Stochastic Sensitivity Analysis for Robust Topology Optimization
Contribution to Book
Advances in Structural and Multidisciplinary Optimization: Proceedings of the World Congress of Structural and Multidisciplinary Optimization
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.
Ren, Xuchun, Xiaodong Zhang.
"Stochastic Sensitivity Analysis for Robust Topology Optimization."
Advances in Structural and Multidisciplinary Optimization: Proceedings of the World Congress of Structural and Multidisciplinary Optimization (1st Edition), Axel Schumacher, Thomas Vietor, Sierk Fiebig, Kai-Uwe Bletzinger, and Kurt Maute (Ed.): 334-346 Cham, Switzlerand: Springer International Publishing.
doi: 10.1007/978-3-319-67988-4_26 isbn: 978-3-319-67988-4