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001025146 0247_ $$2doi$$a10.1149/11204.0049ecst
001025146 0247_ $$2ISSN$$a1938-5862
001025146 0247_ $$2ISSN$$a1938-6737
001025146 0247_ $$2ISSN$$a2151-2051
001025146 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02723
001025146 037__ $$aFZJ-2024-02723
001025146 082__ $$a540
001025146 1001_ $$0P:(DE-HGF)0$$aYang, Danan$$b0$$eCorresponding author
001025146 245__ $$aNumerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers
001025146 260__ $$aPennington, NJ$$b[Verlag nicht ermittelbar]$$c2023
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001025146 520__ $$aStochastic reconstruction is widely employed for effective and flexible imitation of Gas Diffusion Layers (GDLs), e.g., to facilitate the study of their properties. However, the reconstruction often overlooks crucial factors such as fiber curvature, fiber stack arrangement, and fiber anisotropy. Consequently, the impact of these structural characteristics remains poorly understood. In this study, an in-house reconstruction procedure is developed based on the periodic surface model. This procedure enables the generation of GDLs with either straight or curved fibers, layer-by-layer or random arrangement, and different probabilities of through-plane fiber orientation angles. The porosity, domain size, and fiber diameter are extracted from an experimental image-based GDL and utilized as input data for the reconstruction. Furthermore, the different GDLs are compared in terms of pore size distribution and through-plane porosity distribution. It is concluded that introducing proper selections of these fiber features gives the reconstruction more realistic properties.
001025146 536__ $$0G:(DE-HGF)POF4-1222$$a1222 - Components and Cells (POF4-122)$$cPOF4-122$$fPOF IV$$x0
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001025146 7001_ $$0P:(DE-HGF)0$$aGarg, Himani$$b1
001025146 7001_ $$0P:(DE-Juel1)157835$$aBeale, Steven B.$$b2
001025146 7001_ $$0P:(DE-Juel1)168242$$aAndersson, Martin$$b3
001025146 773__ $$0PERI:(DE-600)2251888-5$$a10.1149/11204.0049ecst$$gVol. 112, no. 4, p. 49 - 61$$n4$$p49 - 61$$tECS transactions$$v112$$x1938-5862$$y2023
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001025146 9141_ $$y2024
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