Detailed physics-based computer models of fuel cells can be computationally prohibitive for applications such as optimization and uncertainty quantification. Such applications can require a very high number of runs in order to extract reliable results. Approximate models based on spatial homogeneity or data-driven techniques can serve as surrogates when scalar quantities such as the cell voltage are of interest. When more detailed information is required, e.g., the potential or temperature field, computationally inexpensive surrogate models are difficult to construct. In this paper, we use dimensionality reduction to develop a surrogate model approach for high-fidelity fuel cell codes in cases where the target is a field. A detailed 3D model of a high-temperature polymer electrolyte membrane (PEM) fuel cell is used to test the approach. We develop a framework for using such surrogate models to quantify the uncertainty in a scalar/functional output, using the field output results. We propose a number of alternative methods including a semi-analytical approach requiring only limited computational resources.
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February 2017
Research-Article
Surrogate Modeling for Spatially Distributed Fuel Cell Models With Applications to Uncertainty Quantification
A. A. Shah
A. A. Shah
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A. A. Shah
Manuscript received August 29, 2016; final manuscript received March 26, 2017; published online May 30, 2017. Assoc. Editor: Jan Van herle.
J. Electrochem. En. Conv. Stor. Feb 2017, 14(1): 011006 (15 pages)
Published Online: May 30, 2017
Article history
Received:
August 29, 2016
Revised:
March 26, 2017
Citation
Shah, A. A. (May 30, 2017). "Surrogate Modeling for Spatially Distributed Fuel Cell Models With Applications to Uncertainty Quantification." ASME. J. Electrochem. En. Conv. Stor. February 2017; 14(1): 011006. https://doi.org/10.1115/1.4036491
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