Modelling and Simulation
IMS Acad. year 2021/2022 Winter semester 5 credits
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.
Language of instruction
Course Web Pages
Subject specific learning outcomes and competences
Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.
The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and hybrid systems.
Why is the course taught
The algorithms and basic principles of modelling and simulation are frequently used for electrical circuit simulation, queuing systems simulation, etc. The know-how can be used in other areas, too (for example computer games implementation).
Prerequisite knowledge and skills
Basic knowledge of numerical mathematics, probability, statistics, and basics of programming.
- Dymola, Modelica
- GCC, Octave, Scilab, SIMLIB/C++, OpenModelica
- 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
- Texts available on course WWW page.
- Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7
- Soubor materiálů prezentovaných na přednáškách je dostupný na WWW.
- 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
- Modelica - A Unified Object-Oriented Language for Systems Modeling -
Language Specification, Version 3.4, Modelica Association, 2017
Syllabus of lectures
- Introduction to modelling and simulation. System analysis, classification of systems. Basic introduction to systems theory.
- Model classification: conceptual, abstract, and simulation models. Multimodels. Basic methods of model building.
- Simulation systems and languages, basic means of model and experiment description. Principles of simulation system implementation.
- Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo methods.
- Parallel process modelling. Using Petri nets in simulation.
- Models o queuing systems. Discrete simulation models.
- Time and simulation experiment control, "next-event" algorithm.
- Cellular automata and simulation.
- Continuous systems modelling. Overview of numerical methods for continuous simulation. Introduction to Modelica.
- Combined/hybrid simulation, state events. Modelling of digital systems.
- Special model classes, models of heterogeneous systems, model parameters optimization overview.
- Analytical solution of queuing system models.
- Checking of model validity, verification of models. Analysis of simulation results.
Syllabus of numerical exercises
- discrete simulation: using Petri nets
- continuous simulation: differential equations, block diagrams, examples of models
Syllabus - others, projects and individual work of students
Individual selection of a suitable problem, its analysis, simulation model creation, experimenting with the model, and analysis of results.
project, midterm exam, final exam (written)
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.
At least 10 points you can get during the semester
Course inclusion in study plans