simulation Chapter 3 Overview of the SimDynamic 3 1 What is SimDynamic 3 2 Graphical User Interface 3 3 Nodes 3 4 Lines 3 5 Simulation Parameters 3 6 Run a Model and View Output 3 7 3D Animation Page 34
119. ndendees i DefaultHelpBroker eee SS eeuneesecase apeceseencoes HelpFrame BlankSheet Fig 4 2 Class hierarchy in UIPack package 4 1 1 MyLine The MyLine class is responsible for handling all the line drawings in a model and its definition looks as follows public class MyLine extends Object public Vector points private int lx ly public MyLine Container c Simulator s p blic void drawL Point p int d public void drawC int w Color clr public void setArrowPoints boolean rotated public void drawA public void adjustA int tx int ty boolean rotated public void adjustR int tx int ty boolean rotated Public boolean cheekPoint Point p Point pt 50 This implementation of MyLine allows only vertical and horizontal lines A complete line may contain indefinite number of vertical and horizontal segments MyLine stores end points of every segment in the Vector points Ix and ly stores the last drawing point of the line Constructor needs a Container on which the line will be drawn and a Simulator which will store the line Method drawL draws a line segment from Ix ly to the current point p This method also determines the direction vertical or horizontal of the line The drawC method draws a circle at the last point of an incomplete line The setArrowPoints method calculates the co ordinates of an arrow for a completed line The drawA method draws an arrow at the last point of a
120. nding on the input output from the node will be e Output 0 if the input is greater than the lower limit and less than the upper limit 93 e Output input upper limit if input is greater than or equal to upper limit e Output input lower limit if input is less than or equal to lower limit Parameters e Start of Dead Zone Lower limit of dead zone Default is 1 e End of Dead Zone Upper limit of dead zone Default is 1 5 5 4 Manual Switch The manual switch node enables user to select one of its two inputs on the parameter dialog box to pass through to the output Parameters e Input Selection Select one of two inputs Default is first input 5 5 5 Multi port Switch The multi port switch node has two inputs and a control input Depending on the control input output from the node will be as follows e Ifcontrol input is 1 output first input e Ifcontrol input is 2 output second input e Ifcontrol input less than 1 or greater than 2 ERROR message Parameters e None 5 5 6 Quantizer The quantizer node passes its input signal through a stair step function The effect is to quantize a smooth signal into a stair step output If u is the input and q is the quantization interval specified on the parameter dialog box then output from the node will be y q round u q 94 Parameters e Quantization Interval The interval around which the output is quantized Def
121. nds the largest integer not greater than x e Ceil x finds the smallest integer not smaller than x e Fix x rounds to the nearest integer towards zero 90 Parameters e Function Selects one of the four functions Default is Round x 5 4 17 Sign The sign node can be used to indicate the sign of a signal If the input to the node is positive output will be 1 if it is negative output will be 1 if it is 0 output will be 0 Parameters e None 5 4 18 Sum The sum node adds its inputs element wise By changing the sign of inputs subtraction can also be performed Parameters e Sign of First Input Positive or negative e Sign of Second Input Positive or negative 5 4 19 Trigonometric Function This node performs numerous common trigonometric functions The node accepts real or complex signal and outputs the result of one of the following functions operating on the input e sin e atan cos e sinh e tan e cosh e asin e tanh e acos Parameters e Function Selects one of the above functions Default is sin x 91 5 5 Non Linear Node Set 5 5 1 Backlash The backlash node can be used to model backlash 55 effect of gears or similar drive systems When gears are engaged figure 5 1 a change in input shaft causes an equal change in output Backlash Fig 5 1 Backlash in gears LQ 0 5 0 0 5 1 0 Lg en OLgun W deadband gt Fig 5 2 Dead band This
122. node is the only way to transmit simulation result to the 3D engine Parameters e File Name Name of the file to be saved Default is None 105 5 8 Source Node Set 5 8 1 Bool This node generates a boolean value User has to specify the value true false on the parameter dialog box and the node generates that value at every simulation time step Parameters e Boolean Value The output value from the node Default is true 5 8 2 Chirp Signal The chirp signa node produces a sine wave whose frequency increases at linear rate with time Initial frequency target time and final frequency of the signal should be specified on the parameter dialog box Parameters e Initial Frequency Initial frequency of the signal Default is 0 1 Hertz e Target Time The time at which the signal frequency will reach the target frequency Default value is 20 seconds e Target Frequency Final frequency of the signal Default is 1 Hertz 5 8 3 Clock This node simply outputs the current simulation time at each simulation step For example let step size is 0 3 and simulation start time is 5 0 then output from this node for first few steps will be 5 0 5 3 5 6 5 9 This node is useful for other nodes that need the simulation time Parameters e None 106 5 8 4 Constant This node generates a constant value User has to specify the value real complex on the parameter dialog box and the node generates t
123. nodes from the Math and Logic node set and rotate them using the rotate option of the node menu Rename the Gain nodes as 0 5 and 11 Set Gain node parameters to 0 5 and 11 respectively on the parameter dialog box Draw a branch line from the output line of x2 ntegrator and connect it to the input port of 0 5 Gain node Draw a branch line from the output line of x1 ntegrator and connect it to the input port of 11 Gain node Step 3 Add two Sum nodes from the Math and Logic node set and rotate them Rename the Sum nodes as sum and sum2 Connect the outputs of both Gain nodes to the inputs of sum node Connect the output of sum1 node to one input of sum2 node Change the sign of this input to minus After steps 2 and 3 the model will look like as 114 in figure 6 2 Now it has almost the complete representation of the differential equation except the input i e sine term in the equation iImMD ynani Ose Fig 6 2 Step 2 and 3 for ODE model Step 4 Add a Clock node from the source node set rotate it and rename it as t Adda Gain node from the Math and Logic node set rotate it rename it as 3 77 and set the gain parameter to 3 77 on the parameter dialog box Connect the output of the clock node to the input of the Gain node Add a Trigonometric Function node from the Math and Logic node set and rotate it Connect the output of the 3 77 Gain node to the input of the Trigonometric Function node Select the sine function from th
124. nodes that support both types of data It is up to the user to decide whether Boolean value or numerical value is to be used 3 3 4 Node Parameters Nodes are added to the models with their default parameters Each node has its own parameter dialog box to set parameters To access a node s parameter double click on 39 it parameter dialog box of the corresponding node will be opened Now the user can change parameter values After changing the parameter values click the ok button on the dialog box A click on the cancel button will ignore any change in the parameters and will retain the previous parameter values Parameter Dialog Box for Discrete State Space node is shown here for example Fig 3 6 Parameter dialog box for Discrete State Space Most nodes accept vector 1D array or matrix 2D array parameter input There are some nodes that accept only single number Vectors may be of two types i row vectors 2D array contains only one row ii column vector 2D array contains only a single column To enter a row vector as parameter write the numbers separated by spaces Following dialog box shows how to enter the row vector 2 3 4 as Gain parameter Fig 3 7 Row vector as parameter 40 To enter column vector as parameter write the numbers separated by commas Figure 3 8 shows how to input a column vector as gain parameter Fig 3 8 Column vector as parameter To enter a matrix as parameter enter th
125. nt aspects of the technology But a virtual environment can be used as an effective tool for simulation training and education Barnes 3 provides an awareness of Virtual Reality with respect to simulation The newer workstations and PCs provide performance that support VR at an affordable price VR software is now available to run on these platforms Some of this software comes with simulation engines that support simulation modelling and analysis The paper describes the tools and methods that are particular to VR and illustrates how these are being applied to simulation The applications presented in the paper focus on virtual manufacturing and the VR world animated with behaviour controlled by a simulation engine which uses simulated behaviour rules and model data Sikorsky Aircraft Co USA and British Aerospace UK used simulation software like QUEST 4 IGRIP 5 to recreate the actual manufacturing process Visualization allows the user to assemble the components in a Virtual Manufacturing cell This could be by utilizing the 3D models of the part designs using popular CAD packages such as Unigraphics Intergraph CATIA or Pro ENGINEER to visualize how the components relate to each other In the model the user can manipulate and view each of the components to develop a manufacturing approach Virtual Manufacturing modelling allows the user to introduce the engineering design to the processes that will be used to create the actual part ass
