Multi-Objective Optimization towards Heat Dissipation Performance of the New Tesla Valve Channels with Partitions in a Liquid-Cooled Plate

Liang Xu, Hongwei Lin, Naiyuan Hu, Lei Xi (), Yunlong Li and Jianmin Gao
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Liang Xu: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Hongwei Lin: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Naiyuan Hu: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Lei Xi: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yunlong Li: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jianmin Gao: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2024, vol. 17, issue 13, 1-21

Abstract: The utilization of liquid-cooled plates has been increasingly prevalent within the thermal management of batteries for new energy vehicles. Using Tesla valves as internal flow channels of liquid-cooled plates can improve heat dissipation characteristics. However, conventional Tesla valve flow channels frequently experience challenges such as inconsistencies in heat dissipations and unacceptably high levels of pressure loss. In light of this, this paper proposes a new type of Tesla valve with partitions, which is used as internal channel for liquid-cooled plate. Its purpose is to solve the shortcomings of existing flow channels. Under the working conditions of Reynolds number equal to 1000, the neural network prediction-NSGA-II multi-objective optimization method is used to optimize the channel structural parameters. The objective is to identify the optimal structural configuration that exhibits the greatest Nusselt number while simultaneously exhibiting the lowest Fanning friction factor. The variables to consider are the half of partition thickness H, partition length L, and the fillet radius R. The study result revealed that the optimal parameter combination consisted of H = 0.25 mm, R = 1.253 mm, L = 0.768 mm, which demonstrated the best performance. The Fanning friction factor of the optimized flow channel is substantially reduced compared to the reference channel, reducing by approximately 16.4%. However, the Nusselt number is not noticeably increased, increasing by only 0.9%. This indicates that the optimized structure can notably reduce the fluid’s friction resistance and pressure loss and slightly enhance the heat dissipation characteristics.

Keywords: liquid-cooled plates; Tesla valves; neural network; NSGA-II (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: 2024
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