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Öğe 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 BaturIt 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.Öğe Determination of optimum ejector operating pressures for anodic recirculation in SOFC systems(Pergamon-Elsevier Science Ltd, 2017) Genc, Omer; Toros, Serkan; Timurkutluk, BoraIn this study, a numerical analysis of an ejector for micro combined heat and power system based on 18 kW Solid Oxide Fuel Cell (SOFC) using methane as fuel is presented. An ejector design, which reflects the real system conditions in the view of the flow characteristics, is provided and the ejector performance is numerically investigated for various methane pressure to exhaust pressure ratios and methane inlet temperatures. The results show that the fuel inlet temperature and the pressure ratio of the methane to exhaust significantly affect the steam to carbon ratio (STCR) and entrainment ratio. The higher pressure ratio and methane temperature allow a high entrainment ratio and STCR, but as pressure ratio and methane temperature increase, STCR and entrainment ratio remain unchanged after a specific value. 1140 different scenarios related with the inlet and outlet pressures of the ejector and methane temperature are created to determine the optimum operating conditions. The simulations show that the optimum methane inlet pressure is 7 bar and exhaust pressure is 1.159 bar for the ejector geometry of the interest. The entrainment ratio and STCR are determined as 2.05 and 0.92, respectively at this optimum scenario. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Engineering solid oxide fuel cell electrode microstructure by a micro-modeling tool based on estimation of TPB length(Pergamon-Elsevier Science Ltd, 2021) Timurkutluk, Bora; Altan, Tolga; Toros, Serkan; Genc, Omer; Celik, Selahattin; Korkmaz, Habip GokayIn this study, a typical solid oxide fuel cell (SOFC) electrode microstructure is numerically optimized in terms of the volume fraction of the catalyst, electrolyte and pore phases via a novel tool based on Dream.3D for the synthetic microstructure reconstruction and COM-SOL Multiphysics (R) Modeling for visualizing and computing three/triple phase boundaries (TPBs). First, the properties of the representative volume element are studied by a parameter independence analysis based on the average particle size. The results indicate that the size of the representative volume element should be at least 10 times greater than the largest average particle size in the microstructure, while the number of mesh elements should be selected such that the smallest average particle size in the system is divided into at least 5. The method is then validated with the available studies in the literature and seems to agree well. Therefore, numerical reconstruction of SOFC electrodes by the pro-posed method is found to be a very useful tool in the viewpoints of accuracy, flexibility and cost. Finally, SOFC electrode microstructures having the same particle size distribution of an average particle size of 0.5 mm for each phase but with various phase volume fractions are generated and the resultant TPBs are computed similarly. It is found out that the volume fraction of each phase should be close to each other as much as possible to maximize the active TPB density and among the cases considered, the highest active TPB density of 9.53 mm/mm(3) is achieved for an SOFC electrode including 35 vol% catalyst, 35 vol % electrolyte and 30 vol% porosity. The active TPB density is also found to be around 93% of the total TPB density. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Evaluation of anode support microstructure in solid oxide fuel cells using virtual 3D reconstruction: A simulation study(Pergamon-Elsevier Science Ltd, 2024) Timurkutluk, Bora; Ari, Ali; Altan, Tolga; Genc, OmerIn this study, the impact of microstructural properties of the anode support layer (ASL) composed of catalyst, electrolyte and pore phases on the performance of solid oxide fuel cell (SOFC) is investigated. Synthetic SOFC anode microstructures, comprising two layers including the anode functional layer (AFL), where electrochemical reactions take place, are generated using an open-source software. Different configurations of the anode support layer with various particle sizes and volume fractions of the catalyst and electrolyte phases for different porosities are combined with a consistent AFL to isolate the effect of ASL microstructural parameters. The triple phase boundary (TPB) density, chosen as a useful metric monitoring the anode performance, is then quantitively determined for each anode layer and entire anode in all reconstructed microstructures. The results demonstrate that the microstructure of ASL significantly influences the anode and thus cell performance by impacting reactant gas supply and current collection, emphasizing the necessity for meticulous design. Specifically, ASL microstructures with low volume fractions of Ni and the pore phase, and/or substantial differences between the volume fractions of the phases, are observed to result in discontinuous phase networks, which deactivate TPBs in AFL.Öğe 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, OmerHeat 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.Öğe 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, OmerMagnetic 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.Öğe 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 BaturMagnetic 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.Öğe Microstructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural network(Elsevier, 2023) Timurkutluk, Bora; Ciflik, Yelda; Sonugur, Guray; Altan, Tolga; Genc, Omer; Colak, Andac BaturArtificial neural network (ANN) is used to model active three/triple phase boundaries (TPBs) in solid oxide fuel cell (SOFC) electrodes composed of phases with various particle sizes for the first time in the literature. Electrode microstructures comprising catalyst, electrolyte and pore phases with the same volume fraction, but various mean particle sizes are synthetically generated via Dream.3D software and the active TPB densities are measured by COMSOL software to obtain input data for training the ANN models as well as to validate the network results. In this regard, three learning methods of Bayesian regulation (BR), Levenberg-Marquardt (LM) and Scaled conjugate gradient (SCG) with various hidden layer and neuron numbers are examined. Among ANN models with three inputs and one output, the model with BR including one hidden layer and five neurons performs the best. This model revealing an average relative error of only 0.036 is then employed to simulate SOFC electrodes microstructures with new particle sizes not introduced in the learning process. The active TPB densities estimated by ANN are found to agree well with the computed ones. Therefore, ANN modeling is considered as a useful tool for the prediction of active TPB density in SOFC electrodes after a careful selection of backpropagation method and network structure.Öğe Performance evaluation of ejector with different secondary flow directions and geometric properties for solid oxide fuel cell applications(Elsevier, 2019) Genc, Omer; Timurkutluk, Bora; Toros, SerkanThe mixing chamber length, secondary flow tube inclination angle, diffuser length and diverging angle as well as the direction of secondary flow on the ejector performance are numerically studied. The numerical results of 2560 different design points including the all combinations of the selected parameters indicate that the selected ejector geometric parameters have a great impact on the ejector performance. Within the parameter ranges considered, the best performance based on steam to carbon ratio and the entrainment is obtained from the ejector having a mixing chamber length of 30 mm, secondary flow tube inclination angle of 45 degrees, diffuser length of 90 mm and diffuser diverging angle of 5 degrees, regardless of the secondary flow direction. On the other hand, the parallel flow ejector, where the anode exhaust line flow direction is designed to be parallel to the primary flow direction, is found to exhibit slightly higher steam to carbon ratio and entrainment ratio compared to those of the counter flow ejector. Furthermore, it is seen that the parallel flow ejector can offer wider ranges of the ejector geometric parameters considered for a high performance whereas relatively rigid geometric parameters need to be selected for designing a counter flow ejector.Öğe Proposal of a new surfactant for CuO/water nanofluids: Optimization of surfactant ratio and ultrasonication time(Elsevier, 2024) Genc, OmerThis study investigates the influence of different CuO mass ratios, Darvan C-N surfactant ratios, and ultrasonication times on the stability of CuO/water nanofluids. Various CuO mass ratios (%1, %2 and %3) and Darvan C-N surfactant ratios (%0.5, %1 and %1.5), which were used for the first time for CuO/water nanofluids, were employed for nanofluid synthesis. Three different ultrasonication times (30, 60 and 90 min) were applied for each mass ratio and surfactant ratio. The sedimentation behavior of 27 different nanofluids was measured at 5-h intervals over a 360-h period. Optimum surfactant ratios and ultrasonication times for 1%, 2% and 3% CuO mass ratios were determined as 1%-30 min, 1%-60 min and 1%-60 min, respectively. Subsequently, thermal conductivity measurements were conducted within the range of 25-60 degrees C for the determined optimal conditions and modeled using artificial neural networks (ANN). Finally, a new thermal conductivity correlation based on mass ratio and temperature was proposed, with an R value of 0.9952 and a deviation from experimental values of 1.56%. The findings provide valuable insights into optimizing the stability and thermal conductivity of CuO/ water nanofluids.Öğe Quantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network(Elsevier Sci Ltd, 2024) Timurkutluk, Bora; Ciflik, Yelda; Sonugur, Guray; Altan, Tolga; Genc, OmerVirtual solid oxide fuel cell (SOFC) electrode microstructures composed of pore, electrolyte and catalyst phases with various particle sizes and volume fractions are reconstructed to design high-performance electrodes by investigating the role of microstructural properties on the electrodes and thereby the cell performance. The active TPB (triple phase boundary) densities in these microstructures are numerically measured and the data are used to train numerous artificial neural networks established with different model parameters and learning methods. Based on the results of 10,000 trainings of each model, the network that employs a backpropagation method of Bayesian regulation and has 2 hidden layers with 15 neurons is found to be the best one. It is then used to simulate new cases, whose parameters are in the range of those used in training. Further validation of the best network is also performed by considering a few randomly selected cases. The simulation results providing active TPB densities quantitatively are discussed regarding the microstructural properties. The overall results reveal that active TPBs can be increased by reducing the particle size of the phases and volume fraction of any phase should be selected according to the particle size to improve the number of active TPBs.Öğe Synthetical designing of solid oxide fuel cell electrodes: Effect of particle size and volume fraction(Pergamon-Elsevier Science Ltd, 2022) Timurkutluk, Bora; Ciflik, Yelda; Altan, Tolga; Genc, OmerSolid oxide fuel cell (SOFC) electrode microstructures composed of catalyst, electrolyte and pore phases with various microstructural features are synthetically generated and the effects of the mean particle size and volume fraction of each phase on three/triple phase boundaries (TPBs) are computed. For mono-sized particles with an equal volume fraction, the active and total TPB density are found to decrease with increasing the mean particle size due to decreased surface area. However, both are found to be inversely related to the square of the mean particle size. Active TPB densities of 37.62 mu m mu m(-3), 9.27 mu m mu m(-3) and 4.11 mu m mu m(-3) are obtained from the electrode microstructures with mono-sized particles of 0.25 0.50 mu m and 0.75 mu m mean particle size, respectively. Moreover, similar to 94% of the total TPB density is determined to be active regardless of the mean particle size. TPBs for the polydisperse particles with the same volume fraction also show a decreasing trend with the mean particle size in general. However, no significant change is observed in inactive TPB formations even for the largest particle size investigated, revealing almost fully percolated phases can be achieved when the volume fraction of each phase is equal (similar to 33.3%). On the other, when the volume fractions are also varied, the active TPB is shown to be strongly depended on the volume fraction of the phase having the highest mean particle size. In this regard, among the related cases studied, the lowest active TPB density is computed as 0.25 mu m whereas the highest one is measured as 26.64 mu m. (C) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
