Course detail

Modelling and Simulation

FIT-IMSAcad. year: 2019/2020

Introduction to modelling and simulation concepts. System analysis and classification. Abstract and simulation models. Continuous, discrete, and hybrid models. Heterogeneous models. Using Petri nets in the 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.

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, statistics, and basics of programming.

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
Peringer P.: Modelování a simulace, studijní opora, FIT/ESF, 2006
Soubor materiálů prezentovaných na přednáškách je dostupný na WWW.
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
Modelica - A Unified Object-Oriented Language for Systems Modeling - Language Specification, Version 3.4, Modelica Association, 2017

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

project, midterm exam, final exam (written)

Exam prerequisites:
At least 10 points you can get during the semester

Language of instruction

Czech, English

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 hybrid systems.

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 BIT Bachelor's, 3. year of study, winter semester, 5 credits, compulsory

  • Programme IT-BC-3 Bachelor's

    branch BIT , 3. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus


  1. Introduction to modelling and simulation. System analysis, classification of systems. Basic introduction to systems theory.
  2. Model classification: conceptual, abstract, and simulation models. Multimodels. Basic methods of model building.
  3. Simulation systems and languages, basic means of model and experiment description. Principles of simulation system implementation.
  4. Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo methods.
  5. Parallel process modelling. Using Petri nets in simulation.
  6. Models o queuing systems. Discrete simulation models.
  7. Time and simulation experiment control, "next-event" algorithm.
  8. Continuous systems modelling. Overview of numerical methods for continuous simulation. Introduction to Modelica.
  9. Combined/hybrid simulation, state events. Modelling of digital systems.
  10. Special model classes, models of heterogeneous systems, model parameters optimization overview.
  11. Analytical solution of queuing system models.
  12. Cellular automata and simulation.
  13. Checking of model validity, verification of models. Analysis of simulation results.

Exercise

4 hours, compulsory

Teacher / Lecturer

Syllabus


  1. discrete simulation: using Petri nets
  2. continuous simulation: differential equations, block diagrams, examples of models

Projects

9 hours, compulsory

Teacher / Lecturer

Syllabus

Individual selection of a suitable problem, its analysis, simulation model creation, experimenting with the model, and analysis of results.

eLearning

eLearning: opened course