基于响应带的时变可靠性稳健设计优化方法

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Li Lu, Yizhong Wu, Qi Zhang, Zhehao Xia, Ping Qiao
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引用次数: 0

摘要

提出了一种基于响应带的时变可靠性稳健设计优化方法。该方法为解决时变可靠性鲁棒优化问题提供了一种新的替代框架,该框架由两步转换阶段和求解阶段组成。在转化阶段将原时变可靠性稳健设计优化问题转化为等效确定性稳健设计优化问题,在求解阶段求解等效问题。在变换阶段,将动态模态分解技术与克里金技术相结合,克服了常用变换方法对时分和观测采样都没有标准的问题。在求解阶段,提出了一种构建目标函数响应带的方法,大大减少了目标函数变分评估的计算量。通过5个实例验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A response band-based method for time-dependent reliability-based robust design optimization
In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness of the proposed method.
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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