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    Comparison of experimental thermal conductivity of water-based Al2O3–Cu hybrid nanofluid with theoretical models and artificial neural network output
    (Springer Science and Business Media B.V., 2024) Çolak, Andaç Batur; Bayrak, Mustafa
    The research aimed to experimentally test the thermal conductivity of five distinct Al2O3–Cu/water hybrid nanofluids. These nanofluids were generated at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1, and 0.2. The measurements were conducted within a temperature range of 10–65 °C. The primary objective of this research is to tackle the insufficient empirical data on hybrid nanofluids and establish a dependable artificial neural network model for forecasting their thermal conductivity. A multilayer perceptron feed forward back propagation artificial neural network has been created using the acquired experimental thermal conductivity data. The experimental thermal conductivity data have been compared with four commonly used mathematical correlations and the outputs of an artificial neural network. The findings demonstrated that the constructed artificial neural network accurately forecasted the thermal conductivity of the Al2O3–Cu/water hybrid nanofluid, with an average deviation of just 0.4%. Nevertheless, Maxwell’s mathematical correlation proved to be the most accurate model in predicting the experimental findings, with an average error margin of just 0.08%. © Akadémiai Kiadó, Budapest, Hungary 2024.
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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%.