LAPSE:2023.1628
Published Article
LAPSE:2023.1628
Unsteady 3D-CFD Simulation of a Large Active Area PEM Fuel Cell under Automotive Operation Conditions—Efficient Parameterization and Simulation Using Numerically Reduced Models
February 21, 2023
Abstract
Polymer electrolyte membrane fuel cells (PEMFC) are promising devices for securing future sustainable mobility. Their field of application ranges from locally emission-free stationary power generation to propulsion systems for vehicles of all kinds. Computational fluid dynamic (CFD) simulations are successfully used to access the internal states and processes with high temporal and spatial resolution. It is challenging to obtain reliable physical values of material properties for the parameterization of the numerous governing equations. The current work addresses this problem and uses numerically reduced models to parameterize sophisticated transient 3D-CFD models of a commercial PEMFC. Experimental data from a stack test stand were available as a reference for numerical optimization of selected parameters and validation purposes. With an innovative meshing approach, the homogenized channels approach, a reduction of computational cells by 87% could be achieved, thus enabling the unsteady simulation of a 120 s load step with a computational mesh that represents the entire fuel cell geometry with reasonable computational effort. The water formation and the transport processes during the load step were analyzed. The self-humidification strategy of the fuel cell gases was visualized and the uniformity of the simulated quantities was discussed. An outlook on possible future work on efficient parameterization is given.
Keywords
3D-CFD, experimental data, fuel cell, homogenized channel model, numerical optimization, PEM, PEMFC, Simulation, single-channel model, transient, unsteady
Suggested Citation
Haslinger M, Lauer T. Unsteady 3D-CFD Simulation of a Large Active Area PEM Fuel Cell under Automotive Operation Conditions—Efficient Parameterization and Simulation Using Numerically Reduced Models. (2023). LAPSE:2023.1628
Author Affiliations
Haslinger M: Institute of Powertrains and Automotive Technology, TU Wien, Getreidemarkt 9, Object 1, 1060 Wien, Austria [ORCID]
Lauer T: Institute of Powertrains and Automotive Technology, TU Wien, Getreidemarkt 9, Object 1, 1060 Wien, Austria [ORCID]
Journal Name
Processes
Volume
10
Issue
8
First Page
1605
Year
2022
Publication Date
2022-08-13
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr10081605, Publication Type: Journal Article
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LAPSE:2023.1628
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https://doi.org/10.3390/pr10081605
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Feb 21, 2023
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