Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses. (April 2020)

• Record Type: Journal Article
• Authors: Menz, Morgane; Gogu, Christian; Dubreuil, Sylvain; Bartoli, Nathalie; Morio, Jérôme
• Is Part Of: Reliability engineering & system safety. Volume 196(2020)
• Publication Date: 2020-04
• Subjects: Reliability analysis -- Kriging -- Adaptive approaches -- Finite elements -- Reduced order modeling -- Reduced basis; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2019.106771
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Kriging based adaptive sampling reliability analyses approaches are investigated. A multifidelity approach is proposed by a coupling with reduced basis modeling. Coupling criteria based on residuals and preconditioned residuals investigated. Two applications considered to thermal and mechanical problems. Abstract: Running a reliability analysis on engineering problems involving complex numerical models can be computationally very expensive. Hence, advanced methods are required to reduce the number of calls to the expensive computer codes. Adaptive sampling based reliability analysis methods are one promising way to reduce computational costs. Reduced order modelling is another one. In order to further reduce the numerical costs of Kriging based adaptive sampling approaches, the idea developed in this paper consists in coupling both approaches by adaptively deciding whether to use reduced-basis solutions in place of full numerical solutions whenever the performance function needs to be assessed. Thus, a method combining such adaptive sampling based reliability analyses and reduced basis modeling is proposed using on an efficient coupling criterion. The proposed method enabled significant computational cost reductions, while ensuring accurate estimations of failure probabilities.