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Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space

机译：在缩小的参数空间中按指定目标设计对工程系统进行概率优化

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摘要

A novel probabilistic robust design optimization framework is presented using a Bayesian inference framework. The objective of the study is to obtain probabilistic descriptors of the system parameters conditioned on the user-prescribed target probability distributions of the output quantities of interest or figures of merit of a system. A criterion-based identification of a reduced important parameter space is performed from the typically high number of parameters modeling the stochastically parametrized physical system. The criterion can be based on sensitivity indices, design constraints or expert opinion or a combination of these. The posterior probabilities on the reduced or important parameters conditioned on prescribed target distributions of the output quantities of interest are derived using the Bayesian inference framework. The probabilistic optimal design proposed here offers the distinct advantage of prescribing probability bounds of the system performance functions around the optimal design points such that robust operation is ensured. The proposed method has been demonstrated with two numerical examples including the optimal design of a structural dynamic system based on user-prescribed target distribution for the resonance frequency of the system. (C) 2018 Elsevier B.V. All rights reserved.
机译：利用贝叶斯推理框架提出了一种新颖的概率鲁棒设计优化框架。该研究的目的是获得系统参数的概率描述符，该描述符以用户指定的目标输出量的目标概率分布或系统的品质因数为条件。通过对随机参数化物理系统进行建模的通常数量众多的参数，可以对减少的重要参数空间进行基于标准的识别。该标准可以基于敏感性指数，设计约束或专家意见或这些的组合。使用贝叶斯推断框架，可以得出以感兴趣的输出量的指定目标分布为条件的，减少的或重要的参数的后验概率。这里提出的概率最优设计提供了明显的优势，即在最优设计点附近规定了系统性能函数的概率范围，从而确保了稳健的操作。通过两个数值示例对提出的方法进行了演示，包括基于用户指定的系统共振频率目标分布对结构动力系统进行优化设计。 （C）2018 Elsevier B.V.保留所有权利。

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