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contributor authorDrouet, Vincent
contributor authorBalesdent, Mathieu
contributor authorBrevault, Loïc
contributor authorDubreuil, Sylvain
contributor authorMorio, Jérôme
date accessioned2023-11-29T19:29:59Z
date available2023-11-29T19:29:59Z
date copyright5/17/2023 12:00:00 AM
date issued5/17/2023 12:00:00 AM
date issued2023-05-17
identifier issn1050-0472
identifier othermd_145_7_071703.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294813
description abstractThe present article proposes an algorithm for the sensitivity analysis of a multidisciplinary problem, in which the derivative-based global sensitivity indices are computed with multifidelity Gaussian process models. Two levels of fidelity are used to estimate the indices, where the low-fidelity samples are obtained by stopping the multidisciplinary analysis solver before convergence. A dedicated refinement strategy for the multifidelity Gaussian process is proposed to ensure the accuracy of the sensitivity index estimation. This algorithm is tested on three multidisciplinary problems of increasing complexity (one analytical and two representative engineering design problems), and proved to be both reliable in detecting the noninfluential variables and computationally efficient, compared to classical Monte Carlo integration and to three other candidate algorithms.
publisherThe American Society of Mechanical Engineers (ASME)
titleMultifidelity Algorithm for the Sensitivity Analysis of Multidisciplinary Problems
typeJournal Paper
journal volume145
journal issue7
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4062332
journal fristpage71703-1
journal lastpage71703-13
page13
treeJournal of Mechanical Design:;2023:;volume( 145 ):;issue: 007
contenttypeFulltext


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