Stress-based topology optimization under uncertainty via simulation-based Gaussian process

Abstract Gaussian processes (GP) form a well-established predictive tool which provides a natural platform for tackling high-dimensional random input data in challenging simulations. This paper introduces a generic framework for integrating Gaussian Processes with risk-based structural optimization. We solve robust and reliability-based design problems in the context of stress-based topology optimization under imperfections in geometry and material properties, and loading variability. We construct independent GPs for primal and adjoint quantities, namely the global maximum von Mises stress and its sensitivity where we enhance the computational efficiency by leveraging the information from multiresolution finite element simulations. The GP framework naturally lends itself to modeling noise in data. We investigate the effect of numerical modeling error in high-fidelity simulations via a noisy GP emulator and provide a pareto curve that shows the robustness of optimal design with respect to the noise level. We provide a posteriori error estimates that quantify the discrepancy between the noisy emulator and true simulations, and verify them with a numerical study. We demonstrate our approach on a benchmark L-shape structure which exhibits stress concentration, a compliant mechanism design and a heat sink design. We also provide practical guidelines for estimation of failure probability and its sensitivity to facilitate reliability-based topology optimization.

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