Synthetic modeling of solid oxide fuel cell cathode micro structure

In this thesis, synthetic modeling of solid oxide fuel cell (SOFC) cathode electrodes is carried out. Micro structures of different volume fractions and particle sizes are generated virtually in order to be able to measure the triple phase boundary (TPB), which is the main factor of SOFC performance. The generated microstructures are first examined in detail and the active/inactive and total TPBs are numerically measured depending on the microstructural properties. In line with the results obtained from here, the next step, artificial neural network (ANN) models, prediction studies are made for many different cathode microstructures that are not measured. In ANN studies; It is concluded that by choosing the appropriate training algorithm, hidden layer and number of neurons, results close to numerical measurements can be obtained. Thus, with the results obtained in this thesis study, the cost, labor and time losses arising from the real SOFC cell/cathode production are avoided and the microstructural parameters that revealed the highest performance for the SOFC cathode electrode are determined.