Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/22839
Title: Development of Reduced Models for Proton Exchange Membrane Fuel Cells
Authors: LY CAM HUNG
Keywords: Mathematical modeling, PEMFC, Reduced model, Multi-phase model, Stack, Automated model.
Issue Date: 17-Aug-2010
Source: LY CAM HUNG (2010-08-17). Development of Reduced Models for Proton Exchange Membrane Fuel Cells. ScholarBank@NUS Repository.
Abstract: The proton exchange membrane fuel cell (PEMFC) is a promising candidate for automotive, portable, and stationary applications due to its low-temperature operating regime (typically around 60-80C), high efficiency, quiet operation, and no emissions. Presently, the fuel cell technology is at a state of pre-commercialization and needs further improvements to allow for large-scale commercialization; the latter requires both experiments and mathematical modeling in order to enhance fuel cell performance, reduce material cost, and optimize design and operating conditions. Mathematical modeling of a PEMFC, however, is highly challenging as it involves multi-component, multi-phase, and multi-dimensional transport over multiple length scales ? from O(1e-7m) to O(1m). Generally, models that consider coupled transport phenomena ? mass, momentum, species, energy and charge transfer ? are highly non-linear and computationally expensive; applying these models to typical PEMFC stacks, comprising tens or even hundreds of single cells, quickly becomes prohibitive from the computational point of view. It is therefore of interest to derive reduced mathematical models that can predict the local behavior in a PEMFC stack by preserving the essential physics and geometrical resolution whilst keeping the computational cost to a minimum. In this context, we have successfully developed reduced models (both single- and multi-phase) for a single PEMFC, in which the convergence time and memory requirement decreased by 2-3 order of magnitudes; the results were verified numerically and validated experimentally ? good agreement was obtained. Using the reduced single-cell model as a building block, an automated model (code) generator was then constructed to handle the numerical implementation of stack models. Typically, the reduced, automated code for a stack comprising up to 400 cells needs around 5 minutes for preprocessing and requires less than 15 minutes and around 2.3GB of memory to solve. This approach opens up the possibility for automated wide-ranging parameter studies and optimization of stacks without having to manually redraw the computational domain or manually change other conditions at each iteration.
URI: http://scholarbank.nus.edu.sg/handle/10635/22839
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