Course detail
Modelling and Simulation
FIT-IMSAcad. year: 2014/2015
Introduction to modelling and simulation concepts. System analysis and classification. Abstract and simulation models. Continuous, discrete, and combined models. Heterogeneous models. Using Petri nets 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
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
- Syllabus of lectures:
- Introduction to modelling and simulation. System analysis, classification of systems. Systems theory basics.
- 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.
- Continuous systems modelling. Overview of numerical methods used for continuous simulation. Introduction to Dymola simulation system.
- Combined/hybrid simulation. Modelling of digital systems.
- Special model classes, models of heterogeneous systems. Model optimization.
- Analytical solution of queuing system models.
- Cellular automata and simulation.
- Checking of model validity, verification of models. Analysis of simulation results. Visualization of simulation results.
- discrete simulation: using Petri nets
- continuous simulation: differential equations, block diagrams, examples of models
Syllabus of numerical exercises:
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.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
- recommended prerequisite
Algorithms - recommended prerequisite
Signals and Systems - recommended prerequisite
Introduction to Programming Systems - recommended prerequisite
Linear Algebra - recommended prerequisite
Mathematical Analysis 1 - recommended prerequisite
Discrete Mathematics - recommended prerequisite
Mathematical Analysis 2 - recommended prerequisite
Probability and Statistics
Basic literature
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction to modelling and simulation. System analysis, classification of systems. Systems theory basics.
- 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.
- Continuous systems modelling. Overview of numerical methods used for continuous simulation. Introduction to Dymola simulation system.
- Combined/hybrid simulation. Modelling of digital systems.
- Special model classes, models of heterogeneous systems. Model optimization.
- Analytical solution of queuing system models.
- Cellular automata and simulation.
- Checking of model validity, verification of models. Analysis of simulation results. Visualization of simulation results.
Fundamentals seminar
Teacher / Lecturer
Syllabus
- discrete simulation: using Petri nets
- continuous simulation: differential equations, block diagrams, examples of models
E-learning texts