126. o the main engine simulator of SimDynamic Usability Ease of Use SimDynamic presents a simple and friendly graphical user interface GUI Users can build models as block diagram using simple mouse operation It also provides appropriate error messages whenever an error is encountered in a model during a simulation run 137 Efficiency Object oriented paradigm utilized to develop SimDynamic reduces wasteful use of system resources such as memory and processor cycle resulted from reuse of code Moreover to store models SimDynamic uses Java built in class Vectors that expand themselves as necessary instead of being initially defined with unchanging sizes As a result the amount of memory required by the package at a given time depends on the number of nodes and lines within the current model and that ensures the optimal memory management Reliability Generally the reliability of the software comes down to a simple question does the software provide correct and complete result Now implementation of SimDynamic involves the use of most authenticated and proved algorithms Therefore it is desirable that it will produce correct result Furthermore some of the SimDynamic results were compared to the results from well established simulation package SIMULINK 60 and in all cases results from Simdynamic matched with the results from Simulink Portability Software portability is the ability to use the same program in multiple envir
127. ocess nodes are classified as follows e Direct feedthrough nodes nodes whose current outputs depend on their current inputs are called direct feedthrough nodes e Non direct feedthrough nodes nodes whose current outputs do not depend on their current inputs are called non direct feedthrough nodes e Source nodes nodes that do not need any input to produce output e Sink nodes nodes that do not produce any output With this node classification Simulator uses the following rules to determine node execution order e Source nodes should be executed before any nodes in the model This ensures that their output will be valid at every time step e Sink nodes should be updated after all the nodes in the model have been updated This ensures that sink nodes will always receive valid input e Every other node must be executed before any of the direct feedthrough nodes that it drives This rule ensures that inputs to direct feedthrough nodes will be valid when they are updated e Non direct feedthrough nodes can be updated in any order as long as they are updated before any direct feedthrough nodes they drive This can be accomplished without much difficulty by putting all the non direct feedthrough nodes of a model just after the source nodes in the execution list By applying above rules Simulator generates a node execution list in which source nodes appear at the head followed by the non direct feedthrough nodes in no 70 particular
128. ode A Time vs Input node and a display node are used for graphical and numerical results respectively 6 5 2 Running Simulation and Viewing Result Simulation was carried out for 10 seconds with step size 0 1 and RK_DP integrator Following table table 6 3 shows results for some combinations of the inputs Table 6 3 Results for QDM model From the results shown in the table it is quite obvious that the model has implemented the system according to the specification in the problem statements 6 6 Discussion on SimDynamic 6 6 1 Attributes of SimDynamic SimDynamic is entirely written in Java and the package appears as an applet 58 on an HTML page 59 Therefore it is available over the World Wide Web taking full advantage of the portability of Java and offers most of the advantages of a Web based simulation software Although Web version of SimDynamic lacks some features such as file saving due to security restriction imposed by Java or 3D animation it allows users full interactive capabilities and provides model creation with both numerical and graphical outputs Users may create modify and interact with a simulation model 136 with as ease as with a non Web based software It was mentioned in chapter that SimDynamic can also be used as a non Web based simulation software Appendix D provides full installation instructions and in that case it can be considered as a complete professional simulation package Fro
129. ode fails to find a direct mapping it performs linear interpolation extrapolation to find the output For example following tables are given on the parameter dialog box and the node receives 1 5 and 4 3 as row and column index respectively then output will be 48 861974 For 3 1 and 2 41 as row and column index respectively output will be 62 384233 82 Parameters e Row Table The row values for the table Default values are e Column Table The column values for the table Default values are e Table Data The table of output values Default table is 5 3 4 Polynomial This node evaluates the value of a polynomial of the following form at current simulation time pt anl apit Ea a t ao Polynomial coefficients can be entered on the parameter dialog box in descending order of power Parameters 83 e Coefficients Coefficients of the polynomial in decreasing order of power until the last coefficient which represents the constant for the polynomial Default values are 2345 5 3 5 Pre Look Up Index Search The pre look up index search node maintains a table sorted in ascending order and finds the index of its input in the table If it fails to find index directly from the table it performs linear interpolation extrapolation to find the index of its input For each input number the node outputs a index fraction pair where index is the index of the number in the table that is less then or e
130. omposed of non autonomous simulation nodes With an appropriate user interface the nodes appear as an integrated virtual environment to the user Simulation entities are modelled as simulation entity objects which are distributed at different nodes and are migrated dynamically between the nodes during the simulation run This mechanism of object management presents the possibility of reducing message volume Communication efficiency can therefore be improved by two measures One is to dynamically group closely related simulation entity objects to a specific node Alternatively a point to point communication approach can be used instead of broadcast Grant and Lai 9 present SMART a simulation modelling tool that provides a virtual reality interface for building graphical simulation models The simulation models comprised of nodes and arcs are constructed in three dimensions As the user builds a model he may immerse himself in it using virtual reality hardware and software tools and take advantage of the three dimensional environment provided by SMART It has been developed to explore the use of virtual reality in building simulation models and it serves as a prototype for testing the feasibility of creating a virtual reality simulation modeling software system on a relatively low cost personal computer SMART offers three dimensional interface using virtual reality hardware which includes an electronic glove and head mounted display The spec
131. onments As it is mentioned before SimDynamic is entirely written in Java which itself is a highly portable language since it translates to machine independent byte codes which are interpreted by a Java Virtual Machine JVM So to access the Web version of SimDynamci user will only need a Java compatible Web browser such as Windows Explorer or Netscape irrespective of the machine architecture The non Web based version of the package can also be used on multiple platform Consistency The Abstract Window Toolkit in Java aids the creation of graphical user interface in SimDynamic and allows the interface to be rendered according to the platform dependent information ensuring maximum graphical compatibility with the platform The interface is consistent also in that comparable operations are activated in the same way For example the buttons on various dialog boxes have a consistent look and feel and method of operation 138 e User Guidance The interface incorporates on line help for user guidance and assistance The help provides all the information needed to get along with SimDynamic such as how to build a model simulation parameters functional description of nodes etc 6 6 2 Extension Scopes The most adequate media for remote access is presently the World Wide Web Web based simulation is a means to exploit Web technology to support the future of computer simulation SimDynamic 1s an attempt to introduce dynamic system
132. ontrol systems The proposed software frame is independent of the hardware and operating system quite simple and aiso meets the requirements for a real time control environment such as monitoring supervisory control or scheduling etc Although such a frame is contained in the real time suite of the KEDDC system the contribution focuses to the implementation of the real time suite on PC based systems where the entire power of KEDDC is packed into one low cost and portable system which can be taken to the plant to be analysed and controlled CADACS PC supports a wide variety of control engineering methods It offers tools for the systematic design strategies and high accuracy numerical solutions In addition to modern analysis and design concepts classical methods are also implemented and may be used in combination with modern control techniques CADACS application environment consists of several components and the most important components consist of a group of interactive programs for the management of dynamic systems systems analysis controller synthesis simulation and signal processing A unified database for system models allows a smooth exchange of data between the different programs and between different groups of a development or research project Due to 17 the wide range of methods the system may be used for the task of industrial planning of plants as well as for the subsequent analysis of measured data also for the modeling
