An enhanced data-driven polynomial chaos method for uncertainty propagation. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Wang, Fenggang; Xiong, Fenfen; Jiang, Huan; Song, Jianmei
• Is Part Of: Engineering optimization. Volume 50:Issue 2(2018)
• Page Start: 273
• Page End: 292
• Publication Date: 2018-02-01
• Subjects: Uncertainty propagation -- polynomial chaos -- data-driven -- Galerkin projection -- trajectory optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1323890
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: As a novel type of polynomial chaos expansion (PCE), the data-driven PCE (DD-PCE) approach has been developed to have a wide range of potential applications for uncertainty propagation. While the research on DD-PCE is still ongoing, its merits compared with the existing PCE approaches have yet to be understood and explored, and its limitations also need to be addressed. In this article, the Galerkin projection technique in conjunction with the moment-matching equations is employed in DD-PCE for higher-dimensional uncertainty propagation. The enhanced DD-PCE method is then compared with current PCE methods to fully investigate its relative merits through four numerical examples considering different cases of information for random inputs. It is found that the proposed method could improve the accuracy, or in some cases leads to comparable results, demonstrating its effectiveness and advantages. Its application in dealing with a Mars entry trajectory optimization problem further verifies its effectiveness.