Surrogate Modeling for Spatially Distributed Fuel Cell Models With Applications to Uncertainty Quantification

Abstract

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.