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New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties

机译:结合模糊和概率模型的基于混合可靠性的拓扑优化新方法,用于处理认知和不确定性不确定性

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This study presents a hybrid reliability-based topology optimization (RBTO) method for handling epistemic and aleatory uncertainties. First, we establish a new triple-nested RBTO model based on fuzzy and probabilistic theory for describing the multi-source uncertainties. Subsequently, an efficient single-loop optimization method is proposed to degrade the triple-nested optimization problem into a deterministic optimization problem using the Karush-Kuhn-Tucker optimality condition. Furthermore, the sensitivities of the hybrid reliability constraint with respect to the random probabilistic variables, fuzzy variables, and deterministic design variables are derived using the adjoint variable method. Finally, a cantilever beam example, an L-shape beam design and a 3D example are tested to verify the validity of the proposed single-loop method. (C) 2020 Elsevier B.V. All rights reserved.
机译:这项研究提出了一种基于混合可靠性的拓扑优化(RBTO)方法来处理认知和偶然的不确定性。首先,我们基于模糊和概率理论建立了一个新的三嵌套RBTO模型,用于描述多源不确定性。随后,提出了一种有效的单循环优化方法,该方法使用Karush-Kuhn-Tucker最优性条件将三重嵌套优化问题退化为确定性优化问题。此外,使用伴随变量方法推导了混合可靠性约束对随机概率变量,模糊变量和确定性设计变量的敏感性。最后,通过悬臂梁示例,L形梁设计和3D示例进行测试,以验证所提出的单环方法的有效性。 (C)2020 Elsevier B.V.保留所有权利。

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