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    Microstructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural network
    (Elsevier, 2023) Timurkutluk, Bora; Ciflik, Yelda; Sonugur, Guray; Altan, Tolga; Genc, Omer; Colak, Andac Batur
    Artificial neural network (ANN) is used to model active three/triple phase boundaries (TPBs) in solid oxide fuel cell (SOFC) electrodes composed of phases with various particle sizes for the first time in the literature. Electrode microstructures comprising catalyst, electrolyte and pore phases with the same volume fraction, but various mean particle sizes are synthetically generated via Dream.3D software and the active TPB densities are measured by COMSOL software to obtain input data for training the ANN models as well as to validate the network results. In this regard, three learning methods of Bayesian regulation (BR), Levenberg-Marquardt (LM) and Scaled conjugate gradient (SCG) with various hidden layer and neuron numbers are examined. Among ANN models with three inputs and one output, the model with BR including one hidden layer and five neurons performs the best. This model revealing an average relative error of only 0.036 is then employed to simulate SOFC electrodes microstructures with new particle sizes not introduced in the learning process. The active TPB densities estimated by ANN are found to agree well with the computed ones. Therefore, ANN modeling is considered as a useful tool for the prediction of active TPB density in SOFC electrodes after a careful selection of backpropagation method and network structure.
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    Quantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network
    (Elsevier Sci Ltd, 2024) Timurkutluk, Bora; Ciflik, Yelda; Sonugur, Guray; Altan, Tolga; Genc, Omer
    Virtual solid oxide fuel cell (SOFC) electrode microstructures composed of pore, electrolyte and catalyst phases with various particle sizes and volume fractions are reconstructed to design high-performance electrodes by investigating the role of microstructural properties on the electrodes and thereby the cell performance. The active TPB (triple phase boundary) densities in these microstructures are numerically measured and the data are used to train numerous artificial neural networks established with different model parameters and learning methods. Based on the results of 10,000 trainings of each model, the network that employs a backpropagation method of Bayesian regulation and has 2 hidden layers with 15 neurons is found to be the best one. It is then used to simulate new cases, whose parameters are in the range of those used in training. Further validation of the best network is also performed by considering a few randomly selected cases. The simulation results providing active TPB densities quantitatively are discussed regarding the microstructural properties. The overall results reveal that active TPBs can be increased by reducing the particle size of the phases and volume fraction of any phase should be selected according to the particle size to improve the number of active TPBs.