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Correlating variability of modeling parameters with non-isothermal stack performance: Monte Carlo simulation of a portable 3D planar solid oxide fuel cell stack

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  • He, Zhongjie
  • Li, Hua
  • Birgersson, E.

Abstract

The development of fuel cells has progressed to portable applications recently. This paper conducts a Monte Carlo simulation (MCS) of a spatially-smoothed non-isothermal model to correlate the performance of a 3D 5-cell planar solid oxide fuel cell (P-SOFC) stack with the variability of modeling parameters regarding material and geometrical properties and operating conditions. The computationally cost-efficient P-SOFC model for the MCS captures the leading-order transport phenomena and electrochemical mechanics of the 3D stack. Sensitivity analysis is carried out in two scenarios: first, by varying modeling parameters individually, and second by varying them simultaneously. The stochastic parameters are ranked according to the strength of their correlations with global and local stack performances. As a result, different rankings are obtained for the two scenarios. Moreover, in the second scenario, the rankings change with the nominal values and variability of the stochastic parameters as well as local positions within the stack, because of compensating or reinforcing effects between the varying parameters. Apart from the P-SOFCs, the present MCS can be extended to other types of fuel cells equipped with parallel flow channels. The fast stack model allows statistical modeling of a large stack of hundreds of cells for high-power applications without a prohibitive computational cost.

Suggested Citation

  • He, Zhongjie & Li, Hua & Birgersson, E., 2014. "Correlating variability of modeling parameters with non-isothermal stack performance: Monte Carlo simulation of a portable 3D planar solid oxide fuel cell stack," Applied Energy, Elsevier, vol. 136(C), pages 560-575.
  • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:560-575
    DOI: 10.1016/j.apenergy.2014.09.056
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    References listed on IDEAS

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    1. Razbani, Omid & Wærnhus, Ivar & Assadi, Mohsen, 2013. "Experimental investigation of temperature distribution over a planar solid oxide fuel cell," Applied Energy, Elsevier, vol. 105(C), pages 155-160.
    2. Andersson, Martin & Yuan, Jinliang & Sundén, Bengt, 2010. "Review on modeling development for multiscale chemical reactions coupled transport phenomena in solid oxide fuel cells," Applied Energy, Elsevier, vol. 87(5), pages 1461-1476, May.
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    4. He, Zhongjie & Birgersson, E. & Li, Hua, 2014. "Reduced non-isothermal model for the planar solid oxide fuel cell and stack," Energy, Elsevier, vol. 70(C), pages 478-492.
    5. Calise, F. & Ferruzzi, G. & Vanoli, L., 2009. "Parametric exergy analysis of a tubular Solid Oxide Fuel Cell (SOFC) stack through finite-volume model," Applied Energy, Elsevier, vol. 86(11), pages 2401-2410, November.
    6. Yan, Min & Zeng, Min & Chen, Qiuyang & Wang, Qiuwang, 2012. "Numerical study on carbon deposition of SOFC with unsteady state variation of porosity," Applied Energy, Elsevier, vol. 97(C), pages 754-762.
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    Cited by:

    1. Cuneo, A. & Zaccaria, V. & Tucker, D. & Traverso, A., 2017. "Probabilistic analysis of a fuel cell degradation model for solid oxide fuel cell and gas turbine hybrid systems," Energy, Elsevier, vol. 141(C), pages 2277-2287.
    2. Shao, Qian & Gao, Enlai & Mara, Thierry & Hu, Heng & Liu, Tong & Makradi, Ahmed, 2020. "Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions," Applied Energy, Elsevier, vol. 260(C).
    3. Kannan, Vishvak & Xue, Hansong & Raman, K. Ashoke & Chen, Jiasheng & Fisher, Adrian & Birgersson, Erik, 2020. "Quantifying operating uncertainties of a PEMFC – Monte Carlo-machine learning based approach," Renewable Energy, Elsevier, vol. 158(C), pages 343-359.
    4. Ma, Rui & Liu, Chen & Breaz, Elena & Briois, Pascal & Gao, Fei, 2018. "Numerical stiffness study of multi-physical solid oxide fuel cell model for real-time simulation applications," Applied Energy, Elsevier, vol. 226(C), pages 570-581.
    5. He, Zhongjie & Li, Hua & Birgersson, E., 2016. "Correlating variability of modeling parameters with cell performance: Monte Carlo simulation of a quasi-3D planar solid oxide fuel cell," Renewable Energy, Elsevier, vol. 85(C), pages 1301-1315.

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