Simulation and Modeling

Course Title: Simulation and Modeling

Course No: ENCT353

Nature of the Course: Theory + Lab

Semester: 6

Full Marks: 60 + 40 + 25

Pass Marks: 24 + 16 + 10

Credit Hours: 3

Course Description

This course develops knowledge of modeling and simulation techniques for discrete and continuous systems, emphasizing the development and analysis of simulation models, generation and testing of random numbers and variables, and application of simulation methods to evaluate the performance of stochastic systems.

Course Objectives

The objective of this course is to:

Develop knowledge of modeling and simulation techniques for discrete and continuous systems
Emphasize the development and analysis of simulation models
Cover generation and testing of random numbers and variables
Apply simulation methods to evaluate the performance of stochastic systems

Course Contents

1. Introduction to Simulation

4 hrs

1.1. System and system environment concepts

1.2. Continuous and discrete systems

1.3. Types of models

1.4. Model development life cycle

1.5. Simulation and steps in simulation study

1.6. Advantages and disadvantages of simulation

1.7. Monte-Carlo simulation

1.8. Discrete-event system simulation

2. Physical and Mathematical Models

4 hrs

2.1. Differential and partial differential equations

2.2. Static physical model

2.3. Dynamic physical model

2.4. Static mathematical models

2.5. Dynamic mathematical models

3. Simulation of Continuous System

5 hrs7 marks

3.1. Continuous system models

3.2. Analog computer

3.3. Analog methods

3.4. Hybrid simulation

3.5. Digital-analog simulators

3.6. Continuous system simulation languages (CSSLs)

3.7. Feedback systems

4. Simulation of Queuing System

6 hrs8 marks

4.1. Elements of queuing system

4.2. Characteristics of queuing systems

4.3. Model of queuing system

4.4. Types of queuing system

4.5. Queuing notation (Kendall's notation)

4.6. Measurement of system performance

4.7. Network of queues

4.8. Applications of queuing system

5. Markov Chains

3 hrs5 marks

5.1. Key features of Markov chains

5.2. Markov process with examples

5.3. Applications of Markov chains

6. Random Number

10 hrs13 marks

6.1. Properties of random numbers

6.2. Generation of pseudo-random numbers

6.3. Random number generation: Linear and arithmetic congruential methods

6.4. Tests for random numbers

Kolmogorov-Smirnov test

Chi-Square (χ2) test

Gap test

Poker's method

Testing for auto correlation

6.5. Generating discrete distribution

6.6. Inversion, rejection, composition and convolution

7. Verification and Validation of Simulation Models

3 hrs4 marks

7.1. Verification and validation

7.2. Verification of simulation models

7.3. Calibration and validation of models

7.4. Naylor and finger validation process

7.5. Validation: Errors

8. Analysis of Simulation Output

4 hrs5 marks

8.1. Confidence intervals and hypothesis testing

8.2. Estimation methods

8.3. Simulation run statistics

8.4. Replication of runs

8.5. Elimination of initial bias

9. Simulation Software

3 hrs

9.1. Simulation in Java

9.2. Simulation in GPSS

9.3. Simulation in Python

9.4. Other simulation software

10. Simulation of Computer Systems

3 hrs

10.1. Simulation tools

10.2. High level computer: System simulation

10.3. CPU simulation

10.4. Memory simulation

10.5. Simulation of computer networks

Laboratory Works

Practical sessions cover simulation of R-C amplifier circuit and mass spring damper system, generation of random numbers, Chi-square goodness-of-fit test and Kolmogorov-Smirnov test, simulation of queuing system, and simulation of Markov chain. (22.5 hours)

1.Simulation of the R-C Amplifier Circuit and Mass Spring Damper System
2.Generation of Random Number
3.Chi-Square Goodness-of-Fit Test and Kolmogorov-Smirnov Test
4.Simulation of Queuing System
5.Simulation of Markov Chain

Reference Books

1.Banks, J. (2005). Discrete-event system simulation. Pearson.
2.Gordon, G. (1978). System simulation (Latest Edition). Prentice Hall.
3.Law, A. M., Kelton, W. D. (2007). Simulation modeling and analysis. McGraw-Hill Education.
4.Rubinstein, R. Y., Melamed, B. (1998). Modern simulation and modeling (Latest Edition). John Wiley & Sons.
5.Singh, V. P. (2009). System modeling and simulation. New Age International Publishers.

Notes:

This syllabus follows the official BCT curriculum of Tribhuwan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://ioe.tu.edu.np/pages/computer-engineering-curriculum-structure-2635

Note: Chapters 1 and 2 together carry 10 marks and require approximately 8 hours of study.

Also, Chapters 9 and 10 together carry 8 marks and require approximately 6 hours of study.

Simulations and Modeling