Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimiza...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 269; p. 126729
Main Authors: Zhang, Tianyi, Chen, Lei, Wang, Jin
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.04.2023
Subjects:
CFD
Genetic algorithm
Multi-objective optimization
Neural networks
CFD
Multi-objective optimization
Genetic algorithm
Neural networks
ISSN:0360-5442
Online Access:Get full text
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Summary:The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimization variables. In order to obtain the optimal heat transfer performance and pressure drop performance, Computational Fluid Dynamics (CFD) simulation is carried out for different Reynolds based on the hydraulic diameter numbers (150–750) and tube ellipticity (0.2–1). Then use simulation data to train the Back-Propagation neural networks and establish the prediction model of heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic algorithm with elitist retention strategy (NSGA-II) is used to optimize two prediction results of NNs. Finally, the optimal heat transfer coefficient and pressure drop are given in the form of Pareto front. The optimization results show that when the Reynolds number is 541 and the ellipticity is 0.34, the pressure drop of the TFHE decreases 20%, and the heat transfer coefficient is basically unchanged, whose j/f is 1.28 times as much as that of the original heat exchanger. •Multi objective optimization is used to optimize tube fin heat exchangers.•Neural networks is used to predict the performance of heat exchangers.•The Pareto optimal frontier of heat exchangers optimization is obtained.•The pressure drop of the optimized heat exchanger is decreased by 20%.
ISSN:0360-5442
DOI:10.1016/j.energy.2023.126729