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Fluid-flow devices with low dissipation, but high contact area, are ofimportance in many applications. A well-known strategy to design such devicesis multi-scale topology optimization (MTO), where optimal microstructures aredesigned within each cell of a discretized domain. Unfortunately, MTO iscomputationally very expensive since one must perform homogenization of theevolving microstructures, during each step of the homogenization process. As analternate, we propose here a graded multiscale topology optimization (GMTO) fordesigning fluid-flow devices. In the proposed method, several pre-selected butsize-parameterized and orientable microstructures are used to fill the domainoptimally. GMTO significantly reduces the computation while retaining many ofthe benefits of MTO.  In particular, GMTO is implemented here using a neural-network (NN) since:(1) homogenization can be performed off-line, and used by the NN duringoptimization, (2) it enables continuous switching between microstructuresduring optimization, (3) the number of design variables and computationaleffort is independent of number of microstructure used, and, (4) it supportsautomatic differentiation, thereby eliminating manual sensitivity analysis.Several numerical results are presented to illustrate the proposed framework.

FluTO: Graded Multiscale Fluid Topology Optimization using Neural Networks