<div class="csl-bib-body">
<div class="csl-entry">Roman Baena, V. J., Asouti, V., & Vernes, A. (2025, July 3). <i>A Proper Discretization for ML-Optimizing the Electrical Contact Resistance of Random Carbon Fiber Networks</i> [Conference Presentation]. 8th International Conference on Computational Contact Mechanics (ICCCM 2025), München, Germany. http://hdl.handle.net/20.500.12708/225888</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/225888
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dc.description.abstract
The performance and durability of proton exchange membrane fuel cells (PEMFCs) are strongly governed by the electrical contact resistance (ECR) at the gas diffusion layer (GDL)–bipolar plate (BPP) interface, where up to 59% of total power losses can originate. ECR arises from the complex coupling between GDL microstructure and interfacial contact mechanics, underscoring the need for accurate, physics-informed characterization. This work introduces an integrative framework that combines a physics-based electrical contact model with machine learning (ML) techniques to identify and quantify the influence of morphological and mechanical descriptors of carbon paper GDLs on ECR. A comprehensive, pressure-dependent dataset is generated by simulating GDL microstructures using a Poisson line process under diverse configurations, while the BPP is idealized as a perfectly smooth surface. ECR is computed using a Holm–Greenwood–type model that represents interfacial contacts via ensembles of a-spots, each approximated by circles whose areas match the real GDL–BPP contact regions. To address the geometric simplifications inherent in this approach, we systematically compare alternative digitalizations of fiber–plate contact—including elliptical, rectangular, and circular approximations—and evaluate their convergence toward analytical ECR predictions. The resulting framework provides a robust pathway for elucidating structure–property relationships in GDLs and for guiding the design of low-resistance PEMFC interfaces.
en
dc.language.iso
en
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dc.subject
Contact mechanics
en
dc.subject
Machine Learning
en
dc.subject
Fuel Cells
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dc.title
A Proper Discretization for ML-Optimizing the Electrical Contact Resistance of Random Carbon Fiber Networks
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
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tuw.researchTopic.id
M1
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tuw.researchTopic.id
E2
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Surfaces and Interfaces
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tuw.researchTopic.name
Sustainable and Low Emission Mobility
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
25
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tuw.researchTopic.value
25
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tuw.researchTopic.value
50
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tuw.linking
https://www.unibw.de/icccm2025
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tuw.publication.orgunit
E134-04 - Forschungsbereich Biophysics
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tuw.author.orcid
0000-0002-7917-1536
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tuw.event.name
8th International Conference on Computational Contact Mechanics (ICCCM 2025)
