Systematic Reliability-Based Multidisciplinary Optimization by Parallel Adaptive Importance Candidate Region

Abstract

Reliability-based design optimization (RBDO) has become a prevalent design for aeronautical and aerospace engineering. The main problem is that it is impractical in complex cases with multi-failure regions, especially in multi-objective optimization. The active learning method can obtain an adaptive size of samples to get a relatively acceptable accuracy. The problem of RBDO using the traditional active learning Kriging (ALK) method is that the design space is generally still and only one training point is selected, which is not reasonable based on the concept of importance sampling and parallel calculation. As a consequence, the accuracy improvement is limited. In this paper, we investigate the method of obtaining an optimal size of design and reliability to assess space in parallel, simultaneously. A strategy of parallel adaptive candidate (PAIC) region with ALK is proposed and a sequential optimization and reliability assessment (SORA) method is modified to efficiently improve the accuracy. Importance sampling is used as a demonstration for the modified SORA with more accuracy. The method is then verified using mathematical cases and a scooping system of an amphibious aircraft.