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In this paper, we present a framework for multiscale topology optimization offluid-flow devices. The objective is to minimize dissipated power, subject to adesired contact-area. The proposed strategy is to design optimalmicrostructures in individual finite element cells, while simultaneouslyoptimizing the overall fluid flow. In particular, parameterized super-shapemicrostructures are chosen here to represent microstructures since they exhibita wide range of permeability and contact area. To avoid repeatedhomogenization, a finite set of these super-shapes are analyzed a priori, and avariational autoencoder (VAE) is trained on their fluid constitutive properties(permeability), contact area and shape parameters. The resulting differentiablelatent space is integrated with a coordinate neural network to carry out aglobal multi-scale fluid flow optimization. The latent space enables the use ofnew microstructures that were not present in the original data-set. Theproposed method is illustrated using numerous examples in 2D.

TOMAS: Topology Optimization of Multiscale Fluid Devices using Variational Autoencoders and Super-Shapes