Generating 3D Topologies with Multiple Constraints on the GPU

1. Abstract The objective of this paper is to demonstrate a topology optimization method that can handle multiple constraints. The method relies on the concept of topological sensitivity that captures the first order change in any quantity of interest to a topological change. Specifically, in this paper, the topological sensitivity field for each of constraints is first computed. These fields are then dynamically combined to result in a single topological level-set. Finally, by relying on a fixed-point iteration, the topological level-set leads to optimal topologies (with decreasing volume fractions) that satisfy the constraints. Since the method relies on an assembly-free finite-element analysis, it is parallelization-friendly, and can be easily ported to the GPU, as demonstrated through examples in 3D.

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