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Öğe 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, SomchaiBackground: 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%.Öğe Prediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network(Elsevier, 2022) Colak, Andac Batur; Acikgoz, Ozgen; Mercan, Hatice; Dalkilic, Ahmet Selim; Wongwises, SomchaiTypically, success in the estimation of machine learning is expected to rise with increasing input parameters, whereas the noise issue may rarely arise owing to redundant input factors undesirably influencing the learning algorithm. The parameters such as overall heat transfer coefficient, pressure drop, and overall cost have been determined by two different artificial neural networks evaluated by a multi-layer perceptron model. Using the Levenberg-Marquardt training algorithm, in the first model input layer, a total of 10 input parameters rho, n(p), k(1), Re-1, f(i), Re-2, f(o), n(s), P-1 and P-2 have been defined, while the second involves 8 input parameters by subtracting pumping powers from the first one, thus the noise issue has been investigated using unnecessary input parameters. Overall heat transfer coefficient, tube/annulus sides pressure drops, and overall cost have been estimated with deviations of 0.16%, 0.23%, 0.02%, and 0.003% via Model 1, 0.02%, 0.18%, 0.16%, and 0.15% via Model 2, respectively. Moreover, Model 1 results in the best mean squared errors for annulus side pressure drop and overall cost with the values of 2.54E-04 and 1.93E-04, correspondingly, whereas Model 2 yields the best values of 1.11E-04 and 1.90E-04 for overall heat transfer coefficient and tube side pressure drop, sequentially.Öğe Single phase flow of nanofluid including graphite and water in a microchannel (vol 56, 1, 2020)(Springer, 2020) Yildiz, Oguzhan; Acikgoz, Ozgen; Yildiz, Guldem; Bayrak, Mustafa; Dalkilic, Ahmet Selim; Wongwises, Somchai[Abstract Not Available]
