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Öğe Performance analysis of temperature changes of fuels used in pem fuel cell(Batman Üniversitesi, 2022-12-31) Demir, Merve; Yılmaz, AdemIn this study, the temperature values of the fuels used in Polymer Electrolyte Membrane (PEM) fuel cell were determined and the optimum temperature ranges were obtained for these fuels. Pure hydrogen and oxygen were used in the anode and cathode portions. In this study, moisture was taken as 40%, hydrogen amount as 0.3 ml/min and oxygen amount as 0.5 ml/min. Line temperature values in the system were also tested between 40-80°C with a 5°C difference. In the experiments carried out at 40°C, when the voltage value was taken as 0.442V and the current value was taken as 1.81A, the power value obtained in the system was found to be 0.804W. In the experiment, when the current value is 1.8A and the voltage value is 0.535V at 75°C, the power value in the system is found to be 1.025W. The lowest W value was calculated as 0.804W at 40°C and the highest W value was calculated as 1.025W at 75°C.Öğe An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions(Hindawi, 2019-04-01) Fidan, Şehmus; Oktay, Hasan; Polat, Süleyman; Öztürk, SarperGrowing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg-Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.
