Non-intrusive polynomial chaos expansion for topology optimization using polygonal meshes

TL;DR

This paper introduces a non-intrusive polynomial chaos expansion method integrated into topology optimization with polygonal meshes, effectively accounting for load uncertainties to produce reliable structural designs.

Contribution

It presents a novel probabilistic topology optimization approach using polygonal elements and polynomial chaos to handle load uncertainties efficiently.

Findings

Accurate uncertainty propagation matching Monte Carlo results

Polygonal meshes reduce checkerboard patterns and mesh dependency

Load uncertainties significantly influence optimal structural designs

Abstract

This paper deals with the applications of stochastic spectral methods for structural topology optimization in the presence of uncertainties. A non-intrusive polynomial chaos expansion is integrated into a topology optimization algorithm to calculate low-order statistical moments of the mechanical-mathematical model response. This procedure, known as robust topology optimization, can optimize the mean of the compliance while simultaneously minimizing its standard deviation. In order to address possible variabilities in the loads applied to the mechanical system of interest, magnitude and direction of the external forces are assumed to be uncertain. In this probabilistic framework, forces are described as a random field or a set of random variables. Representation of the random objects and propagation of load uncertainties through the model are efficiently done through Karhunen-Lo\`{e}ve and polynomial chaos expansions. We take advantage of using polygonal elements, which have been shown to be effective in suppressing checkerboard patterns and reducing mesh dependency in the solution of topology optimization problems. Accuracy and applicability of the proposed methodology are demonstrated by means of several topology optimization examples. The obtained results, which are in excellent agreement with reference solutions computed via Monte Carlo method, show that load uncertainties play an important role in optimal design of structural systems, so that they must be taken into account to ensure a reliable optimization process.