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    Experimental investigation on the effect of anode functional layer on the performance of anode supported micro-tubular SOFCs
    (Pergamon-Elsevier Science Ltd, 2022) Timurkutluk, Cigdem; Bilgil, Keremhan; Celen, Ali; Onbilgin, Sezer; Altan, Tolga; Aydin, Ugur
    In this study, anode supported micro-tubular solid oxide fuel cells (SOFCs) are fabricated by extrusion method and the effects of powder size, thickness and sintering temperature of the anode functional layer (AFL) on the electrochemical performance is experimentally investigated. For this purpose, four different commercial NiO powders are tested as initial powder for the fabrication of the anode functional layer. The thickness of AFL is also considered by varying the number of coatings. After deciding the optimum initial NiO powder size used in AFL and AFL thickness, the effect of pre-sintering temperature is examined. The performance tests are performed at an operating temperature of 800 degrees C under hydrogen and air. The microstructures of the samples are also investigated by a scanning electron microscope. The best peak power density is obtained as similar to 0.5 W/cm(2) from the cell having a single layer anode functional layer pre-sintered at 1250 degrees C prepared by NiO powders with 4 m(2)/g surface area. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Experimental Study on the Specific Heat Capacity Measurement of Water-Based Al2O3-Cu Hybrid Nanofluid by using Differential Thermal Analysis Method
    (Bentham Science Publ Ltd, 2020) Colak, Andac Batur; Yildiz, Oguzhan; Bayrak, Mustafa; Celen, Ali; Dalkilic, Ahmet Selim; Wongwises, Somchai
    Background: Researchers working in the field of nanofluid have done many studies on the thermophysical properties of nanofluids. Among these studies, the number of studies on specific heat is rather limited. In the study of the heat transfer performance of nanofluids, it is essential to raise the number of specific heat studies, whose subject is one of the important thermophysical properties. Objective: The authors aimed to measure the specific heat values of Al2O3/water, Cu/water nanofluids and Al2O3-Cu/water hybrid nanofluids using the DTA procedure, and compare the results with those frequently used in the literature. In addition, this study focuses on the effect of temperature and volume concentration on specific heat. Methods: The two-step method was tried to have nanofluids. The pure water selected as the base fluid was mixed with the Al2O3 and Cu nanoparticles and Arabic Gum as the surfactant, firstly mixed in the magnetic stirrer for half an hour. It was then homogenized for 6 hours in the ultrasonic homogenizer. Results: After the experiments, the specific heat of nanofluids and hybrid nanofluid were compared and the temperature and volume concentration of specific heat were investigated. Then, the experimental results obtained for all three fluids were compared with the two frequently used correlations in the literature. Conclusion: Specific heat capacity increased with increasing temperature, and decreased with increasing volume concentration for three tested nanofluids. Cu/water has the lowest specific heat capacity among all tested fluids. Experimental specific heat capacity measurement results are compared by using the models developed by Pak and Cho and Xuan and Roetzel. According to experimental results, these correlations can predict experimental results within the range of +/- 1%.
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    Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method
    (Walter De Gruyter Gmbh, 2022) Colak, Andac Batur; Celen, Ali; Dalkilic, Ahmet Selim
    In the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.
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    Prediction of experimental thermal performance of new designed cold plate for electric vehicles' Li-ion pouch-type battery with artificial neural network
    (Elsevier, 2022) Kalkan, Orhan; Colak, Andac Batur; Celen, Ali; Bakirci, Kadir; Dalkilic, Ahmet Selim
    Since liquid-based thermal management systems are usually preferred methods for battery electric vehicles and cold plates are generally preferred to circulate the coolant, studies on their design are becoming increasingly essential. Besides, it seems useful to work artificial intelligence approaches to evaluate different battery thermal management systems, as it is known that the use of artificial intelligence is increasing in many applications today. The aim of this paper is to build up an artificial neural network model due to predict average battery temperature and maximum temperature difference on the battery surface which are also the artificial neural network outputs. The model inputs are depth of discharge, coolant flow rate (0.1, 0.6 and 1.1 l/min), discharge rate (1C- 5C), coolant inlet temperature (15, 25 and 35 degrees C). It is developed for a serpentine tubed cold plate, and mini channeled one which has novel design. To shorten the training time, after the optimization of the data set, a total of 270 data sets were utilized for training, validation, and test phases. In addition, the developed model predicts successfully average battery temperature and maximum temperature difference on the battery surface in the 10% error band range. Finally, the maximum margin of deviation and R values are 7.3% and 0.997%, respectively.