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Öğe A NUMERICAL STUDY AIMED AT FINDING OPTIMAL ARTIFICIAL NEURAL NETWORK MODEL COVERING EXPERIMENTALLY OBTAINED HEAT TRANSFER CHARACTERISTICS OF HYDRONIC UNDERFLOOR RADIANT HEATING SYSTEMS RUNNING VARIOUS NANOFLUIDS(Begell House Inc, 2022) Colak, Andac Batur; Karakoyun, Yakup; Acikgoz, Ozgen; Yumurtaci, Zehra; Dalkilic, Ahmet SelimIn this paper, three unique artificial neural network models have been developed for three different working fluid cases to predict the radiative, convective, and total heat transfer coefficients over the floor surface of radiant floor heating system in a real-size room. Pure water, multiwall carbon nanotube with 0.7 vol.% and 0.07 vol.% contents, and aluminium oxide with 1.26 vol.% content are the operating fluids having inlet temperatures ranging from 30 degrees C to 60 degrees C, while the mass flow rates are 0.056, 0.09, and 0.125 kg/s. The performances of multilayer perceptron networks with the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient as training algorithms and different neuron numbers have been developed and the Levenberg-Marquardt algorithm, having the highest prediction performance with 99% accuracy, is selected as a result of detailed computational numerical analyses. This study can be considered as a pioneer artificial neural network one on the floor heating systems having nanofluids.Öğe Artificial neural network approach for investigating the impact of convector design parameters on the heat transfer and total weight of panel radiators(Elsevier France-Editions Scientifiques Medicales Elsevier, 2023) Calisir, Tamer; Colak, Andac Batur; Aydin, Devrim; Dalkilic, Ahmet Selim; Baskaya, SenolThe difficulty of the production stages of panel radiators used for heating purposes reveals the importance of determining the heat transfer performance and panel radiator weight values, which are determined depending on the design parameters. In the present work, an artificial neural network model is proposed for predicting the heat transfer and weight values of a panel radiator as outputs depending on the design parameters of convectors. In the multilayer network model developed with 78 numerically obtained data sets, 8 different design parameters were defined as input parameters and heat transfer and in the output layer panel weight values were obtained. The design parameters of the convectors, in other words, input parameters of network model were chosen as the height of convector, thickness of convector sheet, the trapezoidal height of convector, convector base length, opposing convector distance, tip width of convector, convector vertical location and distance between convectors. For the proposed neural network model, the mean squared errors obtained for the heat transfer and panel radiator weight are -1.25E-04 and -7.54E-05 respectively. In addition, an R-value of 0.99999 has been obtained, and the average deviation value has been calculated as 0.001%. The obtained results show that, depending on the design parameters, the proposed artificial neural network model can predict the rate of heat transfer and weight of the panel radiator with high accuracy. This investigation is supposed to fill a significant gap since it is the pioneer one in open sources on machine learning modeling of panel radiators. Thus, it can possibly make a crucial contribution to the related manufacturing industry.Öğe Determination of heat transfer rates of heavy-duty radiators for trucks having flattened and double-U grooved pipes with louvered fins by ANN method: an experimental study(Springer Heidelberg, 2022) Mercan, Hatice; Sonmez, Furkan; Colak, Andac Batur; Dalkilic, Ahmet SelimIn this study, radiators equipped with conventional pipes and newly proposed double-U grooved and brazed pipes are compared with each other under similar operating conditions. The radiators' heat transfer performances have been evaluated experimentally and validation is done using the analytical Number of Transfer Unit Method. The cooling fluid has a fixed composition with 50% water and 50% ethylene glycol. Coolant flow rate changed between 2.5 and 7 kg/s, air velocity changed between 1.5 and 12 m/s and both inlet pressure and temperature values have been kept constant for air and coolant fluid sides throughout the experiments. Heat transfer rate, exit coolant temperature, friction coefficient, pressure drop inside the radiator and the total heat transfer coefficient have been assessed experimentally and compared under changing operation conditions. Using the obtained experimental data, an Artificial Neural Network model has been generated to determine the heat transfer rate for each radiator. The data taken from the numerical model have been compared with the practical one and analyzed extensively. It is observed that heat transfer rate and pressure drop are the highest for the Double U-grooved pipe radiator. The prediction values acquired from the reformed neural network are in good compatibility with the practical data.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 Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection(Elsevier France-Editions Scientifiques Medicales Elsevier, 2022) Acikgoz, Ozgen; Colak, Andac Batur; Camci, Muhammet; Karakoyun, Yakup; Dalkilic, Ahmet SelimMixed convection phenomenon over radiant cooled surfaces with displacement ventilation in living environments is becoming a popular issue due to the airborne viruses and energy economy. Artificial neural networks are one of the machine learning methods that are widely evaluated as an engineering tool. In the current study, heat transfer coefficients for a radiant wall cooling system coupled with mixed and forced convection have been predicted by a machine learning approach. This approach should be noted as a first experimental investigation couple with an artificial neural network analysis in the open sources in which mixed convection systems in real sized living environments is examined. Experimentally obtained heat transfer coefficients have been used in the development of the feed forward back propagation multi-layer perceptron network structure. So as to analyze the impact of the input factors on the prediction performance, two neural network structures with dissimilar input parameters such as various temperatures, velocities, and heat transfer rates have been developed. By means of feed forward back propagation multi-layer perceptron neural network algorithms, convection, radiation, and total heat transfer coefficients have been predicted using the experimentally acquired dataset including 35 data points belonging to the mixed and forced convection conditions. Training, validation, and test data groups include 70%, 15%, and 15% of the dataset, in turn. Training algorithm has been computed via LevenbergMarquardt one with 10 neurons in the hidden layer. The findings obtained from the computational solution have been evaluated as a result of the contrast with the target data with in the +/- 5% deviation band for all heat transfer coefficients. The performance factors have been computed and the estimation precision of the numerical models has been thoroughly examined.Öğe 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 SelimIn 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.Öğe 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 SelimSince 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.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]