133. or for the BlankSheet blsh This class is responsible for keeping track of all the lines and nodes in a model Whenever a new line is drawn in a model its ID is set equal to JineID and lineID is incremented by one In the same way nodelD is used to keep record of the nodes in a model Vectors nodeV and lineV are used to store all the nodes and lines in a model respectively This class also maintains the simulation clock The method init initialises all the variables such as set ine D and nodelD to zero make sure that nodeV and lineV are empty at the beginning of a model construction The simulate method is the main simulation engine It carries out the whole simulation run Its function is described in section 4 5 The method removeLine int id removes a line from the vector ineV When user deletes a line int a model this method is invoked with the id of that line It removes the line from the vector and adjusts ids of the remaining lines The method removeNode int id removes a node from the vector nodeV As user deletes a node from a model this method is invoked with the id of that node It removes the node from the vector and adjusts ids of the remaining nodes 4 3 Anim3D This package contains all the classes responsible for 3D animation Although Anim3D has been included as a package of SimDynamic this can be used as a separate application to generate 3D animation 63 Patsstantannitaruonanmsiarnntanurtapnitereteo CT eer
134. ormula the Dormand Prince pair e RK _ O04 It is based on fourth order explicit Runge Kutta formula e RK _BS It uses an explicit Runge Kutta pair of Bogacki and Shampine It may be more efficient than RK_DP in the presence of mild stiffnes e RK _HE It uses Heun s method which also known as modified Euler formula e RK _ EU It uses simple Euler s method for integration 3 5 3 Step Length SimDynamic starts at the starting time and advances its clock by adding step length to the current clock time until it reaches the stop time At every time step it calculates the output of each node in the model Default time step is 0 2 So for a start time 0 and 46 stop time 10 0 outputs will be calculated at 0 0 2 0 4 10 0 Smaller time steps may ensure more accurate results but at the same time it may increase simulation run time depending on the model complexity and the integrator used Step length and sample time of the nodes in a model must be compatible When setting step length or sample time of nodes it should be made sure that sample time of all the nodes in a model must be an integer multiple of the step length 3 6 Runa Model and View Output Simulating a dynamic system with this package is a two step process First the user has to create a model of the system to be simulated The model graphically depicts the time dependent mathematical relationships among the inputs states and outputs of the system After th
135. os may be vector or matrix but poles must be a vector Width of the output from the node is determined by the dimension of the zeros If zeros is a vector then output will be a single number If zeros is a matrix then output will be a vector with width equal to the number of columns of the matrix The discrete zero pole node converts the given zeros and poles into polynomial form and then carries out the calculation like the discrete transfer function node Parameters e Zeros Values for the zeros Default is 1 e Poles Values for the poles Default is 1 2 79 e Gain Zeros will be multiplied by gain values Default is 1 e Sample Time The time interval at which output will be updated Default is 1 5 2 5 Discrete Filter The discrete filter node can be used to implement digital filters 54 like Infinite Impulse Response IIR and Finite Impulse Response FIR filters It actually evaluates a discrete transfer function expressed in terms unit delay operator z So the coefficients of numerator and denominator should be expressed in ascending zj powers of z Parameters e Numerator Numerator coefficients of the transfer function Default is 1 e Denominator Denominator coefficients of the transfer function Default values is 1 2 e Sample Time The time interval at which output will be updated Default is 1 5 2 6 Unit Delay The unit delay node delays and holds its input signal by one sampling interval M
136. ox Let 2 51 has been set as gain parameter and the node input is 4 31 7 then output will be 23 141 14 35i Parameters e Gain The multiplication factor Default is 1 86 5 4 5 Logical Operator The logical operator node performs one selected by user of the following logical operations on the input e AND e XOR e OR e NAND e NOT e NOR The node accepts both real and boolean values For real value input the node considers any non zero positive negative value as true and zero value as false Parameters e Output Type Selects the output type boolean real from the node Default is real e Logical Operator One of the six operators Default is AND 5 4 6 Magnitude Angle to Complex This node receives a magnitude and a phase angle in radian and outputs the corresponding complex number If the inputs to the node are r and then output will be x iy where x r cos andy r sin Parameters e None 5 4 7 Math Function 1 If u is the input to the node then this node can apply any selected by user of the following functions on u e Power 10 10 e Square u e Exponential e e Square root Vu e Natural log Inu e Conjugate u 87 10 base log logiou e Reciprocal 1 u e 2 base log logu e Factorial u e Magnitude square u Parameters e Function Selects one of the above functions Default is Power 10 5 4 8 Math Function 2 Let and r be the inputs to the node t
137. ps integrators 3D animation should be introduced in the Web based version of SimDynamic Model saving option should be enabled in the Web based version A database can be maintained to save models separately for each user Each user should be given unique ID to maintain his her account Unauthorised access or modification to the models developed using SimDyanmic should be protected by imposing password or time limit restriction on the package Despite few limitations in the Web based version SimDynamic encourages users of all levels of understanding to interactively develop models by providing a high level of user interactivity through simple interfaces Additionally it enables the users to understand the system under consideration through graphical as well as numerical results of the simulation Because of wide range modelling ability and tremendous flexibility SimDynamic may be a rendezvous for the simulation practitioners on the Web 142 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 References Andrew S Tanenbaum 1996 Computer Networks Prentice Hall Inc USA Sun Microsystems Inc 1995 2002 The Source For Java Technology http java sun com Martin Barnes Virtual Reality and Simulation Proceedings of the 1996 Winter Simulation Conference pp 101 110 1996 Quest Software Inc 1985 2002 http www q
138. put value at previous time step At the start of the simulation output from the node will be the initial value provided on the parameter dialog box For example if initial value is 3 increment length is 0 5 and step size is 0 2 then output from this node for first few steps will be as follows Senon Tine 00 02 oe Parameters e Initial Value Starting value for the counter Default is 1 e Increment Length Counter value will be increased by this value at every simulation time step Default is 1 5 6 2 Cumulative Function This node performs one of the following cumulative functions e Cumulative Sum At any simulation step the node will calculate its output using the following formula output gt input 1 0 where n is the total number of simulation steps since simulation started including the current step e Cumulative Average At any simulation step node output will be according to the following formula 3 input output n where n is the total number of simulation steps since simulation started including the current step 97 e Cumulative RMS At any simulation time step node output will be according to the following formula where n is the total number of simulation steps since simulation started including the current step Parameters e None 5 6 3 Data Type Conversion The data type conversion node provides the option to convert boolean type signal to numerical type signa
139. qual to the input and fraction is the normalized position of the input between the index and the next index For example let following table has been entered on the parameter dialog box ae If the node input is 9 37 output will be 2 0 8425 Parameters e Table Data The numbers to search Default values 3 5 6 10 12 5 3 6 Interpolation 2D Using Pre Look Up The interpolation node holds possible output values in a 2D table and it accepts two input row and column pair index fraction pair from pre look up index search It then tries to find the output value by using received row and column indices If it fails to find a direct mapping of the indices it performs linear interpolation For example let following table is the given output table on the parameter dialog box baal A sof ENENGE If the node receives 0 0 75 and 0 0 45 as row and column index respectively from a pre look up index search node then output will be 27 25 84 Parameters e Table Data The table of possible output values Default table is 5 4 Maths and Logic Node Set 5 4 1 Absolute This node finds the absolute value of its input If input is real value x then output will be x And if input is a complex number xtiy then output will be 4 x y Parameters e None 5 4 2 Bit wise Logical Operator This node can be used to perform bit wise logical operation on any signal The node applies one selected by
