Stochastic representations of fiber-based gas diffusion layershttps://doi.org/10.26165/JUELICH-DATA/RCL4O0Froning, DieterJülich DATA2024-12-132024-12-13T10:33:07ZGas diffusion layers (GDLs) are relevant for the efficient fluid transport between the channel structure and the membrane electrode assembly (MEA) of fuel cells [1]. Black/white (BW) images of 25 realizations of a stochastic model represent the micro-structure of paper-type GDLs as manufactured by Toray. A binder model (5 representations) is combined with a fiber model (25 representations each). The 3D structures are represented by 130 images of size 512x512 each with a resolution of 1.5 µm/px. Every image represents a layer of 1.5 µm thickness. This leads to a total amount of 5*25*130=16250 images, arranged in a sub-folder structure that represents the binder model. 130 images of size 512x512 layers represent a section of 768 µm x 768 µm m 195 µm of a GDL. The fiber thickness is 7.5 µm. Binder material is located layer-wise along some fibers with a binder width of 6 µm, 18 µm, 30 µm, 40 µm or filled polygons (indicated as FF).<br> The stochastic fundamentals are published in [2]. Transport simulations using the Lattice Boltzmann method were conducted and presented in [1;3-9]. Machine learning (ML) aspects were addressed in [10-11].<br> For binder with <b>WW</b> in {06, 18 30, 40, FF}, representation <b>N</b> in {1...25}, image number <b>I</b> in {1...130}, image path/names are:<br> binder-<b>WW</b>/Sim<b>N</b>/Image_512x512_<b>N</b>_No_<b>I</b>.png.<br> Fig. 1 in [1] shows images with binder width of (A) 6 µm, (B) 18 µm, (C) 30 µm and (D) filled polygons. Fig. 3 in [3] extends the illustration by an 40 µm example, labelled as (D) in [3].<br> Subsequent simulations in [4-9] favored the binder width of 18 µm. The ML investigations [10, 11] covered the same binder widths as [1].EngineeringFuel cells; GDL; stochastic model; micro-structure; images[1] Froning, D. / Brinkmann, J. / Reimer, U. / Schmidt, V. / Lehnert, W. / Stolten, D. 3D analysis, modeling and simulation of transport processes in compressed fibrous microstructures, using the Lattice Boltzmann method, Electrochimica Acta (2013), Vol. 110 p. 325-334., doi, 10.1016/j.electacta.2013.04.071[2] Thiedmann, R. / Fleischer, F. / Hartnig, C. / Lehnert, W. / Schmidt, V. Stochastic 3D Modeling of the GDL Structure in PEMFCs Based on Thin Section Detection, J. Electrochem. Soc. (2008) , Vol. 155, No. 4 p. B391-B399., doi, 10.1149/1.2839570[3] Froning, D. / Gaiselmann, G. / Reimer, U. / Brinkmann, J. / Schmidt, V. / Lehnert, W. Stochastic Aspects of Mass Transport in Gas Diffusion Layers, Transp Porous Med (2014) 103:469–495., doi, 10.1007/s11242-014-0312-9[4] Froning, D. / Yu, J. / Reimer, U. / Lehnert, W. Stochastic Analysis of the Gas Flow at the Gas Diffusion Layer/Channel Interface of a High-Temperature Polymer Electrolyte Fuel Cell, Appl. Sci. (2018), 8, 2536., doi, 10.3390/app8122536[5] Froning, D. / Yu, J. / Reimer, U. / Lehnert, W. Stochastic Analysis of the Gas Flow at the Gas Diffusion Layer/Electrode Interface of a High-Temperature Polymer Electrolyte Fuel Cell, Transp. Porous Media (2018), Vol. 123 p. 403-420., doi, 10.1007/s11242-018-1048-8[6] Froning, D. / Yu, J. / Reimer, U. / Lehnert, W. Statistische Analyse des lokalen Wassertransportes einer Polymer-Elektrolyt-Brennstoffzelle, Chem. Ing. Tech. (2019), 91, No. 6, 865–871., doi, 10.1002/cite.201800158[7] Yu, J. / Froning, D. / Reimer, U. / Lehnert, W. Apparent contact angles of liquid water droplet breaking through a gas diffusion layer of polymer electrolyte membrane fuel cell, Int. J. Hydrogen Energy (2018), Vol. 43 p. 6318-6330, doi, 10.1016/j.ijhydene.2018.01.168[8] Yu, J. / Froning, D. / Reimer, U. / Lehnert, W. Liquid water breakthrough location distances on a gas diffusion layer of polymer electrolyte membrane fuel cells, J. Power Sources (2018) , Vol. 389 p. 56-60., doi, 10.1016/j.jpowsour.2018.04.004[9] D. Froning, Uwe Reimer, W. Lehnert. Inhomogeneous Distribution of Polytetrafluorethylene in Gas Diffusion Layers of Polymer Electrolyte Fuel Cells, Transp Porous Med (2021),, doi, 10.1007/s11242-021-01542-0[10] D. Froning, J. Wirtz, E. Hoppe, W. Lehnert. Flow Characteristics of Fibrous Gas Diffusion Layers Using Machine Learning Methods, Appl. Sci. (2022), 12, 12193., doi, 10.3390/app122312193[11] D. Froning, E. Hoppe, R. Peters. The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers, Appl. Sci. (2023), 13, 6981., doi, 10.3390/app13126981Froning, Dieter2024-11-05micro-structure[2] Thiedmann, R. / Fleischer, F. / Hartnig, C. / Lehnert, W. / Schmidt, V. Stochastic 3D Modeling of the GDL Structure in PEMFCs Based on Thin Section Detection, J. Electrochem. Soc. (2008) , Vol. 155, No. 4 p. B391-B399. DOI: 10.1149/1.2839570.CCBY