System Modeling and Simulation in .NET

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Models serve as building block representations or approximations of physical reality. When we integrate these models into an executable framework that enables us stimulate interactions and behavioral responses under controlled conditions, we create a simulation of a SYSTEM OF INTEREST (SOI). As analytical models, simulations enable us to conduct WHAT IF exercises with each model or system. In this context the intent is for SEs to understand the functional or physical behavior and interactions of the system for a given set of OPERATING ENVIRONMENT scenarios and conditions. Guidepost 51.1 The preceding discussions provide the foundation for understanding models and simulations. We now shift our focus to understanding HOW SEs employ models and simulations to support analytical decision making as well as create deliverable products for Users.
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Modeling and simulation (M&S) are applied in a variety of ways by SEs to support technical decision making. SEs employ models and simulations for several types of applications: Application 1: Application 2: Application 3: Application 4: Application 5: Application 6: Application 7: Simulation-based architecture selection Simulation-based architectural performance allocations Simulation-based acquisition (SBA) Test environment stimuli Simulation-based failure investigations Simulation based training Test bed environments for technical decision support
To better understand HOW SEs employ models and simulations, let s describe each type of application.
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Application 1: Simulation-Based Architecture Selection
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When you engineer systems, you should have a range of alternatives available to support informed selection of the best candidate to meet a set of prescribed OPERATING ENVIRONMENT scenarios and conditions. In practical terms, you cannot afford to develop every candidate architecture just to study it for purposes of selecting the best one. We can construct, however, models and simulations that represent functional or physical architectural con gurations. To illustrate, consider the following example using Figure 51.1.
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Let s suppose we have identi ed several promising Candidate Architectures 1 through n as illustrated on the left side of the diagram. We conduct a trade study analysis of alternatives (AoA) and determine that the complexities of selecting the RIGHT architecture for a given system application requires employment of models and simulations. Thus, we create Simulation 1 through Simulation n to provide the analytical basis for selecting the preferred architectural con guration.
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51.5 Application Examples of Modeling and Simulation
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Candidate Architecture #1 Entity Entity A A Entity Entity B B 3 Simulation Simulation #1 #1 6 5 Architecture Trade Study Architectural Selection Recommendations #1 Architecture #3 #2 Architecture #1 #3 Architecture #2
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Candidate Architecture #n Entity Entity A A Entity Entity B B 4 Simulation Simulation #n #n
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Figure 51.1 Simulation-Based Architecture Selection
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We exercise the simulations over a variety of OPERATING ENVIRONMENT scenarios and conditions. Results are analyzed and compiled and documented in an Architecture Trade Study. The Architecture Trade Study rank orders the results as part of its recommendations. Based on a review of the Architecture Trade Study, SEs select an architecture. Once the architecture is selected, the simulation serves as the framework for evaluation and re ning each simulated architectural entity at lower levels of abstraction.
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Application 2: Simulation-Based Architectural Performance Allocations
Modeling and simulation are also employed to perform simulation-based performance allocations as illustrated in Figure 51.2. Consider the following example:
Suppose that Requirement A describes and bounds Capability A. Our initial analysis derives three subordinate capabilities, A1 through A3, that are speci ed and bounded by Requirements A1 through A3: The challenge is: How do SEs allocate Capability A s performance to Capabilities A1 through A3 Let s assume that basic analysis provides us with an initial set of performance allocations that is in the ballpark. However, the interactions among entities are complex and require modeling and simulation to support performance allocation decision making. We construct a model of the Capability A s architecture to investigate the performance relationships and interactions of Entities A1 through A3. Next, we construct the Capability A simulation consisting of models, A1 through A3, representing subordinate Capabilities A1 through A3. Each supporting capability, A1 through A3, is modeled using the System Entity Capability Construct shown in Figure 22.1. The simulation is exercised for a variety of stimuli, cues, or excitations using Monte Carlo methods to understand the behavior of the interactions over a range of operating environment scenarios and conditions. The results of the interactions are captured in the system behavioral response characteristics.