Topology optimization under uncertainty via non-intrusive polynomial chaos expansions (INVITED)

Abstract eng:
This paper presents a systematic approach for topology optimization under uncertainty. The approach integrates an efficient non-intrusive polynomial chaos expansion with design sensitivities for reliability-based and robust topology optimization. Uncertainty is introduced in the loading and elemental densities to address the manufacturing variability. The manufacturing variability is represented via a random process with a Karhunen Loeve expansion on underlying uncertain parameters. To demonstrate the effect of uncertainty, the results of the optimization under uncertainty are compared with deterministic optimization results.

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