Modeling is the process of describing a data model that includes the construction and working of the model. This data model is very similar to a Real System and it helps the analyst to predict the effect of changes in the system. In other words, modeling is the creation of a model that represents a system including the system properties. It is an act of building and developing a model.
The Simulation of a system is the operation of a model in the areas of time or space, this information helps the analyst to analyze the performance of an existing or a proposed system. In other words, simulation is defined as the process of making use of a model to study the performance of a system. It is the act of using a model for simulation purposes.
Modeling and Simulation (M&S) refers to the use of already defined data models, for example, the physical, mathematical, or logical representation and illustration of an entity, system, phenomenon, or process as a foundation for simulations to produce data that are used for managerial or technical decision making.
1. System entities
2. Input variables
3. Performance measures
4. Functional relationships
Steps Involved in Developing a Simulation Model are:
Step 1. Identify the problem: Evaluate problems with an existing system, produce requirements for a proposed system.
Step 2. Formulate the problem: Identify the bounds of the system, the problem to be studied. Define the overall objective of the study and issues to be addressed. Define performance measures - quantitative criteria on the basis of which different system configurations will be compared and ranked. Identify, briefly at this stage, the configurations of interest and formulate hypotheses about system performance. Decide the time frame of the study, i.e., will the model be used for a one-time decision (e.g., capital expenditure) or over a period of time on a regular basis (e.g., air traffic scheduling). Identify the end-user of the simulation model, e.g., corporate management versus a production supervisor. Problems must be formulated as precisely as possible
Step 3. Collect and process real system data: Collect data on system specifications (e.g., bandwidth for a communication network), input variables, as well as the performance of the existing system. Identify sources of randomness in the system, select an appropriate input probability distribution for each stochastic input variable and estimate the corresponding parameter(s).
Step 4. Formulate and develop a model: Develop schematics and network diagrams of the system (How do entities flow through the system?). Translate these conceptual models to simulation software acceptable form. Verify that the simulation model executes as intended.
Step 5: Validate The Model: Compare the model's performance under known conditions with the performance of the real system. Perform statistical inference tests and get the model examined by system experts. Assess the confidence that the end-user places on the model and address problems if any.
The following are some of the basic features of Modelling & Simulation.
1. The object is an item that lives in the real world, that is used to study the behavior and characteristics of a model.
2. The Base Model is a theoretical explanation of the properties of objects and their behavior, which is valid across the model.
3. System: The system is the articulate object under specific conditions, that exists in the real world.
4. Experimental Frame is used to carry out analysis of a system that is in the real world, such as the experimental conditions, aspects, objectives, etc. The Basic Experimental Frame is made up of two sets of variable, namely, the Frame Input Variables & the Frame Output Variables, that matches the system or model terminals. The Frame input variable is usually responsible for matching the inputs applied to the system or the model. The Frame output variable is responsible for matching the values outputted to the system or the model.
5. Lumped Model is an exact description of a system that goes by the predefined conditions of a given Experimental Frame.
6. Verification is the process of comparing two or more items to make sure that they are accurate. In Modelling & Simulation, verification can be carried out by comparing the frequency and consistency of a simulation program and the lumped model to make sure that their performance is high.
7. Validation is the process of comparing different results. In Modelling & Simulation, validation is carried out by comparing experiment measurements with the simulation results that are within the context of an Experimental Frame. The model is not valid if the results have gotten did not match.
The following are some of the Benefits and Advantages of using Modelling and Simulation:
1. Easy to understand: They allow you to understand how the system really works without working on a real-time system.
2. Easy to test: Modeling and Simulation allow you to make changes into the system and view its effect on the output without working on real-time systems.
3. Easy to upgrade: Modeling and Simulation allow you to determine the system requirements by utilizing different configurations.
4. Easy to identifying constraints: They allow you to perform bottleneck interpretation that creates a delay in the information, work process, etc.
5. Easy to diagnose problems: Some systems are so complex that it is not easy to understand their communications at a time.
1. When starting a project, Modeling and Simulation helps you to find out the system requirements by testing out different configurations.
2. Knowledge of Modeling and Simulation helps you to diagnose and break down complex systems into easy to understand bits.
3. Knowledge of Modeling and Simulation lets you understand how a system would work in real life by visualizing it in a virtual system.
4. Job Opportunity and Career Advancement.
5. Entrepreneurship Opportunity and Consultancy.
Concepts & Classification
Verification & Validation
Discrete System Simulation
Monte Carlo Simulation
Modelling & Simulation - Database
Modeling And Simulation - Exams And Certification
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