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NON-PROBABILISTIC BASED TOPOLOGY OPTIMIZATION UNDER EXTERNAL LOAD UNCERTAINTY WITH EIGENVALUE-SUPERPOSITION OF CONVEX MODELS

机译:具有凸模型特征值叠加的外部载荷不确定性下基于非概率的拓扑优化

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In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.
机译:本文提出了一种基于凸模型特征值叠加(ESCM)的拓扑优化方法,以解决外部载荷不确定性下的拓扑优化问题。使用基于非概率的未知但有界的凸模型来公式化负载不确定性。得出灵敏度并使用基于梯度的算法解决问题。所提出的基于ESCM的方法产生的材料分布将在不确定载荷下优化最差的结构响应。与基于确定性的拓扑优化公式相比,当涉及负载不确定性时,基于ESCM的方法提供了更合理的解决方案。所提出的基于ESCM的拓扑优化方法的简单性,效率和多功能性可以视为对复杂的基于可靠性的拓扑优化方法的补充。

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