Gas 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).
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].
For binder with WW in {06, 18 30, 40, FF}, representation N in {1...25}, image number I in {1...130}, image path/names are:
binder- WW/Sim N/Image_512x512_ N_No_ I.png.
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].
Subsequent simulations in [4-9] favored the binder width of 18 µm. The ML investigations [10, 11] covered the same binder widths as [1].