RT Journal Article
T1 Robust optimization of heat-transfer-enhancing microtextured surfaces based on machine learning surrogate models
A1 Larrañaga Janeiro, Ana
A1 Martínez, J.
A1 Míguez Tabarés, José Luis
A1 Porteiro Fresco, Jacobo
K1 3312 Tecnología de Materiales
AB 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.
PB International Communications in Heat and Mass Transfer
SN 07351933
YR 2024
FD 2024-02
LK http://hdl.handle.net/11093/6382
UL http://hdl.handle.net/11093/6382
LA eng
NO International Communications in Heat and Mass Transfer, 151, 107218 (2024)
NO Universidade de Vigo
DS Investigo
RD 18-sep-2024