Topology Optimization under Microscale Uncertainty using Stochastic Gradients

TL;DR

This paper introduces a stochastic gradient-based topology optimization method that efficiently accounts for microscale material uncertainties, enabling practical design of microstructured structures at the macroscale.

Contribution

It presents a novel stochastic gradient approach that reduces computational costs in topology optimization under microstructural uncertainty using limited microstructure samples.

Findings

Efficient gradient approximation reduces computational cost.

Method successfully applied to structural mechanics examples.

Enables design of microstructured materials considering uncertainty.

Abstract

This paper considers the design of structures made of engineered materials, accounting for uncertainty in material properties. We present a topology optimization approach that optimizes the structural shape and topology at the macroscale assuming design-independent uncertain microstructures. The structural geometry at the macroscale is described by an explicit level set approach, and the macroscopic structural response is predicted by the eXtended Finite Element Method (XFEM). We describe the microscopic layout by either an analytic geometric model with uncertain parameters or a level cut from a Gaussian random field. The macroscale properties of the microstructured material are predicted by homogenization. Considering the large number of possible microscale configurations, one of the main challenges of solving such topology optimization problems is the computational cost of estimating the statistical moments of the cost and constraint functions and their gradients with respect to the design variables. Methods for predicting these moments, such as Monte Carlo sampling, and Taylor series and polynomial chaos expansions often require many random samples resulting in an impractical computation. To reduce this cost, we propose an approach wherein, at every design iteration, we only use a small number of microstructure configurations to generate an independent, stochastic approximation of the gradients. These gradients are then used either with a gradient descent algorithm, namely Adam, or the globally convergent method of moving asymptotes (GCMMA). Three numerical examples from structural mechanics are used to show that the proposed approach provides a computationally efficient way for macroscale topology optimization in the presence of microstructural uncertainty and enables the designers to consider a new class of problems that are out of reach today with conventional tools.