140. r division Default is multiplication 5 4 13 Real Imaginary to Complex This node accepts two real values as input signal and outputs a complex number treating the input values as real and imaginary part of the complex number Parameters e None 89 5 4 14 Relational Operator This node can be used to compare two signals It accepts two inputs and performs one selected by user of the following relational operation on them e Equal to e Less than equal to lt e Not equal to e Greater than gt e Less than lt e Greater than equal to gt The output type from the node may be boolean or real specified by user If the result is true the output is for real type output true for boolean type output if false it is 0 for real type output fa se for boolean type output Parameters e Output Data Type Selects the data type boolean real of the output from the node Default is real e Relational Operator One of the six operators Default is Equal to 5 4 15 Resolve Complex The resolve complex node can be used to find the magnitude or phase angle or real part or imaginary part of a complex signal Parameters e Function Selects one of the four magnitude angle real imaginary outputs Default is magnitude 5 4 16 Rounding Function It performs one selected by user of the following rounding operation on its input e Round x finds the nearest integer of x e Floor x fi
141. rbance from the road us the control force When the bus is experiencing any road disturbance the distance x1 w will be very difficult to measure and the deformation of the tire x2 w is negligible So the distance xl x2 will be output from the model Newton s law for each of these masses can be expressed as YF m 6 8 i dt 128 6 9 2 F F m dt There are three forces acting on m one damper one spring and control force u and five forces are acting on m2 two dampers two springs and control force u The force from spring 1 is equal to k x x2 and force from damper 1 is equal to b1 vl v2 where v dxI dt and v2 dx2 dt Force from spring 2 is equal to k2 w x2 and force from damper 2 is equal to b2 v2 dw dt So F u k1 xl x2 b1 vl v2 6 10 l EF Hl x1 x2 2 w 2 B1 01 92 62 2 v2 u 6 11 s t 6 4 2 Building the Bus Suspension Model Using equations 6 8 6 11 following model fig 6 18 was built The road disturbance w and control force u are simulated using two step nodes labelled as W and U respectively Fig 6 18 Bus suspension model open loop in SimDynamic 129 The output from Sum3 node is x7 x2 which is multiplied by k Gain node labelled as spring Output from Sum4 node is v v2 which is multiplied by b Gain node labelled as damper1 Sum and Sum2 nodes are used to sum up all
142. real onl Direct feedthrough K e e zeros have same number Input single number Zeros single number vector matrix Poles single number vector Dimension of input parameter Discrete Node Set If numerator and denominator have same number of columns Expansion of input parameter Input single number Numerator single vector matrix Denominator single matrix Discrete Integrator Numerical real only lanes ifecdtinateh If reset and or external initial condition are enabled Dimension of input parameter Expansion of input parameter Supported Dimension of input parameter Single number vector matrix Discrete State Space Numerical real only If D is non zero matrix iscrete Expansion of input parameter xpands only the initial condition Input single number Parameters Dimension of input parameter single number vector matrix A 3 same number of columns Expansion of input parameter Input single number Numerator single vector matrix Denominator single vector Dimension of input parameter Discrete Zero Pole Data type supported Numerical real only Direct feedthrough If poles and zeros have same number of columns Expansion of input parameter Not supported Input single number Zeros single number vector matrix Poles single number vector Dimension of input param
143. reer nan coonaencgPaagnscounaccagnaasngy H pi gt j Component i Behavior SSS SSS Mrsreruvrorstnsesunsnpienuyonanttmstnta nay SphereBehavior Fig 4 4 Class hierarchy in Anim3D package 4 3 1 MainFrame3D This class provides the 3D model editor user interface public class MainFrame3D extends JFrame implements ActionListener public MainFrame3D public void actionPerformed ActionEvent ae Constructor MainFrame3D initialises all the dialog boxes and buttons actionPerformed method handles all the button action and invokes appropriate dialog box for input 4 3 2 Panel3D Panel3D class constructs the 3D scene and adds the scene into a Jpanel Its definition is as follows public class Panel3D extends JPanel public BranchGroup createSceneGraph Vector nodeV Unano Und ImniO public Panel3D Vector nodeV UnilInfo uniinfo 64 Method createSceneGraph creates the whole 3D scene Vector nodeV contains parameters for every single object of the 3D model This method goes through the nodeV vector and creates all the 3D objects and adds them to the java 3D BranchGroup 46 Constructor Panel3D creates a SimpleUniverse 46 and adds the BranchGroup created by createSceneGraph method to it Finally this method adds the SimpleUniverse to the centre of the Jpanel 4 3 3 Behavior Classes All the behavior classes in the package like BoxBehavior or CylinderBehavior are responsible for the mov
144. ro gla ac uk UGrad home_pages nmae nmae rkutta rkutta htm Pair of Runge Kutta formulas Bogacki P Shampine LF A 3 2 Appl Math Lett 1989 2 1 9 William H Press Brian P Flannery Saul A Teukolsky and William T Vetterling 1987 Numerical Recipes The Art of Scientific Computing Cambridge University Press USA http www maths ugq edu au gac math3201 math3201 htm D K Anand and R B Zmood 1995 Introduction to Control Systems Butterworth Heinemann Ltd UK Francis Scheid 1988 Schaum s Outline of Theory and Problems of Numerical Analysis McGraw Hill Companies Inc USA Phillip A Regalia 1995 Adaptive IIR Filtering in Signal Processing and Control Marcel Dekker Inc USA Bernard Friedland 1996 Advanced Control System Design Prentice Hall Inc USA Martin Hargeaves 1996 Engineering Systems Modelling and Control Addison Wesley Longman Ltd England Jerry Banks John S Carson Barry L Nelson 1999 Discrete Event System Simulation Prentice Hall Inc USA James Gosling Frank Yellin The Java Team 1996 The Java Application Programming Interface Volume 2 Window Toolkit and Applets Addison Wesley Publishing Company USA http student dcu ie mahbubk2 simdynamic html The MathWorks Inc 1994 2002 http www mathworks com products simulink 146 Al Appendix A Node Properties Continuous Node Set erivative um
145. s Static models describe a system mathematically in terms of equations where the potential effect of each alternative is ascertained by a single computation of the equation Static models ignore time based variances So they represent a system at a particular point of time or a system in which time simply plays no role Also this type of model does not take into account the synergy of the components of a system where the actions of separate elements can have a different effect on the total system than the sum of their individual effects would indicate On the other hand a dynamic simulation model represents a system as it evolves over time It is a representation of the dynamic or time based behaviour of a system While a static model involves a single computation of an equation dynamic modelling is iterative A dynamic model constantly re computes its equations as time changes Dynamic modelling can predict the outcomes of possible courses of action and can account for the effects of variances or randomness One cannot control the occurrence of random events but can use dynamic modelling to predict the likelihood and consequences of their occurring b Deterministic vs Stochastic Simulation Models A deterministic model can be identified as a model that does not contain any probabilistic i e random components In such models output is determined once the set of input quantities and relationships in the model have been spec
146. s The paper concludes with some examples to demonstrate the architecture Guru et al 41 introduces a web based interface for storing and executing simulation models developed using SIMAN simulation language over the Internet This toolkit consists of a World Wide Web interface to SIMAN and a web accessible database for storing the models To use the system a user simply needs access to the Internet and an account on the web server It allows user to develop execute and test the functionality of a model through the use of the SIMAN debugger run controller The proposed system involves three servers a Application server An Internet user accessing the Web address for the site is directed to this server The software residing here performs the functions of authenticating the user and providing access to user s models in the database It gives the user the capability to edit add or delete models in his her account When user wants to compile execute or debug a model this server creates an appropriate HTML document dynamically generated which contains the 30 instruction of the desired operation and pass it to the SIMAN server on user s request Firewall Fig 2 8 Hardware and software layout b Oracle server The application server interacts with the Oracle server in order to provide accessibility to the user s models The models and the user data are stored in a database c SIMAN server This server holds t
147. s access to many platforms without recompiling e allows access to distant sites without transporting through hardware or software Efficient Maintenance e enables frequent modifications to be made and instantly distributed e allows modifications and implementations made through the server SimDynamic can also be used as a non Web based simulation software Some of the salient features of SimDynamic are summarised here e Functionality The current implementation contains 83 functional nodes that will enable user to model a wide range of real world dynamic systems including mechanical electrical and thermodynamic systems e Expandability The package can be extended easily by implementing additional functional nodes and incorporating them to the main simulation engine e Portability Both the Web based and no Web based version of SimDynamic can be used on various platforms 141 A2 User Guidance SimDynamic user interface provides on line help for user guidance and assistance Reliability Because of the use of authenticated and proved algorithms in SimDynamic development it produces desirable results Suggested Future work Computing power of SimDynamic can be enhanced by incorporating more functional nodes to handle advanced and various types of simulation models particularly discrete event simulation models Better accuracy especially for stiff differential equations can be achieved by implementing variable ste
