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An inquiry into transport phenomena and artificial intelligence-based optimization of a novel bio-inspired flow field for proton exchange membrane fuel cells

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  • Ghanbari, Sina
  • Ghasabehi, Mehrdad
  • Asadi, Mohammad Reza
  • Shams, Mehrzad

Abstract

Achieving an effective flow field configuration is requisite to ensure the optimal performance of a proton exchange membrane fuel cell (PEMFC). Herein, an unprecedented bio-inspired flow field is offered to improve species transport and enhance the power density of PEMFCs in order to provide a pathway for effective utilization of them. The flow field consists of two twin parts, resembling the lung structure, and a repeating converging-diverging pattern, so-called “converging-diverging lung-inspired serpentine (CDLIS).” Utilizing multiphase computational fluid dynamics, this study delves into the transport phenomena and evaluates the comprehensive efficacy of a three-dimensional PEMFC. The CDLIS flow field considerably enhances both crosswise and lengthwise mass transfer by inducing several favorable flow mixing phenomena and improving reactant's velocity distribution. The output power of CDLIS exhibits a substantial increase of 10.87%, 43.63%, 12.22%, 12.83%, 12.52%, and 13.8% compared with the simple serpentine, parallel, two-path serpentine, four-path serpentine, lung-inspired serpentine, and combined parallel-serpentine flow fields, respectively. Due to its highly efficient mass transfer capabilities, enhanced convective mass flow, and outstanding current density, CDLIS possesses the most effective water management and the lowermost flooding risk. To further improve the performance of the PEMFC, artificial intelligence codes based on the non-sorting genetic algorithm II (NSGA II) and a surrogate analytical model are developed to optimize the operating conditions of CDLIS at the limiting current density situation where concentration losses impede the mass transfer of reactants. The target parameters for optimization are current density, water saturation (WS), and oxygen transport resistance (OTR). Under optimum conditions, a remarkable current density of 6.35 A cm−2, a low WS of 0.043, and a reduced OTR of 5.06 s m−1 are obtained.

Suggested Citation

  • Ghanbari, Sina & Ghasabehi, Mehrdad & Asadi, Mohammad Reza & Shams, Mehrzad, 2024. "An inquiry into transport phenomena and artificial intelligence-based optimization of a novel bio-inspired flow field for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s030626192401643x
    DOI: 10.1016/j.apenergy.2024.124260
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