An Adaptive Framework for Robust Structural Shape Optimization under Uncertainty

TL;DR

This paper introduces an adaptive framework for robust structural shape optimization under uncertainty, utilizing a posteriori error estimators to dynamically adjust sampling, meshing, and step sizes, improving optimization accuracy and efficiency.

Contribution

It presents a novel adaptive stochastic optimization method with error estimators for shape derivatives, enhancing robustness and precision in uncertain elasticity problems.

Findings

Effective in minimizing touchdown compliance under uncertain contact forces.

Adaptive adjustments improve convergence and accuracy.

Validated on leg-like structural components.

Abstract

This work proposes an adaptive framework to solve a robust structural shape optimization problem governed by linear elasticity models that account for uncertainties in the loading and material inputs. A posteriori error estimators are constructed to adjust the sample size, mesh size, and step length. The size of the sample set in the stochastic gradient approximation is dynamically determined depending on the variance of the shape derivative. When constructing the a posteriori error estimator in the physical domain, errors arising from the discretization of the deformation bilinear form, which provides a descent direction, are considered, in addition to errors from the discretization of the linear elasticity system. The step length in gradient-based optimization is also adaptively adjusted by estimating the Lipschitz constant of the stochastic shape derivative. Moreover, an analysis of the existence and distributed-form derivation of the stochastic shape derivative is provided. Finally, the proposed estimation-based adaptive stochastic optimization framework is validated on leg-like structural components, demonstrating its effectiveness in minimizing touchdown compliance under uncertain contact forces.