Spatially smoothed fuel cell models: Variability of dependent variables underneath flow fields

Spatial smoothing can be applied to reduce a three-dimensional (3D) fuel cell model to a computationally cost-efficient two-dimensional (2D) counterpart. In this study, the averaged dependent variables in the streamwise direction are augmented with a statistical measure of their distribution in the spanwise direction in the form of standard deviations. A functional form of the latter is postulated and justified a posteriori by comparing the predictions from the 2D spatially-smoothed model with the original 3D counterpart: good agreement is found. This measure of the variability thus complements the average predictions of existing spatially smoothed models. •The variability of dependent variables is quantified for spatially smoothed models.•The variability is expressed in terms of standard deviations.•The standard deviations in the reduced 2D model agree well with the 3D counterpart.•Essential physics are captured when reducing model complexity and computation cost.