Course detail

Modelling and Simulation

FIT-IMSAcad. year: 2010/2011

Introduction to modelling and simulation concepts. System analysis and classification. Abstract and simulation models. Continuous, discrete, and combined models. Heterogeneous models. Using Petri nets and finite automata in simulation. Pseudorandom number generation and testing. Queuing systems. Monte Carlo method. Continuous simulation, numerical methods, Modelica language. Simulation experiment control. Visualization and analysis of simulation results.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Dr. Ing. Petr Peringer

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.

Prerequisites

Basic knowledge of numerical mathematics, probability and statistics, and basics of programming.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

At least half of the points you can get during the semester

Course curriculum

  • Introduction to modelling and simulation. System analysis, clasification of systems. System theory basics, its relation to simulation.
  • Model classification: conceptual, abstract, and simulation models. Heterogeneous models. Methodology of model building.
  • Simulation systems and languages, means for model and experiment description. Principles of simulation system design.
  • Parallel process modelling. Using Petri nets and finite automata in simulation.
  • Models o queuing systems. Discrete simulation models. Model time, simulation experiment control.
  • Continuous systems modelling. Overview of numerical methods used for continuous simulation. System Dymola/Modelica.
  • Combined simulation. The role of simulation in digital systems design.
  • Special model classes, models of heterogeneous systems.
  • Cellular automata and simulation.
  • Checking model validity, verification of models. Analysis of simulation results.
  • Simulation results visualization. Model optimization.
  • Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo method.
  • Overview of commonly used simulation systems.

Work placements

Not applicable.

Aims

The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and combined systems.

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

Within this course, attadance 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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Basic literature

Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7 Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9 Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 3. year of study, winter semester, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Dr. Ing. Petr Peringer

Syllabus

  • Introduction to modelling and simulation. System analysis, clasification of systems. System theory basics, its relation to simulation.
  • Model classification: conceptual, abstract, and simulation models. Heterogeneous models. Methodology of model building.
  • Simulation systems and languages, means for model and experiment description. Principles of simulation system design.
  • Parallel process modelling. Using Petri nets and finite automata in simulation.
  • Models o queuing systems. Discrete simulation models. Model time, simulation experiment control.
  • Continuous systems modelling. Overview of numerical methods used for continuous simulation. System Dymola/Modelica.
  • Combined simulation. The role of simulation in digital systems design.
  • Special model classes, models of heterogeneous systems.
  • Cellular automata and simulation.
  • Checking model validity, verification of models. Analysis of simulation results.
  • Simulation results visualization. Model optimization.
  • Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo method.
  • Overview of commonly used simulation systems.

Fundamentals seminar

2 hours, optionally

Teacher / Lecturer

Dr. Ing. Petr Peringer

Syllabus

  1. discrete simulation: using Petri nets, using SIMLIB/C++
  2. continuous simulation: differential equations, block diagrams, examples of models in SIMLIB/C++

Exercise in computer lab

2 hours, optionally

Teacher / Lecturer

Dr. Ing. Petr Peringer

Syllabus

  1. Introduction to Dymola simulation system, continuous simulation.

Project

9 hours, optionally

Teacher / Lecturer

Dr. Ing. Petr Peringer