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Summary: Solar photovoltaic‐thermal (PV/T) collectors, are hybrid collectors used to convert solar radiation into usable thermal and electrical energy. Recently, the field of research on PV/T is has focused on improving the efficiency of the PV/T collector by replacing the conventional heat transfer fluids (HTFs) with nanofluids. This article investigates the effect of hybrid nanofluids mixture ratio on the useful energy and overall efficiency of a PV/T collector operating with Al2O3‐ZnO water nanofluid as the HTF. Experiments to measure the thermophysical properties of the hybrid nanofluids were conducted for various temperatures, volume concentrations, and mixture ratios, furthermore, accurate correlation models were proposed. Metrological data and energy output readings collected from the PV solar farm at Cyprus International University were used to validate our model. The study observed that at the optimum mixture ratio (0.47 of Al2O3 in the hybrid), the electrical, thermal, and exergy efficiencies of the PV/T collector are 13.8%, 55.9%, and 15.13% respectively. Also, the cell temperature drops by 21% when the mass flow rate is 0.1 kg/s as compared to when it is 0.01 kg/s. Finally, the study concludes that by using the Al2O3‐ZnO hybrid nanofluid an overall peak thermal efficiency of 91% can be attained, and this represents a 34% enhancement in the collector's performance when compared to water. [ABSTRACT FROM AUTHOR]

The amelioration of photovoltaic (PV) and photovoltaic/thermal (PV/T) systems have garnered increased research interest lately, more so due to the discovery of the thermal property augmentation of nanofluids. The overarching goal of this study is to conduct a comparative analysis of mono, hybrid, and ternary hybrid nanofluids utilized as fluids for heat transfer applications and particularly as cooling mediums in PV/T applications. Al2O3, ZnO, Al2O3-ZnO, and Al2O3-ZnO-Fe3O4 nanofluids are synthesized at 1% volume concentration using the two-step method. The zeta potential tests carried out showed that the fluids have high stability. The numerical model developed in this study was validated using real data culled from Cyprus International University. The findings in this study showed that the Al2O3-ZnO-Fe3O4 ternary hybrid nanofluid and ZnO mono nanofluid were more efficient heat transfer fluids for the PV/T system. The optimum relative electrical PV/T efficiency against that of the PV is 8.13% while the electrical and thermal enhancement recorded in this study was 1.79% and 19.06%, respectively, measured for the ternary hybrid nanofluid based PV/T system. This present study shows that despite the limitation of pumping power and pressure drop associated with nanofluid in thermal systems, the close performance evaluation criterion values as compared with water is positive for practical utilization of nanofluid in PV/T systems. [ABSTRACT FROM AUTHOR]

• Volume concentration have an inverse relationship with SHC of hybrid nanofluids. • Nanoparticle size also affects SHC, though this remains scarcely researched. • Machine learning models give better estimation of SHC than classical models. • There is continuous enhancement of the SHC of hybrid NFs at high temperature. Nanofluids have gained tremendous research interests in diverse fields of study due to their improved properties, especially as heat transfer fluids. Numerous studies have revealed the characteristics, limitations, and applicability of single type nanoparticles and the attributes of their colloidal mixtures. The discovery of the synergistic effect of nanoparticles, as relating to their improved thermophysical properties have been further explored in experimental and numerical studies of hybrid nanofluids. While the major focus has been on thermal conductivity, and viscosity, another extremely important property that gets little study attention is the specific heat capacity, which is a key thermal property in energy systems. Proper understanding of the specific heat capacity (SHC) of hybrid nanofluids is a sine qua non for proper decisions for heat transfer and heat storage applications. The crux of this review study is to give a detailed and comprehensive review of the specific heat capacity of hybrid nanofluids. The synthesis, characterization, and stability of hybrid nanofluids are presented. Furthermore, the different effects like temperature, volume concentration, mixture ratio, and particle size that influence the SHC behaviour of hybrid nanofluids are analyzed in this study. A compilation and discussion on correlation and machine learning models developed for hybrid nanofluids are also presented. Results suggest that volume concentration have an inverse relationship with the SHC of hybrid nanofluids, an increase in temperature improves the SHC of hybrid nanofluids, and this is more significant at temperatures above 50 ℃. Finally, although scarcely researched, the SHC is also affected by the size of the nanoparticles. [ABSTRACT FROM AUTHOR]

The utilization of solar thermal collectors has increased significantly in the last decade. The heat transfer fluids that is used in the design of these systems has notable effects on their performance. The recent discoveries of hybridized nanofluids, give a promising approach for investigating the performance of parabolic trough collectors (PTC), especially at different design conditions and environmental parameters. This study investigates the thermodynamic performance of a Kalina cycle incorporated with a parabolic trough collector that utilizes ternary nanofluid as a heat transfer fluid. The use of mono, hybrid and ternary nanofluids (THNF) in the parabolic trough collector is examined based on their impact on the efficiency of thermal system. The nanofluids considered are Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2. The performance of integrated cycle based on the effect of solar irradiation and volume fraction of the nanofluids are studied and compared with conventional fluids namely: thermal VP1 oil, DowThermal oil, and Salt (7NaNO 3 , 40NaNO 2 , 53KNO 2). In addition, an optimization study is conducted using genetic algorithm to maximize the net power output of the Kalina cycle and the exergetic efficiency of overall system. The results showed that performance of the system which uses nanofluids as a working fluid was higher than the systems using conventional fluids. Furthermore, the results demonstrate that maximum net power output of the system is recorded when Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2 nanoparticles are used. The power outputs were evaluated as 59.72 kW, 56.64 kW, 54.54 kW, and 61.21 kW respectively. The optimization study reveals that the optimum exergetic efficiency when Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2 nanofluids are used can reach up to 21.0%, 17.2%, 10.8%, and 25.1% respectively. • The effects of nanofluids on PTC/kalina is examined. • Effects of volume fraction, solar radiation and temperature is analyzed. • Optimum exergy efficiency measured is for CuO-MgO-TiO2 at 25.1%. [ABSTRACT FROM AUTHOR]

Nanofluids are known to have immense potential for heat transfer applications because of their unique thermophysical properties when compared to the conventional heat transfer fluid. Predicting the thermophysical features like thermal conductivity has posed a challenge to their application. This article addresses some of the challenges posed in their prediction by using data sets from several experimental research on various hybrid nanofluids to train an intelligent neural network. The thermal conductivity of hybrid nanofluids is predicted using seven different input variables namely, volume concentration, temperature, the acentric factor of the base fluid, nanoparticle bulk density, mixture ratio of particles, the thermal conductivity, and size of nanoparticles. 715 experimental data points from studies using different hybrid nanoparticles are used in developing a multi-layer perceptron artificial neural network (ANN) and support vector regression (SVR) models. The performance validation of the models is computed using the mean square error (MSE) and the coefficient of determination (R 2 ). The performance result showed an R 2 value of 0.99997 and 0.99788 in the validation phase of the ANN and SVR model, respectively. This indicates that the models are capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations. Finally, a universal formula using MLP-ANN for predicting the thermal conductivity of hybrid nanofluids is presented. [ABSTRACT FROM AUTHOR]