Title:  Modelling and Simulation 

Code:  IMS 

Ac.Year:  2017/2018 

Term:  Winter 

Curriculums:  

Language of Instruction:  Czech 

Public info:  http://www.fit.vutbr.cz/study/courses/IMS/public/ 

Private info:  http://www.fit.vutbr.cz/study/courses/IMS/private/ 

Credits:  5 

Completion:  credit+exam (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  39  4  0  0  9 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  70  10  0  0  20 



Guarantor:  Peringer Petr, Dr. Ing., DITS 

Lecturer:  Hrubý Martin, Ing., Ph.D., DITS Peringer Petr, Dr. Ing., DITS 
Instructor:  Hrubý Martin, Ing., Ph.D., DITS 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Intelligent Systems FIT BUT 

Prerequisites:  

Substitute for:  

 Learning objectives: 

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

  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.  Knowledge and skills required for the course: 

  Basic knowledge of numerical mathematics, probability, statistics, and basics of programming.
 Learning outcomes and competences: 

  Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.  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, "nextevent" 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.
 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.  Fundamental literature: 


 Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0130986097
 Law A., Kelton D.: Simulation Modelling and Analysis, McGrawHill, 1991, ISBN 0071008039
 Ross, S.: Simulation, Academic Press, 2002, ISBN 0125980531
 Study literature: 


 Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0130986097
 Law A., Kelton D.: Simulation Modelling and Analysis, McGrawHill, 1991, ISBN 0071008039
 Texts available on course WWW page.
 Controlled instruction: 

  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.  Progress assessment: 

  project, midterm exam
 Exam prerequisites: 

  At least 10 points you can get during the semester
 
