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Stochastic Sensitivity Analysis for Robust Topology Optimization

Conference paper
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Abstract

Topology optimization under uncertainty poses extreme difficulty to the already challenging topology optimization problem. This paper presents a new computational method for calculating topological sensitivities of statistical moments of high-dimensional complex systems subject to random inputs. The proposed method, capable of evaluating stochastic sensitivities for large-scale, robust topology optimization (RTO) problems, integrates a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives. In addition, the statistical moments and their topology sensitivities are both determined concurrently from a single stochastic analysis. When applied in collaboration with the gradient based optimization algorithm, the proposed method affords the ability of solving industrial-scale RTO design problems. Numerical examples indicate that the new method developed provides computationally efficient solutions.

Keywords

Stochastic sensitivity analysis Polynomial dimensional decomposition Robust design topology optimization Topological derivatives 

Notes

Acknowledgments

The authors acknowledge financial support from the U.S. National Science Foundation under Grant No. CMMI-1635167 and the startup funding of Georgia Southern University.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringGeorgia Southern UniversityStatesboroUSA
  2. 2.State Key Laboratory of Bridge Engineering Structural DynamicsChina Merchant Chongqing Communications Research and Design Institute Co., Ltd.ChoingqingChina

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