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    An experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation
    (Elsevier, 2023) Sahin, Fevzi; Genc, Omer; Gokcek, Murat; Colak, Andac Batur
    It is important to predict the thermophysical properties of nanofluids, which have higher heat transfer perfor-mance compared to the base fluid, without the need for experimental studies. In this study, two different artificial neural networks were created to predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid. The thermal conductivity and zeta potential of the Fe3O4/water nanofluid prepared at three different concen-trations were experimentally measured. An innovative mathematical correlation is proposed to calculate thermal conductivity based on temperature and concentration using the obtained experimental data. Considering that the correlations in the literature can generally be calculated according to concentration, the novelty of the proposed model stands out. The calculated values for thermal conductivity and zeta potential of the created artificial neural network and the new mathematical correlation were compared with the results of the experiments. In addition, a comprehensive performance analysis was made by calculating different performance parameters. The R values of the neural network models were above 0.99 and mean squared error values were obtained as 1.47E-05 and 1.58E-06, respectively. In addition, the mean deviation values calculated for the thermal conductivity of the network model were 0.03%, while it was 0.05% for the new mathematical correlation. The study results showed that ANN models can predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid with high accuracy. The proposed new mathematical correlation was also found to have higher error rates compared to the ANN model, although it was able to calculate thermal conductivity values with high accuracy.
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    Experimental determination of NiFe2O4-water nanofluid thermophysical properties and evaluation of its potential as a coolant in polymer electrolyte membrane fuel cells
    (Pergamon-Elsevier Science Ltd, 2024) Sahin, Fevzi; Acar, Mahmut Caner; Genc, Omer
    Heat is generated as a byproduct of the electrochemical reactions of a polymer electrolyte membrane fuel cell. This heat raises the temperature of the system, and if it is not properly removed from the cell, it can cause membrane damage and overheating. Thermal management is thus critical for PEM fuel cell stability, efficiency, and safety. This paper investigates the potential use of NiFe2O4-water nanofluid as a novel coolant for PEM fuel cells. To determine the nanofluid thermophysical properties, an experimental study for different mass% concentrations of NiFe2O4-water nanofluid is first performed. Then, a three-dimensional CFD model is built to demonstrate the effect of nanofluid use on the thermal performance of a cooling plate. Simulation results show that replacing water with 0.5% NiFe2O4 nanofluid improves temperature uniformity by 11.97%. Furthermore, nanofluid cooling reduces maximum surface temperature by up to 0.75 degrees C while increasing pressure drop by 5.6%, implying higher pumping power.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Experimentally determining the thermal properties of NiFe2O4 magnetic nanofluid under suitable stability conditions: Proposal the new correlation for thermophysical properties
    (Elsevier, 2023) Sahin, Fevzi; Genc, Omer
    Magnetic nanofluids are seen as new generation heat transfer fluids because they can act as smart fluids due to the fact that the applied external magnetic field effect can be easily controlled. In this study, NiFe2O4 magnetic nanofluids with 5 different mass ratios between 0.1 and 0.5% were produced with appropriate stability. Firstly, suitable stable nanofluids were prepared and their thermal conductivities, viscosities, specific heats, and densities were experimentally measured at different temperatures (20-60 degrees C) and concentrations. The obtained data were then used to develop artificial neural network models with MSE and R values of 9.3916E-04 and 0.99969, respectively, and a new concentration and temperature-dependent correlation was obtained for thermal properties. Furthermore, the thermal performance of the nanofluids was evaluated by comparing them with performance criteria such as the PER and Mo values, within the studied concentration and temperature range. The maximum PER value was calculated as 1.185, and the minimum Mo values for laminar flow conditions and turbulent flow conditions were calculated as 1.022 and 1.009, respectively, in the working temperature and concentration range.
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    From experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids
    (Elsevier, 2023) Sahin, Fevzi; Genc, Omer; Gokcek, Murat; Colak, Andac Batur
    Magnetic nanofluids (MNs) are considered advanced heat transfer fluids of the future due to their ability to function as intelligent fluids, with the applied external magnetic field effect being readily manageable. In this study, firstly, the stabilities of Fe3O4-water MNs prepared at 0.1, 0.25, 0.5, 0.75 and 1 mass ratios were determined by zeta potential measurement. The thermal conductivity and viscosities of MNs with appropriate stability were measured at 20-60 degrees C for all mass ratios. Secondly, using experimental data, two different artificial neural network (ANN) models were developed: one for thermal conductivity and viscosity depending on the temperature (20-60 degrees C) and mass ratio values and one for zeta potential depending on pH and mass ratio. Finally, using the obtained ANN data, two new mathematical correlations are proposed to predict thermal conductivity and viscosity. The study's results revealed that the developed ANN model has MSE and R values of 4.51E-06 and 0.99968, respectively, for thermal conductivity and viscosity of Fe3O4-water MNs can be accurately predicted by novel mathematical correlations.