Abstract
This paper presents the Regression-Based global Sensitivity Analysis method (RBSA). It is an approach for quantitative, variance-based, sensitivity analysis of mathematical models used for design purposes. The method is based on the subdivision of the global variance into its components, due to the design-factor contributions, using general polynomial regression models. The performance of the RBSA is compared to other methods commonly used in engineering for computing sensitivity, namely, the method of Sobol’, the Fourier amplitude sensitivity test, the method of Morris, and the standardized regression coefficients. It was found that RBSA, under certain circumstances, provides very accurate results with a significant reduction of the number of required model evaluations. A test case, using the mathematical models of two subsystems of a spacecraft, demonstrates how RBSA facilitates the discovery and understanding of the effects of the design choices on the performance of the system.
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Appendix: Communication and Power Subsystem
Appendix: Communication and Power Subsystem
In Table 9 we describe the settings of the discrete variables used for the analysis of the communication and power subsystem. Further, the analysis of the communication and power subsystems cannot be performed considering them as separate from the other subsystems of the satellite and irrespectively of the orbit that the satellite will undergo. Some boundary conditions need to be set. In Table 10, the settings of all the parameters that significantly influence the performances of the communication and power subsystems are presented.
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Ridolfi, G., Mooij, E. (2016). Regression-Based Sensitivity Analysis and Robust Design. In: Fasano, G., Pintér, J.D. (eds) Space Engineering. Springer Optimization and Its Applications, vol 114. Springer, Cham. https://doi.org/10.1007/978-3-319-41508-6_12
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