Basics of modeling and simulation
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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.
Basics of modeling and simulation
- 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
- 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
- 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)
- 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: