Deep learning-based optimization of a microfluidic membraneless fuel cell for maximum power density via data-driven three-dimensional multiphysics simulation.

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Abstract

A deep learning-based method for optimizing a membraneless microfluidic fuel cell (MMFC)performance by combining the artificial neural network (ANN) and genetic algorithm (GA) was for the first time introduced. A three-dimensional multiphysics model that had an accuracy equivalent to experimental results (R2 = 0.976) was employed to generate the ANN’s training data. The constructed ANN is equivalent to the simulation (R2 = 0.999) but with far better computation resource efficiency as the ANN’s execution time is only 0.041s. The ANN model is then used by the GA to determine the inputs (microchannel length = 10.040 mm, width = 0.501 mm, height = 0.635 mm; temperature = 288.210 K, cell voltage = 0.309 V) that lead to the maximum power density of 0.263 mWcm-2 (current density of 0.852 mAcm-2) of the MMFC. The percentage error between the ANN-GA and numerically calculated maximum power densities differed only by 0.766%.Copyright © 2022 Elsevier Ltd. All rights reserved.