Robust optimization of heat-transfer-enhancing microtextured surfaces based on machine learning surrogate models

Abstract

Currently, there is a growing industry-wide focus on enhancing the thermohydraulic performances of devices, with the goal of achieving more efficient energy management. However, due to manufacturing constraints, there has been a tendency to focus on simple geometric designs. Recently, the emergence of additive manufacturing techniques and advancements in artificial intelligence have enabled new possibilities in this field. In this work, a pioneering exploration of a novel methodology for optimization of microfins to enhance the heat transfer on flat surfaces is presented. The optimization is performed using data-driven algorithms to accelerate the evaluation of the performance of these surfaces; these algorithms are trained on a database of 15,694 numerical simulations of enhanced surfaces. The performance evaluation criterion (PEC), equal to the ratio between the thermal and hydraulic performance parameters of the geometry, is used as the objective function. To avoid an optimization that focuses solely on the compactness of the fins while ignoring their shape, the optimal geometry is sought, which proves to be a challenge. Hence, an optimization method that classifies the surfaces based on their periodicity is proposed, focusing on improving the performance in terms of the morphology. Results present a PEC augmentation range from +0.08 to +0.28.