On convergence speedup in topology optimization (INVITED)

Abstract eng:
This paper introduces a simple-to-implement, multiscale-inspired approach to improve convergence speed in topology optimization. To ensure convergence toward globally optimal Michell-like structures, topology optimization approaches often apply continuation schemes where e.g. the penalization exponent is increased gradually. In this way, one nudges the process by going from an initially convex problem (variable thickness sheet) to a penalized, black and white solution. Iteration counts for such continuation approaches are usually counted in many hundreds or up to thousands. By introducing an extra constraint that limits the p-norm of the difference between the local density field and a smoothed (homogenized) one, the continuation scheme can be eliminated. It is demonstrated that this approach systematically creates extremely detailed and highly optimized Michell-like structures within at most 200 iterations.

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