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A hybrid descent mean value for accurate and efficient performance measure approach of reliability-based design optimization

机译:基于可靠性的设计优化的准确有效的性能度量方法的混合下降平均值

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摘要

The robustness and efficiency of performance measure approach (PMA) depend on the reliability loop in reliability-based design optimization (RBDO). For the reliability loop in the PMA using the minimum performance target point (MPTP) search, existing approaches can obtain stable results but may converge to inaccurate results, and higher computational efforts are required to achieve the optimum results for highly nonlinear problems. In this paper, a hybrid descent mean value (HDMV) approach is proposed based on a novel merit function, which is applied to combine the MPTP search formulas of the descent mean value (DMV) and advanced mean value (AMV). The merit function is used to adaptively control the numerical instability of the inverse reliability analysis for RBDO-based PMA. The accuracy, robustness and efficiency of the proposed DMV and HDMV methods are compared with existing methods through four nonlinear performance functions, two structural RBDO problems and a complex aircraft panel problem. The results illustrate that the DMV and HDMV methods are more robust, efficient and accurate than existing reliability methods. For the aircraft panel problem, a simultaneous buckling pattern is finally achieved by the proposed methods with better performance in terms of both convergence rate and computational efficiency. (C) 2018 Elsevier B.V. All rights reserved.
机译:性能评估方法(PMA)的鲁棒性和效率取决于基于可靠性的设计优化(RBDO)中的可靠性循环。对于使用最低性能目标点(MPTP)搜索的PMA中的可靠性环,现有方法可以获得稳定的结果,但可能会收敛到不准确的结果,并且需要更多的计算工作才能获得针对高度非线性问题的最佳结果。本文提出了一种基于新的优值函数的混合下降均值(HDMV)方法,该方法用于结合下降均值(DMV)和高级均值(AMV)的MPTP搜索公式。优点函数用于自适应控制基于RBDO的PMA的逆可靠性分析的数值不稳定性。通过四个非线性性能函数,两个结构性RBDO问题和一个复杂的飞机面板问题,将所提出的DMV和HDMV方法的准确性,鲁棒性和效率与现有方法进行了比较。结果表明,DMV和HDMV方法比现有的可靠性方法更健壮,高效和准确。对于飞机面板问题,通过收敛速度和计算效率方面都具有较好性能的方法,最终实现了同时屈曲模式。 (C)2018 Elsevier B.V.保留所有权利。

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