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CFD (computational fluid dynamics)-based optimal design of a micro-reformer by integrating computational a fluid dynamics code using a simplified conjugate-gradient method

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  • Cheng, Chin-Hsiang
  • Huang, Yu-Xian
  • King, Shun-Chih
  • Lee, Chun-I
  • Leu, Chih-Hsing

Abstract

This study is focused on computation optimization of the geometry for the flow channels in a micro-reformer used for methanol steam reforming. Three-dimensional mass and momentum transport phenomena with a pure fluid simulation in a micro-reformer are predicted using a commercial computational fluid dynamics code. Meanwhile, a simplified conjugate-gradient method is adopted to seek the optimal manifold shape and channel width of the micro-reformer iteratively using a Python interface. In the present study, the geometrical optimization tasks involve the designs of the inlet manifold and outlet manifold shapes as well as channel width distribution, and the design purpose is to obtain a uniform flow distribution throughout the entire micro-reformer so as to increase the hydrogen gas production rate. Cubic-spline interpolation is used in shape design to fit the points on the manifold shape more smoothly. The results show that the velocity standard deviation decreased from 0.14 to 0.048 and 0.051 after searching the optimal manifold shapes and channel widths, respectively. The manifold shapes of the inlet and outlet as well as the channel widths can efficiently lead to significant uniformity in the flow fields using a simplified conjugate-gradient method.

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  • Cheng, Chin-Hsiang & Huang, Yu-Xian & King, Shun-Chih & Lee, Chun-I & Leu, Chih-Hsing, 2014. "CFD (computational fluid dynamics)-based optimal design of a micro-reformer by integrating computational a fluid dynamics code using a simplified conjugate-gradient method," Energy, Elsevier, vol. 70(C), pages 355-365.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:355-365
    DOI: 10.1016/j.energy.2014.04.005
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    3. Chin-Hsiang Cheng & Yi-Han Tan, 2020. "Numerical Optimization of a Four-Cylinder Double-Acting Stirling Engine Based on Non-Ideal Adiabatic Thermodynamic Model and SCGM Method," Energies, MDPI, vol. 13(8), pages 1-19, April.
    4. Marinić-Kragić, Ivo & Vučina, Damir & Milas, Zoran, 2018. "Numerical workflow for 3D shape optimization and synthesis of vertical-axis wind turbines for specified operating regimes," Renewable Energy, Elsevier, vol. 115(C), pages 113-127.
    5. Bai, Xingying & Luo, Lizhong & Huang, Bi & Huang, Zhe & Jian, Qifei, 2021. "Flow characteristics analysis for multi-path hydrogen supply within proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 301(C).
    6. Wang, Junye, 2015. "Theory and practice of flow field designs for fuel cell scaling-up: A critical review," Applied Energy, Elsevier, vol. 157(C), pages 640-663.
    7. Ouyang, Kwan & Wu, Horng-Wen & Huang, Shun-Chieh & Wu, Sheng-Ju, 2017. "Optimum parameter design for performance of methanol steam reformer combining Taguchi method with artificial neural network and genetic algorithm," Energy, Elsevier, vol. 138(C), pages 446-458.
    8. Inbamrung, Piyanut & Sornchamni, Thana & Prapainainar, Chaiwat & Tungkamani, Sabaithip & Narataruksa, Phavanee & Jovanovic, Goran N., 2018. "Modeling of a square channel monolith reactor for methane steam reforming," Energy, Elsevier, vol. 152(C), pages 383-400.
    9. Chin-Hsiang Cheng & Yu-Ting Lin, 2020. "Optimization of a Stirling Engine by Variable-Step Simplified Conjugate-Gradient Method and Neural Network Training Algorithm," Energies, MDPI, vol. 13(19), pages 1-18, October.
    10. Chin-Hsiang Cheng & Yu-Ting Lin, 2022. "Computational Optimization of Free-Piston Stirling Engine by Variable-Step Simplified Conjugate Gradient Method with Compatible Strategies," Energies, MDPI, vol. 15(10), pages 1-16, May.

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