Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events

Abstract

In practical engineering, especially in aeronautical engineering, the failure probability is extremely rare due to the incorporation of safety factors in the mechanical design phase. Consequently, a significant challenge is to assess the reliability of mechanical products with implicit functions and rare failure events. To address this issue, this work presents a parallel adaptive ensemble of metamodels (EM) coupled with hypersphere sampling strategy to improve the accuracy and efficiency of reliability analysis. The proposed method consists of three main features. First, a new heuristic ensemble strategy is proposed to provide a powerful and robust metamodel. Second, a n-dimensional uniform sampling technique with better space-filling ability is taken to improve efficiency, which leads to a decrease in the extensive sample size required to capture rare failure events. Third, an effective parallel enrichment strategy is developed by the proposed pseudo-improved U learning function. When parallel computation is possible, the proposed method can select a batch of informative updated points simultaneously to update the EM. Three numerical examples and a planar ten-bar structure are presented to demonstrate the accuracy and efficiency of the proposed method. This method is also applied to the reliability assessment of the aircraft lock mechanism.