Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions
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DOI: 10.1016/j.apenergy.2019.114318
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- Li, Zheng & Yu, Jie & Wang, Chen & Bello, Idris Temitope & Yu, Na & Chen, Xi & Zheng, Keqing & Han, Minfang & Ni, Meng, 2024. "Multi-objective optimization of protonic ceramic electrolysis cells based on a deep neural network surrogate model," Applied Energy, Elsevier, vol. 365(C).
- Fan, Ruijia & Chang, Guofeng & Xu, Yiming & Xu, Jiamin, 2024. "Investigating and quantifying the effects of catalyst layer gradients, operating conditions, and their interactions on PEMFC performance through global sensitivity analysis," Energy, Elsevier, vol. 290(C).
- 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.
- Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
- Giugno, Andrea & Mantelli, Luca & Cuneo, Alessandra & Traverso, Alberto, 2020. "Performance analysis of a fuel cell hybrid system subject to technological uncertainties," Applied Energy, Elsevier, vol. 279(C).
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Keywords
Solid oxide fuel cell; Electrochemical model; Global sensitivity analysis; Polynomial chaos expansion; Bayesian model evidence;All these keywords.
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