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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 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.
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    MODELING THE INFLUENCE OF NANOPARTICLES AND GYROTACTIC MICROORGANISMS ON NATURAL CONVECTION IN A HEATED SQUARE CAVITY USING ARTIFICIAL NEURAL NETWORK (ANN)
    (Begell House Inc, 2024) Colak, Andac Batur
    The phenomenon of natural convection, which is widely used in nature and engineering applications, is a current issue that can be encountered in every field of daily life. In this study, the natural convection characteristics of a complex liquid containing nanoparticles and gyrotactic microorganisms in a heated square cavity were investigated using artificial neural network approach. The Nusselt number, average Sherwood number of nanoparticles, and average Sherwood number of microorganisms were considered as natural convection parameters and an artificial neural network model was developed to estimate these values. The Lewis number, Brownian motion parameter, thermophoresis parameter, and buoyancy ratio parameter values were defined as input parameters in the network model, which has a multilayer perceptron architecture developed with a total of 24 datasets. There were 10 neurons in the hidden layer of the network model, which has a Bayesian regularization training algorithm. The outputs obtained from the network model were compared with the target values; in addition, the prediction performance of the model was extensively analyzed using various performance parameters. It was seen that the predicted values obtained from the neural network and the target values were in an ideal harmony. On the other hand, the value of the coefficient of determination for the network model was 0.99999% and the mean deviation rates were lower than -0.03%. The results of the study showed that the developed neural network model can predict the natural convection parameters discussed with high accuracy.
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    A NOVEL MACHINE LEARNING STUDY: MAXIMIZING THE EFFICIENCY OF PARABOLIC TROUGH SOLAR COLLECTORS WITH ENGINE OIL-BASED COPPER AND SILVER NANOFLUIDS
    (Begell House Inc, 2024) Colak, Andac Batur; Bayrak, Mustafa
    Estimating the heat transfer parameters of parabolic trough solar collectors with machine learning is crucial for improving the efficiency and performance of these renewable energy systems, optimizing their design and operation, and reducing costs while increasing the use of solar energy as a sustainable power source. In this study, the heat transfer characteristics of two different nanofluids flowing through the porous media in a straight plane underneath thermal jump conditions were investigated by machine learning methods. For the flow in the parabolic trough solar collector, two different nanofluids obtained from silver- and copper-based motor oil are considered. Flow characteristics were obtained by nonlinear surface tension, thermal radiation, and Cattaneo-Christov heat flow, which was used to calculate the heat flow in the thermal boundary layer. A neural network structure was established to estimate the skin friction and Nusselt number determined for the analysis of the flow characteristic. The data used in the multilayer neural network, which was developed using a total of 30 data sets, were divided into three groups as training, validation, and testing. In the input layer of the network model with 15 neurons in the hidden layer, 10 parameters were defined and four different results were obtained for two different nanofluids in the output layer. The prediction performance of the established neural network model has been comprehensively studied by means of several performance parameters. The study findings presented that the established artificial neural network can predict the heat transfer characteristics of two different nanofluids obtained from silver- and copper-based motor oil with deviation rates less than 0.06%.