Engineering of polydisperse porous media for enhanced fluid flows through systematic topology tuning via differentiable direct numerical simulation

Recent advancements in automatic differentiation, which played a pivotal role in deep learning, offer a promising approach to addressing challenges in controlling fluid flow behavior. We demonstrate the power of the method by optimizing the packing of a polydisperse system of periodically arranged circular rods to minimize the pressure drop across the media. We show how the optimum topology of the porous media changes with changing the packing fraction.