Optimising shapes of multiple pin fins in a microchannel using deep reinforcement learning and mesh deformation techniques

The utilisation of advanced pin fin designs in microchannels is useful for enhancing cooling efficiency. Advancements in machine learning and processing power have sparked interest in shape optimisation techniques. This research employs a novel framework that integrates Deep Artificial Neural Networks and Reinforcement Learning with a Computational Fluid Dynamics (CFD) solver to optimise multiple pin fin shapes within a microchannel. By incorporating Radial Basis Function interpolation and Proximal Policy Optimisation alongside FLUENT, acting as the CFD environment, the reinforcement learning agent adeptly explores the design space to enhance the thermohydraulic performance factor (TPF), aiming to maximise Nusselt number while minimising pressure loss. Unlike previous heat transfer optimisation studies, which typically required mesh regeneration at each step, the proposed framework could bypass the meshing step and alter the geometry directly by relying on the RBF interpolation technique to deform the mesh directly. Three distinct scenarios investigated in this study are uniform deformation of all pin fins, deformation of all pin fins arranged in two rows, and individual deformation of each pin fin. Extensive simulations, exceeding 90,000 different cases, demonstrate that although the optimisation process for the individual deformation of each pin fin requires more iterations compared to others, it surpasses them in terms of TPF improvement. Notably, significant improvements are achieved, such as a 49 % enhancement in Nusselt number and a 33 % reduction in pressure drop, culminating in an impressive 63 % increase in TPF compared to the initial geometry.