Course detail

Simulation Tools and Techniques

FIT-SNTAcad. year: 2018/2019

Theory of modelling and simulation, DEVS (Discrete Event System Specification) formalism. Simulation systems, their design and implementation. Algorithms used for simulation control, introduction to parallel and distributed simulation. Continuous, discrete, and combined simulation: model description methods, simulation tools, numerical methods. Special types of models; corresponding methods, techniques, and tools. Modeling of systems described by partial differential equations. Introduction to model validation and verification. Simulation experiment control. Simulation results analysis and visualization overview. Simulation system case study.

Learning outcomes of the course unit

The basics of modeling and simulation theory. Understanding the principles of simulation system implementation. Knowledge of advanced simulation methods and techniques.
Creation of simulation tools, models, and practical use of simulation methods.

Prerequisites

Basic knowledge of modelling, simulation, algorithms, and numerical mathematics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • Rábová, Z. a kol.: Modelování a simulace, VUT Brno, 1992, ISBN 80-214-0480-9
  • Cellier, F., Kofman, E.: Continuous System Simulation, Springer, 2006, ISBN: 978-0-387-26102-7
  • Fishwick, P.: Simulation Model Design and Execution, Prentice Hall, 1995, ISBN 0-13-098609-7
  • Soubor materiálů dostupný na WWW stránce předmětu.

  • Fishwick, P.: Simulation Model Design and Execution, Prentice Hall, 1995, ISBN 0-13-098609-7
  • Law, A., Kelton, D.: Simulation Modelling and Analysis, McGraw-Hill, 2000, ISBN 0-07-100803-9
  • Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, second edition, Academic Press, 2000, ISBN 0-12-778455-1
  • Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
  • Cellier, F., Kofman, E.: Continuous System Simulation, Springer, 2006, ISBN: 978-0-387-26102-7
  • Fujimoto, R.: Parallel and Distribution Simulation Systems, John Wiley & Sons, 1999, ISBN:0471183830
  • Chopard, B.: Cellular Automata Modelling od Physical Systems, Cambridge University Press, 1998, ISBN:0-521-67345-3

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

At least half of the points for each project.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

    Syllabus of lectures:
    1. Introduction. Theory of modelling and simulation, DEVS formalism.
    2. DEVS simulator.
    3. Simulation systems: classification, principles of design and implementation. Simulation control algorithms.
    4. Parallel and distributed simulation.
    5. Continuous simulation: numerical methods, stiff systems, algebraic loops. Dymola simulation system, Modelica language.
    6. Discrete simulation: implementation of events and processes. Queueing systems.
    7. Combined/hybrid simulation: state conditions and state events.
    8. Modelling of systems described by partial differential equations. Basics of sensitivity analysis.
    9. Digital systems simulation models and tools. Simulation and cellular automate.
    10. Models of uncertainty, using fuzzy logic in simulation. Qualitative simulation.
    11. Multimodels. Optimization methods in simulation. 
    12. Simulation experiment control, simulation results analysis. Introduction to model validation and verification. Visualization methods. User interfaces of simulation systems.
    13. Simulation system implementation case study. Examples of simulation models.

    Syllabus - others, projects and individual work of students:
    • Individual solution of specified simulation problem, or extending of given simulation system to allow the use of new modelling methods.

Aims

Students will be introduced to design and implementation principles of simulation systems. Further, the methods and techniques for modeling and simulation of various types of models will be presented.

Specification of controlled education, way of implementation and compensation for absences

Within this course, attendance on the lectures is not monitored.
The knowledge of students is examined by the projects and
by the final exam. The minimal number of points which
can be obtained from the final exam is 30. Otherwise,
no points will be assigned to a student.


Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MGM , any year of study, summer semester, 5 credits, compulsory-optional
    branch MSK , any year of study, summer semester, 5 credits, optional
    branch MIS , any year of study, summer semester, 5 credits, optional
    branch MBS , any year of study, summer semester, 5 credits, compulsory-optional
    branch MMI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MMM , any year of study, summer semester, 5 credits, compulsory-optional
    branch MIN , 1. year of study, summer semester, 5 credits, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus


  1. Introduction. Theory of modelling and simulation, DEVS formalism.
  2. DEVS simulator.
  3. Simulation systems: classification, principles of design and implementation. Simulation control algorithms.
  4. Continuous simulation: numerical methods, stiff systems, algebraic loops. Dymola simulation system, Modelica language.
  5. Discrete simulation: implementation of calendar queue, events and processes. Queueing systems.
  6. Combined/hybrid simulation: state conditions and state events.
  7. Modelling of systems described by partial differential equations. Basics of sensitivity analysis.
  8. Digital systems simulation models and tools. Simulation and cellular automate.
  9. Parallel and distributed simulation.
  10. Models of uncertainty, using fuzzy logic in simulation.
    Qualitative simulation.
  11. Multimodels. Optimization methods in simulation. 
  12. Simulation experiment control, simulation results analysis. Introduction to model validation and verification. Visualization methods.
  13. Simulation system implementation case study. Examples of simulation models.

Project

13 hours, compulsory

Teacher / Lecturer

Syllabus


  • Individual solution of specified simulation problem, or extending of given simulation system to allow the use of new modelling methods.

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