Modeling and Simulation simulation and modeling methodology

1. Princess Nora University Modeling and Simulation simulation and modeling methodology Arwa Ibrahim Ahmed

2. Keywords: What is a model? predictions about the behavior of the system. • In all cases a model should be an abstraction of the system: an attempt to distill, from the mass of details that is the system itself, exactly those aspects that are essential to the system’s behavior. • Once a model has been defined through this abstraction process, it can be parameterized to reflect any of the alternatives under study, and then evaluated to determine its behavior under this alternative.

3. Keywords Simulation It is an experiment in a computer where the real system is replaced by the execution of the program It is a program that imitates the behaviour of the real system

4. History: Computer simulation developed hand-in-hand with the rapid growth of the computer, following its first large-scale deployment during the Manhattan Project in World War II to model the process of nuclear detonation. It was a simulation of 12 hard spheres using a Monte Carlo algorithm. Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. There are many types of computer simulations; the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.

5. Problem with direct experimentation There are many reasons why investigations into the behavior of a system cannot be carried out by direct experimentation on the system. Why? (Reasons for using modeling instead of direct experimentation) • At best experimentation will probably be disruptive, at worst, dangerous. For example, a system manager of a heavily loaded file server is unlikely to allow experimentation. • May be that the internal behavior which we wish to investigate may not be accessible in a working system. For example, in most operating systems it is difficult to obtain the exact timing of instruction level events. • Direct Experimentation, since it involves monitoring, is expensive (in terms of system resources) and time consuming. • It is also difficult to use the results of direct experimentation to extrapolate to other scenarios of operation. For example, experimenting with one configuration of a communication network is unlikely to give us much insight into the expected performance if another configuration is used.

6. Simulation advantage: • Using simulations is – as a rule – cheaper and safer than conducting experiments with a prototype of the real thing. One of the biggest computers worldwide is currently designed in order to simulate the detonation of nuclear devices and their effects in order to support better preparedness in the event of a nuclear explosion. Similar efforts are conducted to simulate hurricanes and other natural catastrophes. • Simulations can often be even more realistic than traditional experiments, as they allow the free configuration of environment parameters found in the operational application field of the final product. Examples are supporting deep water operation of the US Navy or the simulating the surface of neighbored planets in preparation of NASA missions.

7. Simulation advantage: • Simulations can often be conducted faster than real time. This allows using them for efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained from operational data. This use of simulation adds decision support simulation systems to the tool box of traditional decision support systems. • Simulations allow setting up a coherent synthetic environment that allows for integration of simulated systems in the early analysis phase via mixed virtual systems with first prototypical components to a virtual test environment for the final system. If managed correctly, the environment can be migrated from the development and test domain to the training and education domain in follow-on life cycle phases for the systems (including the option to train and optimize a virtual twin of the real system under realistic constraints even before first components are being built).

8. Computer simulation in practical: analysis of air pollutant dispersion using atmospheric dispersion modeling. design of complex systems such as aircraft and also logistics systems. design of Noise barriers to effect roadway noise mitigation. flight simulators to train pilots. weather forecasting. Simulation of other computers is emulation. forecasting of prices on financial markets (for example Adaptive Modeler). behavior of structures (such as buildings and industrial parts) under stress and other conditions.

9. Computer simulation in practical: • Strategic Management and Organizational Studies. • Reservoir simulation for the petroleum engineering to model the subsurface reservoir Process Engineering Simulation tools. • Robot simulators for the design of robots and robot control algorithms • Urban Simulation Models that simulate dynamic patterns of urban development and responses to urban land use and transportation policies. • Traffic engineering to plan or redesign parts of the street network from single junctions over cities to a national highway network, for transportation system planning, design and operations. • design of industrial processes, such as chemical processing plants. • modeling car crashes to test safety mechanisms in new vehicle models.

10. A Few Example Applications Flight Simulator War gaming: test strategies; training Transportation systems: improved operations; urban planning Games Computer communication network: protocol design Parallel computer systems: developing scalable software

11. Model characterization

12. Model characterization The characterization of a system model can be summarized by a tree diagram that starts at the system model root and steps left or right at each of the three levels:

13. Model characterization

14. Model characterization A discrete-event simulation model is defined by three attributes: • stochastic | at least some of the system state variables are random; • dynamic | the time evolution of the system state variables is important; • discrete-event | significant changes in the system state variables are associated with events that occur at discrete time instances only.

15. Data preparation: • The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models). • Input sources also vary widely: • Sensors and other physical devices connected to the model; • Control surfaces used to direct the progress of the simulation in some way; • Current or Historical data entered by hand. • Values extracted as by-product from other processes; • Values output for the purpose by other simulations, models, or processes.