148. s displays the information about SimDynamic DispFrame DispFrame provides the framework to display numerical results of a simulation The class SnkDspl from NodePack package supplies the numerical results to this class after a simulation run is completed TvsXFrame this class is responsible for graphical output of a simulation It plots the graph of a simulation result with respect to time The class SnkTvsinp from NodePack package provides necessary data to this class XvsYFrame works in the same way as 7vsXFrame but it derives its data from the class SnkXYGrph of NodePack package 4 2 NodePack NodePack is the core of this software and provides all the functional nodes This package also contains the main simulation engine the Simulator class and some other important classes Prams AOU ORe ener TSR Os todd peneseus res smmas ems ameny JComponent Narratsshrtenenneeeent gnersenebateceresehensnne Integrator Fig 4 3 Class hierarchy in NodePack package 54 4 2 1 Basic Basic class can be considered as the utility class for SimDynamic This class contains various utility methods to ease the task of other classes Basic extends Object public public publ ise p blig pupric publre Public Public public public pub ase public public p blic public public public public publ ie publsc Public public public public public public equaiTo double
149. s from four equations is used as the final output from the system The main challenge was to find the coefficients a b c for each input to develop the above system A close observation showed that c has the greatest affect on the equation b has linear affect and a has the least affect on the equation With lower c values equation output tends to rise and vice versa Coefficient values were chosen in such a way that all the individual equations produce the minimum satisfactory 0 5 output so that the final output from the system is satisfactory 0 5 when all the inputs are at their respective threshold values From these observations and assumptions following coefficient values are set for each equation after numerous trial and error run e Input 1 P a 1 b 1 c 1 1 e Input 2 IP2 a 1 b 1 c 1 2 e Input 3 IP a 1 b 1 c 0 8 e Input 4 IPy a 1 b 1 c 0 6 Following QDM model is built using all above equations Fig 6 19 QDM model in SimDynamic 135 Equation for each input is implemented using similar steps as in section 6 1 For each input an additional switch node and a saturation node are used in series Switch node will force a zero input if the input is below the threshold value and the saturation node is used to limit the input value within the valid input range i e 0 0 1 0 Outputs from all for equations are summed and then multiplied by 0 25 to get the arithmetic average using a gain n
150. shape click on the appropriate button Each button click will open a dialog box for the selected shape Here parameter dialog box for a box is shown for example B x Fig 3 19 Dialog box for 3D box On the dialog box user has to define the initial position center X Center Y Center Z colour in term of RGB value and dimension of the shape All the dimensions in the 3D model editor are measured in meter More importantly to animate any 3D object at least one data simulation result from SimDynamic file should be associated with it 3D animator will read current position of the object from the associated data file s and render the 3D animation Parameter button enables the user to set some 3D parameters User can set the origin of the 3D space and can also set the time delay during 3D rendering Save button can be used to save a 3D model Using the Joad an existing 3D model can be opened To start 3D animation click the run button and a new 3D window will be opened 48 Chapter Four 4 The Design of SimDynamic SimDynamic is a collection of Java classes objects which provides basic utilities for constructing simulation models for dynamic systems In the current implementation simulation random number generation and analysis of simulation result both numerical and graphical are built into the library In the following sections the design of the Simdynamic package will be presented in great detail SimDynamic is inten
151. signal defined by the user The node has two parameters time points and output values User can define the shape of the signal by specifying these parameters on the dialog box For example default values for time points and output values are 0 2 and 0 2 respectively This means the node will repeat a signal every 2 seconds from the start of the simulation time that has maximum amplitude of 2 The node uses linear interpolation to calculate the value of the signal between the specified time points So for these parameter values and simulation step size 0 2 output from the node for first 2 2 seconds will be Sinise 0 02 oa os oa io v2 ia vs as oT az Again if the time points and output values are 1 2 3 and 5 8 13 respectively and simulation step size is 0 2 then output will be Sinan ie 0 0 oa o 58 or Parameters e Time Values Time values that determine the period of the generated signal Default is 0 2 e Output Values Values that determine the shape amplitude of the generated signal Default is 0 2 110 5 8 13 Signal Generator This can produce one of the three different waveforms sine wave square wave and sawtooth wave User has to specified amplitude and frequency on the parameter dialog box for the signal Parameters e Wave Form Selects the wave form sine square sawtooth Default is sine wave e Amplitude Signal amplitude Default is 1 e Frequency Frequency of the gener
152. status bar double click on the Time vs Input node A new window like figure 6 4 will show the simulation result This window will show graphical result of the simulation In this graph simulation time is shown along x axis and the output is shown along y axis To view the numerical result add a Display node from the Sink node set Draw a branch line from the output line of the x1 ntegrator and connect it to the input of the Display node Run the simulation again When simulation is completed double click on the Display node Another window like figure 6 5 will show the numerical result of the simulation 116 SimDynamic Time Ys X Fig 6 4 Graphical result for ODE model Display Time 1 2 0 098713 Time 1 4 0 342308 Time 1 6 0 618678 Time 1 8 0 813158 Time 2 0 855053 Time 2 2 0 802978 n M Fig 6 5 Numerical result for ODE mode KE 6 2 Simple Damped Pendulum Consider the simple damped pendulum shown in figure 6 6 It is assumed that the mass is m kg length of the weightless rod is L metre and there is a frictional torque at the pivot point that is related to the angular velocity through the coefficient c N m s rad ax wee wee ee ar ee ee ee ee ee e we oa mg Fig 6 6 A simple pendulum The equation of motion of this damped pendulum is 6 4 6 2 1 Building the Pendulum Model From equation 6 4 following figure 6 7 model is buil
153. sually available twenty four hours a day access to the simulation software is not limited by time constraints So the users can work within their own time schedule Controlled Access With non Web based package user can access the model and software directly from the computer on which the model is running There is every possibility of inadvertent modification to the model whenever the user has free access to the complete source code of the model and the software On the other hand Web based simulation software and models can be protected from unauthorized modifications by imposing password and time limit restrictions on either the simulation package or the entire site User Friendliness Most of the non Web based packages need proper installation of the software and working knowledge of the operating commands Web based simulation software and models developed with Java require only a Java compatible Web browser for viewing Such browsers like Internet Explorer or Netscape are readily available easy to install and already installed on many computer systems Since many users are familiar with navigating Web browsers the total software learning curve using a model is a minimal Furthermore web based simulation environment provides the same user interface for all users Although some non Web based groupware computer based systems that support groups of people engaged in a common task simulation packages provide a common int
154. t and process oriented discrete event simulation SimPack supports simulation development for a wide variety of modeling types including the following e Declarative Models An emphasis on explicit state to state changes as found in finite state automata and Markov models 18 ye es w DUA a SE i i Aa t e Functional Madels A focus on function OF procedure as in queuing networks block models sulse processes and oaie Petri nets gt Multimodels Defined as conglomerates of other models connected i in a graph or network to solve combined Taiioh problems at multiple abstraction levels Constraint Models The constraint part of SimPack includes capabilities for modeling 1 difference equation acc 2 differential equation systems and 3 delay differential equation systems In most cases the most uniform method of simulation is to convert the equation s into first order form and then to simulate the system by updating the state vector Otter 16 presents DSSIM a general purpose simulation for dynamic systems DSSIM is a part of ANDECS 17 environment which is a powerful and flexible software package for the analysis and design of controlled dynamic systems ANDECS consists of a wide collection of methods such as basic mathematical methods like matrix computation interpolation of signals or root finding of nonlinear functions Analysis and design methods for linear dynamic systems like linear simulat
155. t from the node Default is real e Crossing Offset The value that the input will cross Default is 0 e Crossing Direction One of the above crossing directions Default is Either 5 6 5 Initial Condition The initial condition node sets an initial value for a signal At the simulation start time the output from the node will be the initial value given on the parameter dialog box after then the node outputs its input without any alteration Parameters e Initial Value Output from the node at the first simulation time step Default is l 5 6 6 Matrix Concatenation The matrix concatenation node can be used to concatenate two matrices Concatenation can be accomplished in two ways i horizontally or ii vertically For example let following two matrices are inputs to the node 99 3 12 61 5 66 31 7 32 6 91 If the matrices are concatenated horizontally then the output from the node will be 2 4 2 i l 3 2i 5 T3 l 2i 9 05 3 12 61 12 5 66 31 Zl 7 32 6 91 If the matrices are concatenated vertically then output will be Z 4 3 2i 7 3 l 9 05 3 12 5 66 2l k32 Parameters 2 i 5 2i 12 61 31 6 9i e Concatenation Method Selects one of the two concatenation methods Default is Horizontal concatenation 5 6 7 Merge The merge node superimposes two signals on a single signal An initial output and the offsets for the input signals on this output should be defined on the para
156. t is 1 e Switch off Point Threshold for switch off Default is 1 e Output when on Output from the node when the relay is on Default is 1 e Output when off Output from the node when the relay is off Default is 0 5 5 9 Saturation The saturation node can be used to restrict a signal within a specified rang User has to define upper limit and lower limit on the parameter dialog box If the input signal to the node is within the range specified by the lower limit and upper limit the input signal passes through unchanged If input is greater than the upper limit output from the node will be the upper limit If input is less than the lower limit output will be the lower limit Parameters e Upper Limit Upper bound of the range Default is 1 e Lower Limit Lower bound of the range Default is 1 5 5 10 Switch The switch node has two inputs and a control input User has to specify a threshold value on the parameter dialog box If the control input is greater than or equal to the threshold value then output will be equal to the first input otherwise output will be equal to the second input Parameters e Threshold Holds the threshold value for control input Default is 0 96 5 6 Miscellaneous Node Set 5 6 1 Counter This node provides a simple counter An initial value and increment length should be specified on the parameter dialog box The node will generate output by adding the increment length with its out
157. t of another node and release the mouse figure 3 12 SimDynamic will complete the line by connecting it with the targeted node figure 3 13 3 4 2 Manipulating Lines A line can be deleted its colour can be changed or a branch line can be drawn from an existing line by using line menu To activate the line menu position the mouse tip on the line and click right mouse button Line menu will appear figure 3 14 Now choose appropriate menu item to manipulate line Fig 3 14 Line menu To delete the line choose delete from the menu SimDynamic will delete the line and will reset the input output port of the node to which the line was connected To change the colour of the line select color from the menu SimDynamic will open a colour chooser select a new colour and click ok button The line will be redrawn in the new colour 44 a f integrator Fig 3 15 Branch line drawing step Fig 3 16 Branch line drawing step 2 To draw a branch line select branch from the line menu A point will appear on the line Position the mouse cursor near the point mouse cursor will be turned into a cross hair cursor figure 3 15 Now press the left mouse button and drag the mouse to draw branch line figure 3 16 To finish the branch line follow the same procedures as with normal lines 3 5 Simulation Parameters Before starting a simulation run user has to set simulation parameters To access the sim
158. t reaches the stop time At each step Simulator computes a node s states and outputs by invoking the action method of that node It passes the current simulation time and step length to the node s action method The node itself derives the input for this simulation time step from its previous node Using this input simulation time step and the node s state if any at previous simulation time step action method computes the valid output and state for current simulation step After the action method Simulator invokes the node s isOpOk method to ensure the validity of currently calculated output At the end of each step Simulator computes the simulation time for the next step and advances its clock to that time At the end of the simulation time each node holds the final values of the model s output and state 71 Chapter Five 5 Functional Description of the Nodes This chapter provides a brief functional description of each node in SimDynamic Each description also mentiones all the parameters for the node that the user can set on the node parameter dialog box Nodes are discussed here according to the node set library For characteristic properties of nodes see appendix A These nodes have been selected carefully so that the package can be used to model a wide range of complex systems In some cases ideas have been borrowed from existing non Web based packages like VisSim and Simulink 5 1 Continuous Node Set 5 1 1 Derivative
159. t using SimDynamic In this model input to the right most ntegrator node renamed as theta is d6 dt and output from this node green signal line is 8 on which Trigonometric Function node is applied to have sin Two Product nodes labelled as g L and g L sin respectively are used to perform the multiplication g L sin 0 Similarly the left most Integrator node renamed as d 0 dt outputs red signal line d6 dt and this is multiplied by c mL using three Product nodes Four Constant nodes are used to supply the values for mass labelled as Mass length labelled as Length torque labelled as torque and acceleration due to gravity labelled as g These four nodes in this model enable 118 the user to change respective parameters and immediately see what happens for what if exploration Fig 6 7 Pendulum model in SimDynamic To view the graphical output from the simulation a Time vs Input node is used and input to this node is 8 So the angular displacement of the pendulum will be plotted against the simulation time Rest of the nodes in the model are used to calculate the position x and y co ordinate of the bob at each simulation time step These values are used later for 3D rendering Two To Dat File nodes labelled as xcor and ycor are used to save the x co ordinate and y co ordinate values respectively in data files that will be used for the 3D rendering 6 2 2 Running Simulation and Viewing Resu
160. table 6 2 Fig 6 9 Graphical result for pendulum model rod length 0 5 meter simulation time along x axis 0 along y axis Table 6 2 Numerical result for pendulum model rod length 0 5 meter 1 04719 0 91565 0 23019 0 55600 0 91581 0 67531 0 01451 0 60502 0 77629 0 43198 0 17691 0 61732 0 61236 0 19949 0 31889 0 57817 0 42820 0 00256 0 3983 0 48989 0 24031 0 15672 0 41391 0 36628 0 06929 0 25325 0 37452 0 22725 0 06721 0 29185 0 29584 121 0 09345 0 15891 0 28040 0 19679 0 01823 0 20386 0 23228 0 09585 0 09746 0 20733 0 16330 0 00794 0 14103 0 17935 0 08872 0 05739 0 15182 0 13218 0 02104 0 09637 6 2 3 3D Animation for Pendulum Model This section describes all the steps needed to build a 3D pendulum model using SimDynamic This will provide a general idea of how to use SimDynamic to construct a 3D model These 3D model building steps have been omitted for other examples described later To start the 3D model editor select 3D Animation from simulation menu In the pendulum model three objects will be used A small box will be used as the hook of the pendulum a cylinder will be used as the rod and a sphere will be used as the bob Step 1 Click the box button On the box parameter dialog box set box width height and depth to 0 04 each Since the box will remain static during 3D rendering no data files will be added to box Leave other fields wit
161. taining the statistical results derived from the engine s efforts and organizes these results into a form that meets the needs of the customer Once all the jobs associated with the customer request are complete an email message is sent telling him that the results are ready for pick up Rao et al 43 present WESE A Web Based Environment for Systems Engineering The design and development of modern systems is complicated by their size and complexity Furthermore many of today s complex systems are built using subsystems and components available from third party manufacturers Using WESE a developer can analyse different design alternatives involving components from various researchers and manufacturers The environment provides a flexible framework to develop formal specifications and verify the designs using mechanized theorem proving WESE also provides the techniques to generate different specifications documents such as cost of the system The primary input to WESE is a System Specification Language SSL description of the system The input SSL source is parsed into an intermediate form SSL IF using a parser SSL IF forms the input to other modules of WESE SLL IF is used to construct simulations generate specifications and documentation from the distributed factories The factories play a pivotal role in providing a uniform interface to generate the various components specified in the SSL specifications The various subsystems o
