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# Introduction to simulation and modeling

antim19
28 Nov 2015
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### Introduction to simulation and modeling

1. Welcome To Our Presentation
2. Group name: Shopnochari
3. Group Member: Name ID Debashish Kumer Shingho 123-15- 2054 Md. Saddam Hossain 123-15-2010 MST.Eshita Katun 123-15-2081 Antim 123-15-2162
4. Importance of simulation & Systemwith model
5. Originof simulation word --- Middle English simulacion & Latin simulātiōn- (stem of simulātiō)
6. Simulation ---? Simulation modeling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. Simulation modeling is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand.
7. Simulation in Computer science--- simulation has some specialized meanings – Alan Turing used the term "simulation" to refer to what happens when a universal machine executes a state transition table that describes the state transitions, inputs and outputs of a subject discrete-state machine.
8. SimulationModeling Workflow--- Use a 2D or 3D CAD tool to develop a virtual model, also known as a digital prototype Generate a 2D or 3D mesh for analysis calculations. Define finite element analysis data based on analysis type. Perform finite element analysis, review results, and make engineering judgments based on results.
9. Simulationis Needed  experiment with new designs or policies prior to implementation  can be used to verify analytic solutions  different capabilities for a machine, requirements can be determined.  designed for training allow learning without the cost and disruption of on-the-job learning. Simulation is not Needed  problem can be solved using common sense  simulation costs exceed the savings  Resources or time are not available  system behavior is too complex or can’t be defined.  isn’t the ability to verify and validate the model.
10. Why is Modeling and Simulation Important in Engineering?  Unavailable input and output.  Experiment may be too dangerous.  Cost of experimentation might be too high.  Time constants of the system may not be compatible with human dimensions.  Experimental behavior might be obscured by disturbances.
11. Advantage:  New polices, operating procedures, decision rules, information flows, organizational procedures.  New hardware designs, physical layouts, transportation systems.  Hypotheses about how or why certain phenomena occur can be tested for feasibility.  Insight can be obtained about the interaction of variables.  Insight can be obtained about the importance of variables to the performance of the system.
13. Disadvantages: There are disadvantages in using a simulation model:-  We have a poor understanding of how some physical systems work so that we do not have sufficient data to produce a mathematical model. For this reason it has not been possible to create simulations that can accurately predict the occurrence and effects of earthquakes and tsunami.  The formula and functions that are used may not provide an accurate description of the system resulting in inaccurate output from the simulation.  Complex simulations can require the use of a computer system with a fast processor and large amounts of memory.  Simulation results may be difficult to interpret.  Simulation modeling and analysis can be time consuming and expensive.
14. Areas of Application:
15. What is Model? •Representation of a real or theoretical System. •Simplification of the System. •Understanding of the System.
16. Why we need modeling ?  Cost effective way to represent a real-world system.  Key aspects of the system, components.  And how those components communicate with one another.  To test designs before implementation.
17. Model Types • Visual Models : Graphical sketches, Computer solid models  Physical Models: Prototypes, mock-ups, structural models. • Logical Models : Algebraic and different equation used for computer simulation.  Empirical Models: Relationship between variables.
18. Deterministic Simulation Models • A model that does not contain probability. • Every run will result the same. • Single run is enough to evaluate the result.
19. Stochastic Simulation Models • A model that contains probability. using random numbers.  Every run will result differently. • Multiple runs are required to evaluate the results.
20. Define An Achievable Goal To model the…” is NOT a goal! “To model the…in order to select/determine feasibility/…is a goal. Goal selection is not cast in concrete Goals change with increasing insight
21. Example for process Model
22. Diagram of simulation & modeling
23. Modeling ---  Disadvantages:  Higher software cost .  Additional training required.  Limited portability.  Advantages:  Standard features often needed .  Shorter development cycle .  Very readable code.
24. What is a System: A set of principles or procedures according to which something is done; an organized scheme or method.  Primary objective is complete a task  Object are connected together  Follow set of defined rules
25. System Environment --- where all the system objects are grouped together to accomplish the task. System Boundary: System boundary are something to detect whether the changes occurs inside the system or outside the system
26. Types of System: There are two types of system.  Discrete System  Continuous System
27. What system made of: Some important key concept of a system is given below  Entity  Attribute  Activity  State  Event  Endogenous  Exogenous
28. Entity: Entityis a real world object in the system
29. Attribute: A property of an entity
30. Activity: A time period or task which is done of specified length.
31. State: The collection of variables necessary to describe the system at any time
32. Event: An sudden occurrence that may change the state of the system.
33. Endogenous: Endogenous describe activities and events occurring within a system. Exogenous Exogenous describe activities and events in an environment that affect the system
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