The document provides an overview of modeling and simulation, discussing their importance in representing complex systems and aiding in decision-making. It categorizes models into physical and mathematical types and elaborates on the benefits and risks of simulations, such as flexibility and potential inaccuracies. Various simulation software and classifications of models are also mentioned, highlighting the distinctions between discrete and continuous systems.

1. Basics of Modeling and Simulation
Serdar Kaya, serdar@grenergyllc.com cell: +90 5394772377

2. X
Y
Why do we need modeling and simulation
Human brain can comprehend only two or three dimensional objects or concepts?
Two Dimensions of X and Y

3. X
Y
Z
Three dimensions of X, Y and Z

4. X
Y
Z
X
Y
Z
X
Y
Z
T1
T2
T3
Four dimensions of X, Y, Z and T

5. X
Y
Z
R
S
More domain and dimensions

6. X
Y
Z
3
2
1
Linear Scale

7. X
Y
Z
100
10
1
Logarithmic Scale

8. What Is A Model ?
A Representation of an object, a
system, or an idea in some form
other than that of the entity itself.
(Shannon)

9. Types of Models:
Physical
(Scale models, prototype plants,…)
Mathematical
(Analytical queueing models, linear
programs, simulation)

10. What is Simulation?
● dynamic behavior of a model
● representation of dynamic behavior
● changing behavior of parameters in dynamic
system

11. Modeling Team
We Need:
-Knowledge of the system under investigation
-System analyst skills (model formulation)
-Model building skills (model Programming)
-Data collection skills
-Statistical skills (input data representation)

12. Terminology
System Entities Attribute Activity Event State
Variables
Digestion
System
Stomach Acidity Eating Digestion Mucous
Pulsing
Content

13. " Simulation of Resource Reservation Protocol in Wireless Networks, "
Enguang He & Min Zhang (PPT file)
" Simulation of Rare Events in Communications Networks," Xiangping Chen
(PPT file)
" Parallel Discrete Event Simulation," Hariharan L Thantry (PPT file)
" Difficulties in Simulating the Internet," Mingyu Sun
College of Engineering
Department of Computer Science
CSE808 Modeling and Discrete Simulation (Fall 2019)
Instructor: Herman D. Hughes
Class Meeting: Tu&Thu 10:20 - 11:40 am C207Wells Hall.
Office Hours: Tu 1:30-4:30 p.m. or by appointment. 2132EB

14. Sim-to-Real: Using Simulations for 3D Perception and
Navigation
Dr. Ruigang Yang
Baidu Research
Thursday, Feb 28, 2019
11 AM - 12 PM
EB 3105

21. ● To simulate is to try to duplicate the characteristics
of a real system
● mathematical simulation models
● Real systems to help make business decisions
● Use in education to reduce cost

22. Benefit of Modeling/Simulation
1. Flexibility
2. Can handle large and complex systems
3. Can answer “what-if” questions
4. Does not interfere with the real system
5. Allows study of interaction among variables
6. “Time compression” is possible
7. Handles complications that other methods can’t

23. Risks of Modeling/Simulation
1. inaccuracy
2. Initial time consuming
3. Sceneroi time needed
4. Each case is unique might have unconsidered
parameters

24. Cost of Modeling/Simulation Software

25. Available Models/Simulations
Monte Carlo Simulation
Metlab
Ansys (fluid flow)
Flight Simulators
Surgery Simulators
War/Fighting Simulators
Civilization Simulators

26. Available Models/Simulations

27. Available Models/Simulations
https://www.youtube.com/watch?v=jj0WsQYtT7M

28. Highly Complicated Models/Simulations

29. Highly Complicated Models/Simulations

30. Highly Complicated Models/Simulations
https://www.youtube.com/watch?v=857wSFqZGSU

31. Highly Complicated Models/Simulations
BP Plc Uses Borrowed Tech to Drive Billions in Savings
An employee demonstrates a oil drilling simulator in a training room at BP
Plc's International Centre for Business & Technology (ICBT)

32. https://www.youtube.com/watch?v=gQxspJWH6J0
Drilling Simulator

33. ● General purpose languages
(Visual Basic, C++, Fortran, etc.)
● Special purpose languages and programs
(GPSS, Simscript, Microsaint, BuildSim, etc.)
● Spreadsheet models
Simulation Software Packages

34. SIMULATION “WORLD-
VIEWS”
● Pure Continuous Simulation
● Pure Discrete Simulation
– Event-oriented
– Activity-oriented
– Process-oriented
● Combined Discrete / Continuous Simulation

35. Systems, Models, and Simulation(cont’d.)
• Classification of simulation models
– Static vs. dynamic
– Deterministic vs. stochastic
– Continuous vs. discrete
• Most operational models are dynamic, stochastic, and
discrete – will be called discrete-event simulation models

36. Systems, Models, and Simulation(cont’d.)
• Types of systems
– Discrete
▪ State variables change instantaneously at separated points in time
▪ Bank model: State changes occur only when a customer arrives or departs
– Continuous
▪ State variables change continuously as a function of time
▪ Airplane flight: State variables like position, velocity change continuously
• Many systems are partly discrete, partly continuous

37. Examples Of Both Type Models
● Continuous Time and Discrete Time
Models:
CPU scheduling model vs. number of
students attending the class.

38. Examples (continued)
● Continuous State and Discrete State
Models:
Example: Time spent by students in a
weekly class vs. Number of jobs in Q.

39. n Static and Dynamic Models:
CPU scheduling model vs. E = mc2
Other Type Models
Input
Output
Input
Output
●Deterministic and Probabilistic Models: