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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.