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author:

Cai, Zenan (Cai, Zenan.) [1] | Ou, Kai (Ou, Kai.) [2] | Wu, Yuheng (Wu, Yuheng.) [3] | Shen, Ying (Shen, Ying.) [4] | Yang, Yan (Yang, Yan.) [5] | Wang, Ya-Xiong (Wang, Ya-Xiong.) [6]

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EI

Abstract:

As the power of the automotive fuel cell increases, the traditional orbiting-type scroll compressor may not meet fuel cell cathode inlet air mass flow demand. To improve the air compressor discharge mass flow, the optimal structure and performance of a novel variable cross-section double-scroll compressor with co-rotating have been investigated. The effects of different structural parameters on the performance are analyzed by sensitivity analysis method. A multi-objective grey wolf optimization algorithm is applied to optimize the structural parameters of the scroll compressor. After that, the heat characteristics of the co-rotating air compressor initial and optimized designs are analyzed and verified by computational fluid dynamics method. The results exhibit that the optimized design structure could increase the mass flow rate and pressure ratio by 4.49 % and 10.53 %, respectively, while reducing the area utilization coefficient and total scroll teeth mass by 6.41 % and 10.77 %, respectively, compared to the initial design. The compressor could achieve optimal performance when the discharge pressure approaches the design pressure, with the maximum isentropic efficiency reaching 88.20 %. As the rotational speed and radial clearance improve, both the gas leakage rate and the discharge temperature of the scroll compressor increase. When the radial clearance is 0.075 mm, the leakage velocity is 21.33 % higher compared to a radial clearance of 0.030 mm. The research findings provide valuable insights for designing high-performance co-rotating scroll compressors, offering an efficient, compact, and reliable gas supply solution for high-power fuel cell systems. © 2025 Elsevier Ltd

Keyword:

Air Compressibility of gases Computational fluid dynamics Efficiency Fuel cells Leakage (fluid) Mass transfer Scroll compressors Sensitivity analysis Shape optimization Structural analysis Structural optimization

Community:

  • [ 1 ] [Cai, Zenan]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Ou, Kai]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Wu, Yuheng]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Shen, Ying]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Yang, Yan]Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing, 400054, China
  • [ 6 ] [Wang, Ya-Xiong]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

Energy

ISSN: 0360-5442

Year: 2025

Volume: 337

9 . 0 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 0

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