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Coupled computational fluid dynamics-response surface methodology to optimize direct methanol fuel cell performance for greener energy generation

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  • Sharifi, Shima
  • Rahimi, Rahbar
  • Mohebbi-Kalhori, Davod
  • Colpan, C. Ozgur

Abstract

Optimization of operational parameters is vital for improving the performance of direct methanol fuel cells. To investigate the effects of these parameters on the power density, the experiments were performed using an experimental setup to yield the highest performance. In this regard, response surface methodology (RSM) was applied to select the proper combination of operating variables such as cell temperature, methanol concentration, and oxygen flow rate. Furthermore, a computational fluid dynamics (CFD) model of DMFC flow field plates, including two parallel-serpentine channels with circular bends were conducted using the finite element method at the optimum operating conditions, which obtained by applying RSM. The developed model solves the conservation of charge, mass, momentum, and species (methanol, water, and oxygen) transport equations. The performance tests based on RSM gave the optimum operating conditions as a cell temperature of 70 °C, methanol concentration of 1 M, and an oxygen flow rate of 300 ml/min. The mathematical model in the optimal operating conditions showed that the polarization curve obtained from the modeling study is in good agreement with the experimental data. Also, the concentration distributions of methanol and oxygen at the optimum operating conditions were predicted by the CFD model.

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  • Sharifi, Shima & Rahimi, Rahbar & Mohebbi-Kalhori, Davod & Colpan, C. Ozgur, 2020. "Coupled computational fluid dynamics-response surface methodology to optimize direct methanol fuel cell performance for greener energy generation," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s036054422030400x
    DOI: 10.1016/j.energy.2020.117293
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    6. Abdelkareem, Mohammad Ali & Sayed, Enas Taha & Nakagawa, Nobuyoshi, 2020. "Significance of diffusion layers on the performance of liquid and vapor feed passive direct methanol fuel cells," Energy, Elsevier, vol. 209(C).
    7. Ke, Yuzhi & Zhang, Baotong & Bai, Yafeng & Yuan, Wei & Li, Jinguang & Liu, Ziang & Su, Xiaoqing & Zhang, Shiwei & Ding, Xinrui & Wan, Zhenping & Tang, Yong & Zhou, Feikun, 2023. "Bubble-derived contour regeneration of flow channel by in situ tracking for direct methanol fuel cells," Energy, Elsevier, vol. 264(C).
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    9. Zhang, Rongji & Cao, Jiamu & Wang, Weiqi & Zhou, Jing & Chen, Junyu & Chen, Liang & Chen, Weiping & Zhang, Yufeng, 2023. "An improved strategy of passive micro direct methanol fuel cell: Mass transport mechanism optimization dominated by a single hydrophilic layer," Energy, Elsevier, vol. 274(C).

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