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Thursday, June 23, 2011

Modeling and Simulation Supports Entire Development Cycle

8.3 Modeling and Simulation
Modeling and simulation tools are used in all phases of system development from definition to end-of-life. Systems engineers are concerned with the models and simulations used in system definition, design selection and optimization, and performance verification. Systems engineers should identify the models and simulations needed for these tasks during the program planning phase so that any development of required models and simulations can be complete by the time they are needed. Examining the customer’s system requirements and the planned trade studies help identify the needed models and simulations. Parameter diagrams are often helpful in identifying the models and simulations needed.
Models constrained by requirements are typically adequate to be used in the system definition phase to define a baseline design concept. Models may be adequate to develop error budgets and allocations but performance simulations are often necessary to select and optimize designs.
System simulations and particularly performance simulations are especially useful in system performance verifications. Therefore it is necessary to include any necessary validation of system simulations in test plans and procedures. End-to-end system simulations are sometimes needed to verify final design compliance with requirements. Other uses include developing the requirements for data analysis tools needed during subsystem and system verification testing, reducing risk and time for developing test software and supporting troubleshooting during test and operational support.
Examples of how models and simulations might be used in system development are shown in Figure 8-4. In this figure the system under development is assumed to be a system that measures parameters by sampling the parameters that are related to a desired phenomenon that cannot be easily or economically measured directly. The measured samples are assumed to be processed first by Data Algorithms, which in this example produce calibrated data. The calibrated data are

Figure 8-4 Examples of ways models and simulations might be used in developing a sensor or measurement system.
then input to Product Algorithms which use the calibrated data to produce estimates of the desired phenomena.
It is assumed that a database of truth data is available. This truth data is used in two ways. It is used to predict the parameters that the system is designed to sample by using a model; called a Parameter Model in Figure 8-4. These predicted parameters are then the input to the System Model and System Simulation. The truth data is also used to assess the validity of the system model and the system simulation by comparing the results predicted by the Product Algorithms with the truth data. This example assumes that the System Model generates calibrated data and the System Simulation generates data that must be processed by the Data Algorithms to provide calibrated data. If truth data is available for the desired phenomenon during system operation then the truth data can be used to assess the performance of the system during operation as suggested by the figure.
It is assumed that Environmental Models are developed that can also generate the parameters to be measured. Information from the System Specification is used to generate the parameters in the desired range and with the desired statistics. If the database of truth measurements is representative of the specified range and statistics of the phenomena to be measured then the Parameter Model can be used to generate inputs for system design analysis and as comparisons for system test data analysis and comparisons. If no database of truth measurements is available then Environmental Models are used in place of the Parameter Model but it is not possible to assess results against truth data.
8.3.1 Performance Modeling and Simulation - System performance models and system performance simulations are used in trade studies to evaluate alternative designs and to iteratively optimize the selected design. Typically the system design objective is to develop the “best value” design solution. A “best value” design can be defined as:
·         Achieves performance above minimum thresholds
·         Has life cycle costs within customer’s or marketing’s defined cost limits
·         Meets requirements allocations (mass, power, etc.)
·         Assessed to be relatively low risk (so that cost targets are likely attainable)
A general approach to achieving a best value system design is to develop multiple design concepts, assess the cost and performance of each and iterate until the best value is achieved. This usually involves progressively lower level trade studies.
 Assessing the cost and performance of system design concepts requires analysis and state-of-the-art tools. Design tools for mechanical, thermal, electrical and optical analysis are well developed, widely available and indispensible for design of modern systems. The same cannot be said for the cost models and top level performance modeling and simulation tools for systems analysis. System performance modeling and simulation tools are too specialized for widespread utility. Thus most systems organizations must develop the modeling and simulation tools needed for defining their systems. Useful cost models are available for some systems for organizations developing systems for government agencies like the Defense Department and NASA. Examples of cost estimating models useful for several types of systems and cost estimating tasks include SEER ( and PRICE (
The first two steps in seeking a best value design are shown in Figure 8-5. The cost model is used to identify a number of design parameters that drive the system cost and quantify how the cost, or the relative cost, depends on each design parameter. The system performance modeling and simulation tools are used to quantify the dependence of system performance on each of the same design parameters.

Figure 8-5 Cost models and system performance models and simulations are used to determine the relationship of cost and performance on design parameters.
Having the relationships of cost and performance on design parameters these data can be combined to reveal how the selected design parameters drive the relationship of performance to relative life cycle as shown in Figure 8-6. Assuming the cost and performance relationships are determined for n design parameters then the result is n trades of cost vs. performance as a function of each of the n design parameters. It is usually straightforward to select the value of each design parameter that offers the best value design according to the desired criteria. For example, in Figure 8-6 the best value for the design parameter shown is 8 cm because it’s near the maximum of the linear portion of the parametric curve and it offers the best performance within the constraints on this particular design parameter.

Figure 8-6 The best value design is determined by combining the data from the cost model and performance models and simulations that determine how design parameters drive cost and performance.

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