A stochastic framework for computationally efficient fail-safe topology optimization

doi:10.1016/j.engstruct.2023.115831

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A stochastic framework for computationally efficient fail-safe topology optimization

2023-05-01

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DOI：10.1016/j.engstruct.2023.115831

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Abstract

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Fail-safe robustness is an important design philosophy for critical structural systems such as airframes. The basic idea of fail-safe design is that a structure should be designed to survive normal loading conditions when local damage occurs. In the context of topology optimization, fail-safe consideration innovates the morphogenesis for structural design in addition to the parametric safety margin. Existing fail-safe topology optimization methods usually enumerate all/predefined possible damages among the design domain and optimize the worst-damage scenario. However, virtually all damages can occur and cause the structural system to stop functioning. This work proposes the Stochastic Fail-Safe Topology Optimization (S-FSTO) for computationally viable fail-safe topology optimization which considers all possible damages according to the associated severity, including the worst-damage scenario. Critical damages for density value updates are sampled through the proposed two-level sampling strategy, which allows arbitrary size and location of damages. A design domain segmentation strategy is proposed to improve the sampling efficiency. The required number of FEA runs could be reduced by multiple orders of magnitudes. The proposed S-FSTO could be conveniently equivalent to a variety of existing schemes by customizing the proposal distributions of the two-level sampling strategy. The dependence on subjective decisions for selecting local damages could be minimized. The proposed framework has been evaluated for the design of cantilever beams and airplane bearing brackets. The generated conceptual designs are significantly more robust than the deterministic ones when local damage occurs. For the design of cantilever beams, S-FSTO produced designs with similar performance as that from the original FSTO and saved 99.4% FEA runs.

Keywords:

Fail-safe design

Robust design

Topology optimization

Design optimization

Design under uncertainty