Machine Learning Assisted Microchannel Geometric Optimization—A Case Study of Channel Designs

Long Huang (), Junjia Zou, Baoqing Liu, Zhijiang Jin and Jinyuan Qian
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Long Huang: Institute of Wenzhou, Zhejiang University, Wenzhou 310027, China
Junjia Zou: School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
Baoqing Liu: Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Zhijiang Jin: Institute of Wenzhou, Zhejiang University, Wenzhou 310027, China
Jinyuan Qian: Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2023, vol. 17, issue 1, 1-20

Abstract: At present, microchannel heat exchangers are widely applied in the fields of air-conditioning and heat pumping applications given their high heat transfer performance, compact size, and low material cost. However, designing and optimizing the channel geometries remain challenging, as they require balancing multiple competing objectives to achieve the optimal performance. This study investigates various parameters, including the channel count, wetted perimeter, cross-sectional area, and mass flow rate for each channel, to achieve the optimal efficiency. The optimization objectives include maximizing the heat transfer rate, minimizing the refrigerant convective thermal resistance, maximizing the refrigerant heat transfer coefficient, and minimizing the pressure drop. A multi-objective genetic optimization algorithm, in conjunction with artificial neural network (ANN)-based machine learning models, was used to predict the heat transfer rate to speed up the calculation process during the optimization. We identified that a gradient reduction in the wetted perimeter from the air inlet along the airflow direction could enhance the heat transfer rate. Additionally, the results indicate that an increase in the number of channels leads to an enhanced heat transfer efficiency rate. However, with the increase in the number of channels, the cross-sectional area of each channel is correspondingly reduced to maintain a consistent overall cross-sectional area. This reduction increases the fluid resistance, leading to an increased pressure drop across the system. This observation is critical for a microchannel design optimization, highlighting the importance of attaining a balance between achieving a higher heat transfer efficiency and maintaining a favorable fluid dynamic performance.

Keywords: microchannel heat exchanger; multi-objective optimization; heat transfer enhancement; artificial neural networks; flow distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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