162. tching figure 2 2 and yawing figure 2 3 Pitching is equivalent to nodding the head up and down and yawing is swinging the head left and right To use the I Glasses to build simulation models the user simply needs to put on the VIO I Glasses and look around the way he usually does in the real world As the orientation of the user changes the virtual world is rendered accordingly 15 2 2 Dynamic System Simulation Kimbrough 12 provides APL algorithms for control system development and demonstrates their use by solving a typical control problem The paper outlines useful numerical techniques for simulating dynamic systems and for solving some of the central equations of the control theory APL functions are presented to check observability controllability and stabilizability of a linear dynamic system expressed in the following standard form x Bu D x u t v dt y Cx where the shapes of A B C D x y u and v are n n n p n q m n n m p and q respectively The vector x is the state vector the vector y is the measurement vector the vector u is the linear control and the variable v is the non linear control The matrices A B and C are time invariant but D is allowed to vary with x u and tf the time Simulation of dynamic systems is typically conducted by using a numerical integration routine A fourth order Runge Kutta integration routine is also provided in the paper This is a fixed step
163. telepresence and VR systems gives the Mixed Reality or Seamless Simulation systems Here the computer generated inputs are merged with telepresence inputs and or the user s view of the real world For example a fighter pilot sees computer generated maps and data displays inside his helmet visor or on cockpit displays 1 2 Simulation A system is defined to be a collection of entities e g people or machines that act and interact together toward the accomplishment of some logical end A model is a simplified representation of a system at some specific point in time and or space intended to promote understanding of the real system Simulation is the discipline of designing a model of an actual or theoretical physical system and manipulating the model in such a way that it operates on time or space to compress it thus enabling one to perceive the interactions that would not otherwise be apparent because of their separation in time or space It is a discipline for developing a level of understanding of the interaction of the parts of a system and of the system as a whole And this level of understanding is seldom achievable via any other discipline Simulations are generally iterative in their development One develops a model simulates it learns from the simulation revises the model and continues the iterations until an adequate level of understanding is developed 1 2 1 Types of Simulation Models a Static vs Dynamic Simulation Model
164. three forces act upon mass The resultant force output from Sum2 node is multiplied I m1 Gain node labelled as Mass1 and fed to an ntegrator node labelled as V1 The output from this ntegrator node V1 is fed to a second ntegrator node labelled as X1 Similar description can be made for the second mass spring system A Time vs Input node is used to view the graphical output from the simulation This model will only provide open loop response from the system To construct a closed loop model figure 6 19 feedback controller was designed feeding back the following five states see appendix C for derivation x dxI dt yl xl x2 dyl dt yl at ew f Fig 6 19 Bus suspension model closed loop in SimDynamic The controller used the following gain matrix 2300000 0 500000000 0 8000000 An Integrator node is used to find fy and four Mux nodes are used to combine all signals in a vector form A Dot Product node labelled as controller is used as the feedback controller 130 6 4 3 Running Simulation and Viewing Result Simulation was carried out for 50 seconds for the open loop model using RK_DP integrator and step size 0 01 Following parameter values were use e Massil ml 2500 kg e Mass2 m2 320 kg e Spring constant k 80000 N m e Spring2 constant k2 500000 N m e Damper constant bi 350 Ns m e Damper constant b2 15020 Ns m The open loop response from the bus suspension model is s
165. time internally When simulation time exceeds time delay parameter the node finds the output at the time that corresponds to the current simulation time minus the delay time If the time delay parameter is not a multiple of the step size then the node performs linear interpolation or extrapolation on the stored time and input value pairs to calculate the output Following example will clarify this Let simulation step size be 0 2 and the node receives following inputs from a clock node Sinton tine 00 02 0a oe om tiara on or oa 06 fos and initial output parameter is set to 5 Following table shows the output from the node when time delay parameter is set to 0 05 and 0 45 Sn 00 por oF 06 om Output with time delay 0 05 5 0 0 0 0 35 0 55 0 75 Parameters e Initial Output The node will continue to output this value before simulation time exceeds time delay parameter Default is 0 e Time Delay Input signal will be delayed this amount of time Default value is l 75 5 1 7 Variable Transport Delay The variable transport delay node can be used to delay its input by a variable amount of time It works the same way as the transport delay node Only difference is that this node accepts the time delay parameter as an input Parameters e Initial Output The node will continue to output this value before delayed output is generated 5 1 8 Zero Pole The zero pole node finds the
166. ts a signal and plots the signal against the simulation time Simulation time is shown along x axis and input signal is shown along y axis If the sample time of the received signal is continuous it produces a point to point plot If the sample time is discrete then it produces a stair step plot If the width of the received signal 1s more than one vector or matrix signal it uses different colours in this order green yellow red orange cyan and magenta 104 Parameters e None 5 7 4 To File The To File node accepts all types numerical boolean data as input and saves the value of its input in a text file User has to provide a file name on the parameter dialog box Any text editor like Notepad or MS Word can be used to view the saved file Parameters e File Name Name of the file to be saved Default is None 5 7 5 XY Graph The XY graph node accepts two signals and plots the first signal against the second signal First input is shown along the y axis and second input is shown along the x axis Parameters e None 5 7 6 To Data File The To data file node can be used to save simulation result for later use by SimDynamic This node accepts all types numeric boolean data as input and saves the value of its input in a data file in a format that can only be used or opened by SimDynamic The most important use of this node is for 3D rendering SimDynamic 3D image generator reads data from files so this
167. ublic Public static Static static static static static static static static static static static static static static static static static static static static static static static static static static static static Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex mult Complex Cl Complex C2 div Complex Cl Complex C2 pow Complex Cl Complex C2 powl0 Complex Cl exp Complex Cl ln Complex Cl log10 Complex C1 log2 Complex Cl sqr Complex Cl sqrt Complex Cl conjugate Complex Cl inverse Complex Cl reciprocal Complex Cl double magnitude2 Complex Cl double abs Complex Cl double angle Complex Cl Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex sin Complex Cl cos Complex Cl tan Complex Cl asin Complex Cl acos Complex Cl atan Complex Cl Sinh Complex Cl cosh Complex Cl tanh Complex Cl asinh Complex Cl acosh Complex Cl atanh Complex Cl ma2cmplx double dl double d2 All the methods in this class have self explanatory names So functional description of the methods has been skipped here 58 4 2 3 Matrix Matrix class contains all the methods required for matrix operations Its definition is as follows public class Matrix extends Object public static Vector inverse
168. uest com Delmia Corp http www delmia com Karen C Jones Marc W Cygnus Richard L Storch Kenneth D Farnsworth Virtual Reality for Manufacturing Simulation Proceedings of the 1993 Winter Simulation Conference pp 882 887 1993 AutoSimulations Inc http www prolog italia com Autosimulations htm Robert Macredie Simon J E Taylor Xiaoning Yu and Richard Keeble Virtual Reality and Simulation An Overview Proceedings of the 1996 Winter Simulation Conference pp 669 674 1996 Hank Grant and Chuen Ki Lai Simulation Modelling with Artificial Reality Technology SMART An Integration of Virtual Reality and Simulation Modelling Proceedings of the 1998 Winter Simulation Conference pp 437 441 1998 Fifth Dimension Technologies 1996 SDT Glove User s Manual Pretoria Virtual I O 1995 VIO I Glasses user s Manual Seattle Scott Kimbrough Control System Development Tools ACM SIGAPL APL Quote Quad Proceedings of the international conference on APL APL in transition January 1987 Volume 17 Issue 05 10 1987 4 Schmid Chr Real Time Control with CADACS PC In M Jamshidi and C J Herget Ed Recent Advances in Computer Aided Control Systems Engineering North Holland Amsterdam 1992 S 337 355 Schmid Chr KEDDC A Computer Aided Analysis and Design Package for Control Systems In M Jamshidi und C J Herget Ed Advances in Computer Aided Control Systems Engineering
169. ulation parameters select parameter menu item from Simulation menu Following parameter dialog box will appear Fig 3 17 Simulation parameters dialog box 45 3 5 1 Simulation time Simulation time specifies how long the simulation will continue This time is measured in seconds And of course it is in pseudo time Starting time need not be always 0 Simulation may start from any starting time The only requirement is that stop time must be greater than the start time It should be noted that simulation time and physical time are not the same For example running a simulation for 10 seconds will usually not take 10 seconds The amount of time it takes to run a simulation depends on many factors including the model s complexity the simulation step sizes and the computer s clock speed available memory etc 3 5 2 Integrator The dynamic models are generally described by differential equations The solutions of these equations display the operations of the system and this is the fundamental property of dynamic models This software provides a number of integrators to solve ordinary differential equations ODE Because of the diversity of dynamic system behavior some integrators may be more efficient than others at solving a particular problem Here is a brief description of each solver that has been implemented Greater details about integrators can be found in chapter 4 e RK _ DP It is based on an explicit Runge Kutta f
