Microstructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural network
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
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 mi crostructures 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.
Açıklama
Anahtar Kelimeler
Solid oxide fuel cell Electrode design, Synthetic microstructure generation, Three/triple phase boundaries, Artificial neural network
Kaynak
Powder Technology
WoS Q Değeri
Q2
Scopus Q Değeri
N/A
Cilt
425
