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A multi-constraint failure-pursuing sampling method for reliability-based design optimization using adaptive Kriging
Engineering with Computers ( IF 8.7 ) Pub Date : 2020-08-31 , DOI: 10.1007/s00366-020-01135-3
Xiaoke Li , Xinyu Han , Zhenzhong Chen , Wuyi Ming , Yang Cao , Jun Ma

Using surrogate models to substitute the computationally expensive limit state functions is a promising way to decrease the cost of implementing reliability-based design optimization (RBDO). To train the models efficiently, the active learning strategies have been intensively studied. However, the existing learning strategies either do not individually build the models according to importance measurement or do not completely relate to the reliability analysis results. Consequently, some points that are useless to refine the limit state functions or far away from the RBDO solutions are generated. This paper proposes a multi-constraint failure-pursuing sampling method to maximize the reward of adding new training points. A simultaneous learning strategy is employed to sequentially update the Kriging models with the points selected in the current approximate safe region. Moreover, the sensitive Kriging model as well as the sensitive sample point are identified based on the failure-pursuing scheme. A new point that is highly potential to improve the accuracy of reliability analysis and optimization can then be generated near the sensitive sample point and used to update the sensitive model. Besides, numerical examples and engineering application are used to validate the performance of the proposed method.

中文翻译:

使用自适应克里金法进行基于可靠性的设计优化的多约束故障追踪采样方法

使用替代模型来替代计算成本高的极限状态函数是降低实现基于可靠性的设计优化 (RBDO) 成本的一种很有前景的方法。为了有效地训练模型,已经深入研究了主动学习策略。然而,现有的学习策略要么没有根据重要性度量单独构建模型,要么与可靠性分析结果不完全相关。因此,会生成一些对细化极限状态函数无用或远离 RBDO 解的点。本文提出了一种多约束失败追踪采样方法,以最大化增加新训练点的奖励。采用同步学习策略,用当前近似安全区域中选择的点顺序更新克里金模型。此外,基于故障追踪方案识别敏感克里金模型以及敏感样本点。然后可以在敏感样本点附近生成一个很有可能提高可靠性分析和优化准确性的新点,并用于更新敏感模型。此外,数值算例和工程应用被用来验证所提出方法的性能。然后可以在敏感样本点附近生成一个很有可能提高可靠性分析和优化准确性的新点,并用于更新敏感模型。此外,数值算例和工程应用被用来验证所提出方法的性能。然后可以在敏感样本点附近生成一个很有可能提高可靠性分析和优化准确性的新点,并用于更新敏感模型。此外,数值算例和工程应用被用来验证所提出方法的性能。
更新日期:2020-08-31
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