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Sensitivity Analysis and Uncertainty Quantification of a Mars Ascent Vehicle Concept

Show simple item record Noyes, Connor Benito, Joel Peterson, Lee D. 2019-07-02T18:34:22Z 2019-07-02T18:34:22Z 2017-03-04
dc.identifier.citation 2017 IEEE Aerospace Conference, Big Sky, Montana, March 4-11, 2017 en_US
dc.identifier.clearanceno CL#17-0319
dc.description.abstract The design of a conceptual Mars ascent vehicle is a challenging problem. In order to aid the vehicle and mission concept design it is important to understand the driving design parameters and the expected performance in the presence of model errors and uncertainties. An existing six degree of freedom simulation model is analyzed on a statistical basis using the methods available in the Design Analysis Kit for Optimization and Terascale Applications toolkit. The methods utilized include conventional Monte Carlo techniques, metamodeling via polynomial chaos expansions, and global variance-based sensitivity analyses. Two additional analysis methods referred to as “Monte Carlo filtering” and ”Classification trees” are used to determine which uncertain parameters are driving the performance of the vehicle. Monte Carlo filtering provides a methodology to determine which parameters cause qualitatively different behavior while the classification trees use heuristics to partition the input space and assign probabilities to each partition. These methods serve as qualitative descriptors of model sensitivity while variance-based global sensitivity analysis seeks a quantitative mapping from total output variance to the variance of individual inputs. Application of these techniques to several outputs of a Mars ascent vehicle concept simulation indicates that only a select few input factors dominate their variance. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2017 en_US
dc.title Sensitivity Analysis and Uncertainty Quantification of a Mars Ascent Vehicle Concept en_US
dc.type Preprint en_US

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