170. ustomize the dialog box initialised by initDialog method The method cancel abstract method is used to perform the cancel operation of the parameter dialog box If user clicks the cancel button in the parameter dialog box this method restores the previous parameter values Abstract method checkConsistency is used to handle the ok action of the parameter dialog box If user changes the parameter and clicks ok button in the parameter dialog box this method will parse the new parameters It also generates error messages if user enters invalid parameter action double simT is the most important method in the class It is mentioned in section 3 1 that every node in SimDynamic carries out a specific function such as integration generation of sine wave etc at each simulation time step All the nodes implement this action method to perform its specific function at simulation time simT 61 4 2 5 Integrator The dynamic models are generally described by differential equations The solutions of these equations display the operations of the system and this is the fundamental property of dynamic models This class provides a number of numerical methods to solve ordinary differential equations ODE Because of the diversity of dynamic system behavior some methods may be more efficient than others at solving a particular problem public class Integrator extends Object public Integrator public Vector integrate Vector A Vector X Ve
171. ut from the node when simulation time reaches the step time Default is 1 e Sample Time The time interval at which output will be updated Default is 0 5 8 16 Uniform Random Number This node generates uniformly distributed random variates Minimum value maximum value of the distribution and an initial seed should be specified on the parameter dialog box This node uses java s built in random number generator to generate a random numbers R using the initial seed If a and B are the minimum and maximum values respectively then uniform variate X can be found from following formula 57 X at Bp R Parameters e Minimum Minimum value of the distribution Default is 1 e Maximum Maximum of the distribution Default is 1 e Initial Seed Starting seed for the random number generator Default is 0 e Sample Time The time interval at which output will be updated Default is 1 112 Chapter Six 6 Application of SimDynamic SimDynamic can be used to simulate the behaviour of a wide range of real world systems including electrical mechanical and thermodynamic systems In this chapter a few examples are presented Since most dynamic systems are expressed in terms of differential equations the chapter starts with an example of how to solve a differential equation of second order using SimDynamic More complex and real world systems are presented in the subsequent sections First example provides all the steps ne
172. va _ Based Simulation for the World Wide Web Computer amp Operation Research 26 1999 pp 607 621 144 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Elmaghraby A S Elfayoumy S A Karachiwala I S Graham J H Emam A Z Sleem A M Web Based Performance Visualization of Distributed Discrete Event Simulation Winter Simulation Conference Phoenix Arizona pp 1618 1623 1999 Salisbury C F Farr D S Moore J A Web Based Simulation Visualization Using Java 3D Winter Simulation Conference Phoenix Arizona pp 1425 1429 1999 Schmid C A Remote Laboratory Using Virtual Reality on the Web Simulation Special Issue On Web Based Simulation 73 1999 No 1 S 13 21 Schmid C Virtual Control Laboratories and Remote Experimentation in Control Engineering Proceedings of 11th EAEEIE Annual Conference on Innovations in Education for Electrical and Information Engineering ULM Germany S 213 218 April 2000 Schmid C Remote Experimentation Techniques for Teaching Control Engineering 4th International Scientific Technical Conference Process Control 2000 Kouty nad Desnou Czech Republic June 2000 Schmid C VCLab The Virtual Control Engineering Laboratory Invited paper Proceedings of IFAC Symposium on System Identification Santa Barbara California USA June 2000 C Schmid and A Ali A Web based System for Control
173. vides skeleton applet for easily building a visualisation of a simulation simdiag is a collection of JavaBeans based classes for displaying simulation results A SimJava simulation model is a collection of entities from the Sim entity class each running in its own thread within Java Virtual Machine JVM These entities are connected together by ports from the Sim_port class and can communicate with each other by sending and receiving event from the Sim event class objects A central system class Sim_system controls all the threads advances the simulation time and delivers the events The progress of the simulation is recorded through trace messages produced by the entities and saved in a file Utilizing the Remote Method Invocation RMI facilities of Java Page Moose and Griffin 28 extends the SimJava as described earlier simulation package to enable the construction of distributed web based simulation In the Java distributed object model a remote object is one whose methods can be invoked from another JVM possibly on different hosts An object of this type is described by one or more remote interfaces which are Java interfaces that declare the methods of the remote object A client can call a remote object in a server and that server can also be a client of other remote objects To add the RMI capabilities to SimJava a simple master slave architecture has been designed In this architecture a master server is one that encapsulat
174. y multiply form a web Thus the WWW is a body and organization of information which has no top down structure but is organized in a completely bottom up fashion by the individuals creating the individual pages The addresses of the pages are called URL s Universal Resource Locators 1 and there is a standard communications protocol HTTP 1 for requesting and obtaining any public Web page from any public server The user of the Web operates on a client a workstation or PC running a Windows UNIX Macintosh or OS 2 operating system The client has software called a browser which enables it to request a page from the server and process the HTML file presenting the processed material to the user Therefore the Internet and the WWW hold tremendous potential for the communication and the general distribution of information within groups with a common professional interest 1 4 Web based Simulation In recent years the Internet in general and World Wide Web WWW or Web in particular have grown rapidly as dissemination tools for different kinds of information resources Frequently the Web is used for deployment of educational and commercial material Educators are using the Web to post course notes syllabi homework assignments and even exams and quizzes Companies are using the Web for advertising publicity and to sell products Through JAVA 2 an object oriented programming language people can also customize WWW based
175. y of computer graphics One must look at the screen as a window through which one beholds a virtual world It is up to the computer graphics to make the picture in the window look real sound real and the object real b Video Mapping Video Mapping can be considered as an extension of WoW approach A video input of the user s silhouette is merged with a 2D computer graphic in a WoW system So the user watches a monitor that shows his body s interaction with the virtual world c Immersive System Immersion is a key issue in VR systems as it is central to the paradigm where the user becomes a part of the simulated world The ultimate VR systems completely immerse the user s personal viewpoint inside the virtual world It has to meet four conditions 1 a head mounted device HMD with a wide field of view 2 tracking the position and attitude of the user s body 3 transducers that interpret user s natural behaviours and 4 negligible delays in the rate at which the virtual environment is updated in response to user s movements and actions d Telepresence Telepresence is defined as the experience of presence in an environment by means of a communication medium In other words it refers to the mediated perception of an environment This environment can be either a temporarily or spatially distant real environment or an animated but non existent virtual world synthesized by a computer e Mixed Reality Merging the
176. yDim fdim public static boolean canBeResolved MyDim dimi MyDim dim2 55 MyDim dim3 boolean expand MyDim fdim public static boolean canBeResolved MyDim diml MyDim dim2 MyDim dim3 MyDim dim4 boolean expand MyDim fdim public static boolean updateParam String str Vector mV byte type String err_msg public static void tf2ssAB Vector A Vector B Vector num Vector den int n public static void tf2ssCD Vector C Vector D Vector num Vector den int n public static void factToPoly Vector Mat Vector gV boolean g All the members in this class have been declared as static So other classes can access these methods or variables without instantiating an object of type Basic First five methods of this class equalTo greatTn performs normal relational operations gt lt gt etc on floating point numbers These methods are written in addition to Java s normal relational operators to attain desired precision in floating point calcualtion The checkRegion method returns true if the point x y is within the circle centred at cx cy with radius r The vecToArray double R double C Vector V converts a 2D vector matrix of Complex objects to two 2d array containing the real and imaginary parts respectively The vecToArray double M Vector V method converts a 2D vector matrix of Double objects to a 2d array of double The vecToArray double R Vector V

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