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000943321 1001_ $$0P:(DE-Juel1)5106$$aFroning, Dieter$$b0$$eCorresponding author
000943321 245__ $$aFlow Characteristics of Fibrous Gas Diffusion Layers Using Machine Learning Methods
000943321 260__ $$aBasel$$bMDPI$$c2022
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000943321 520__ $$aThe material characteristics of gas diffusion layers are relevant for the efficient operation of polymer electrolyte fuel cells. The current state-of-the-art calculates these using transport simulations based on their micro-structures, either reconstructed or generated by means of stochastic geometry models. Such transport simulations often require high computational resources. To support material characterization using artificial-intelligence-based methods, in this study, a convolutional neural network was developed. It was trained with results from previous transport simulations and validated using five-fold cross-validation. The neural network enables the permeability of paper-type gas diffusion layers to be predicted. A stochastic arrangement of the fibers, four types of binder distributions, and compression of up to 50% are also considered. The binder type and compression level were features inherent to the material but were not the subject of the training. In this regard, they can be seen as features hidden from the training process. Nevertheless, these characteristics were reproduced with the proposed machine learning model. With a trained machine learning model, the prediction of permeability can be performed on a standard computer.
000943321 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
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000943321 7001_ $$0P:(DE-Juel1)176619$$aWirtz, Jannik$$b1$$ufzj
000943321 7001_ $$0P:(DE-Juel1)171318$$aHoppe, Eugen$$b2
000943321 7001_ $$0P:(DE-Juel1)129883$$aLehnert, Werner$$b3
000943321 773__ $$0PERI:(DE-600)2704225-X$$a10.3390/app122312193$$gVol. 12, no. 23, p. 12193 -$$n23$$p12193 -$$tApplied Sciences$$v12$$x2076-3417$$y2022
000943321 8564_ $$uhttps://juser.fz-juelich.de/record/943321/files/applsci-12-12193-v2.pdf$$yOpenAccess
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000943321 9141_ $$y2022
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