NDLI: Quantile-based optimization under uncertainties using adaptive Kriging surrogate models

Content Provider	SpringerLink
Author	Moustapha, Maliki Sudret, Bru Bourinet, Jean Marc Guillaume, Beît
Copyright Year	2016
Abstract	Uncertainties are inherent to real-world systems. Taking them into account is crucial in industrial design problems and this might be achieved through reliability-based design optimization (RBDO) techniques. In this paper, we propose a quantile-based approach to solve RBDO problems. We first transform the safety constraints usually formulated as admissible probabilities of failure into constraints on quantiles of the performance criteria. In this formulation, the quantile level controls the degree of conservatism of the design. Starting with the premise that industrial applications often involve high-fidelity and time-consuming computational models, the proposed approach makes use of Kriging surrogate models (a.k.a. Gaussian process modeling). Thanks to the Kriging variance (a measure of the local accuracy of the surrogate), we derive a procedure with two stages of enrichment of the design of computer experiments (DoE) used to construct the surrogate model. The first stage globally reduces the Kriging epistemic uncertainty and adds points in the vicinity of the limit-state surfaces describing the system performance to be attained. The second stage locally checks, and if necessary, improves the accuracy of the quantiles estimated along the optimization iterations. Applications to three analytical examples and to the optimal design of a car body subsystem (minimal mass under mechanical safety constraints) show the accuracy and the remarkable efficiency brought by the proposed procedure.
Starting Page	1403
Ending Page	1421
Page Count	19
File Format	PDF
ISSN	1615147X
Journal	Structural and Multidisciplinary Optimization
Volume Number	54
Issue Number	6
e-ISSN	16151488
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-07-08
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Quantile-based design optimization RBDO Kriging Adaptive design of experiments Theoretical and Applied Mechanics Computational Mathematics and Numerical Analysis Engineering Design
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Graphics and Computer-Aided Design Software Computer Science Applications Control and Optimization

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