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Robust Topology Design Optimization Based on Dimensional Decomposition Method

Ren, Xuchun; Zhang, Xiaodong (January 2019, Mechanics and Engineering – Numerical Calculation and Data analysis , 2019 in Beijing)

Abstract This paper presents an efficient approach for robust topology design optimization (RTO) which is based on polynomial dimensional decomposition (PDD) method. The level-set functions are adopted to facilitate the topology changes and shape variations. The topological derivatives of the functionals of robustness root in the concept of deterministic topological derivatives and dimensional decomposition of stochastic responses of multiple random inputs. The PDD for calculating robust topological derivatives consists of only a number of evaluations of the deterministic topological derivatives at the specified points in the stochastic space and provides effective and efficient design sensitivity analyses for RTO. The numerical examples demonstrate the effectiveness of the present method.
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Stochastic design optimization accounting for structural and distributional design variables

https://doi.org/10.1108/EC-10-2017-0409

Ren, Xuchun; Rahman, Sharif (November 2018, Engineering Computations)

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Stochastic Sensitivity Analysis for Robust Topology Optimization

https://doi.org/10.1007/978-3-319-67988-4_26

Ren, Xuchun; Zhangm, Xiaodong (December 2017, World Congress of Structural and Multidisciplinary Optimisation WCSMO 2017: Advances in Structural and Multidisciplinary Optimization)

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