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Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluid-based solar energy technologies more cost-effective. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids have been known to provide perceptibly increased heat transfer characteristics over conventional fluid, and so have been experimentally applied to solar collector applications. Recently, attention has grown towards three-particle hybrid nanocomposites, to investigate their increased thermophysical properties for heat transfer applications. This study numerically investigates the practical application of CuO-MgO-TiO2 ternary nanofluids in a photovoltaic thermal (PV/T) collector. The proposed model explores the effect of volume fraction, solar irradiation, and mass flow rate on the performance of the collector system. The result showed an improved electrical efficiency of 13.54%. 58.38% and 15.68% thermal efficiency and energy efficiency respectively were also gotten, calculated at an optimum volume fraction of 0.01. The maximum cell temperature drop was 8.24 °C measured at a mass flow rate of 0.1 kg s−1. The study also concludes that a maximum increase of 11.14% of the total PV/T system was achieved by the use of ternary nanofluid. The study also investigated the pressure drop and pumping power of the PV/T collector with and without nanofluid. The result showed that a lower pressure drop and pumping power for the CuO–MgO–TiO2 ternary nanofluid which makes it more desirable as compared to the Al2O3–ZnO hybrid nanofluid. Finally, it is shown that the performance of the PV/T collector depends highly on the volume fraction of the ternary nanofluid. [ABSTRACT FROM AUTHOR]

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]

The growing interest in hybrid nanofluids is due to the synergistic effects of nanoparticles, which could give them better heat transfer properties as compared to base fluids, and conventional nanofluids. Several factors like temperature and volume fraction have been used in explaining the behaviours of hybrid nanofluids, however, the need to investigate comprehensively, the mixing ratios of hybrid nanofluids, remains a critical research area in the development of efficient nanofluids as heat transfer fluids. In this study, three mixture ratios of 1:1:1 (33.3% Al 2 O 3 , 33.3% ZnO, 33.3% Fe 3 O 4), 1:2:1 (25% Al 2 O 3 , 50% ZnO, 25% Fe 3 O 4), and 1:1:2 (25% Al 2 O 3 , 25% ZnO,50% Fe 3 O 4) ternary hybrid nanofluid (THNF) are synthesized at volume concentrations of 0.5%, 0.75%, 1%, and 1.25%. All experiments were carried out at a temperature range between 25 °C-65 °C. The effect of temperature, volume concentration, mixture ratio, are examined, as well as the development of a machine learning model for accurate prediction. The thermal conductivity and dynamic viscosity behaviour of the THNF were investigated. The result showed that temperature and volume concentration significantly affected the thermophysical properties of the fluid. The optimum Thermal Conductivity enhancement (TCE) was retrieved for the 1:1:1 THNF, at 36.018%. The 2:1:1, and 1:2:1 mixture ratios had a 32.92%, and 31.68% TCE respectively. At 1% volume concentration, the optimum TCE (as compared with water) for the mono, hybrid, and THNF measured are 18.98%, 28.58%, and 32.45% respectively. It is seen that the least viscosity was recorded for the 1:1:1 mixture ratio (0.001 Pa.s), while the highest viscosity was measured for the 2:1:1 THNF mixture ratio (0.021 Pa. s). The Gaussian process regression (GPR) gave an excellent prediction showing an R2 value of 0.9656, and 0.934 for the thermal conductivity and dynamic viscosity prediction respectively. In terms of application to solar thermal systems, the low viscosity of 1:1:1 Al 2 O 3 –ZnO-Fe 3 O 4 THNF makes that a low-pressure drop and pump work is required in practical applications of the 1:1:1 Al 2 O 3 –ZnO-Fe 3 O 4 THNF. [Display omitted] • Least viscosity was recorded for the 1:1:1 mixture ratio. • Optimum Thermal Conductivity enhancement was retrieved for the 1:1:1 THNF. • Optimum prediction was retrieved using the machine learning algorithm. • Highest viscosity was measured for the 2:1:1 THNF mixture ratio (0.021 Pa. s). [ABSTRACT FROM AUTHOR]

Decade research on nanofluids have solidified the claim that they have excellent heat transfer properties. The properties of mono nanofluids can be optimized by varying the volume concentration of the nanoparticle, however, there is a limit to this due to the challenge of the trade-off of the net negative of viscosity increase. In resolving this limitation, hybrid nanofluids have been synthesized by researchers to maximize the unique properties of different types of nanoparticles. The early days of hybrid nanofluid experimentations consisted of two-particle nanofluids, however, in recent years, a new class of working fluids has been discovered and extensively explored, consisting of three solid nanoparticles dispersed in a conventional fluid. These fluids are referred to as 'ternary nanofluid, 'ternary hybrid nanofluid' or 'trihybrid nanofluids'. This paper gives a comprehensive review of all studies that have synthesized this new class of nanofluids. The major focus in this study was given to their synthesis methods, characterizations, stability tests, thermophysical properties (thermal conductivity, specific heat capacity, and viscosity). This study also reviews the heat transfer applications of ternary nanofluids. The ternary nanofluids are shown to significantly increase the thermal conductivity and heat transfer properties of base fluids. The type and sizes, as well as mixture ratio of nanoparticles are important factors that determine the effectiveness of ternary nanofluids. Several areas still need to be covered for proper understanding of the hybridization effects of three particle nanofluids, like the effect of combining different nanoparticle types, particle sizes, nanoparticle shape, and base fluids. [ABSTRACT FROM AUTHOR]

This study presents an experimental nanoparticle synthesis and the numerical analysis of a parabolic trough collector (PTC) operating with olive leaf synthesized TiO2/water nanofluid. The PTC is modeled after the LS-2 collector for various operating conditions. An analysis of the heat transfer and entropy generation in the PTC is carried out based on the first and second laws of thermodynamics for various parameters of nanoparticle volumetric concentration (0 ≤ φ ≤ 8%), mass flow rate (0.1 ≤ m ≤ 1.1kg/s), and inlet temperatures (350-450 K) under turbulent flow regime. The effect of these parameters is evaluated on the Nusselt number, thermal losses, heat convection coefficient, outlet temperature, pressure drop, entropy generation rate, and Bejan number. The results show that the values of the Nusselt number decrease with higher concentrations of the nanoparticles. Also, the addition of nanoparticles increases the heat convection coefficient of the nanofluid compared to water. The thermal efficiency of the system is improved with the use of the new nanofluid by 0.27% at flow rates of 0.1kg/s. The entropy generation study shows that increasing the concentration of nanoparticles considerably decreases the rate of entropy generation in the system. It is also observed that increasing the volumetric concentration of nanoparticles at low mass flow rates has minimal effect on the rate of entropy generation. Finally, a correlation that provides a value of mass flow rate that minimizes the entropy generation rate is also presented for each values of inlet temperature and nanoparticle volumetric concentration. [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]