Effect of hybrid nanofluids mixture ratio on the performance of a photovoltaic thermal collector

Wole‐Osho, Ifeoluwa; Adun, Humphrey; Adedeji, Michael; Okonkwo, Eric C.; Kavaz, Doğa; Dagbasi, Mustafa

doi:10.1002/er.5619

Cited by 57 publications

(13 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, by controlling the loading concentration of the nanoparticles, and hence the internal structure of the nanofluid, up to 45% of the solar irradiance can potentially be absorbed and converted into electrical and thermal energy (figure 4(b)). It is feasible to consider that, through further refinement of the particle geometries, flow rate conditions, and the collector architecture, higher collector performances could potentially be achieved [13,14,25,31,49]. However, such investigations are outside the scope of this current work.…”

Section: Solar Energy Conversion Performance Of Pvt System With Ag Nanofluidsmentioning

confidence: 97%

“…The majority of the nanofluids investigated and reported in the literature so far have largely focused on exploiting a narrow and monomodal size distribution of nanoparticles including the shape and/or composition [28,29]. This monomodal approach toward developing nanofluids has largely overlooked the possibility of combining a variety of different particle geometries, particle sizes, and/or compositions which could further enhance the eligibility of nanofluids for PVT applications [30,31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the potential of nanofluids containing different Ag nanoparticle size distributions for enhanced solar energy conversion in hybrid photovoltaic-thermal (PVT) applications

2022

View full text Add to dashboard Cite

Hybridising photovoltaic and photothermal technologies into a single system that can simultaneously deliver heat and power represents one of the leading strategies for generating clean energy at more affordable prices. In a hybrid photovoltaic-thermal (PVT) system, the capability to modulate the thermal and electrical power output is significantly influenced by the spectral properties of the heat transfer fluid utilised. In this study, we report on one of the first experimental evaluations of the capability of a multimodal silver nanofluid containing various particle shapes and particle sizes to selectively modulate the solar energy for PVT applications. The diverse set of particle properties led up to a 50.4% enhancement in the solar energy absorbed by the nanofluid over the 300 nm – 550 nm spectral region, where silicon is known to exhibit poor photovoltaic conversion performances. This improved substantially the absorption of solar energy, with an additional 18 – 129 W m-2 of thermal power being generated by the PVT system. Along with the advancements made in the thermal power output of the PVT system, a decrease of 4.7 – 36.6 W m-2 in the electrical power generated by the photovoltaic element was noted. Thus, for every ~11 W m-2 increase of thermal power achieved through the addition of the nanoparticles, a reduction of ~3 W m-2 in the ability to generate clean electricity was sustained by the PVT. Despite the energy trade-offs involved under the conditions of the nanofluid, the PVT system cumulatively harvested 405 W m-2 of solar energy, which amounts to a total conversion efficiency of 45%. Furthermore, the economics of the additional energy harvested through merging of the two systems was found to reach an enhancement of 77% under certain European conditions.

show abstract

Section: Solar Energy Conversion Performance Of Pvt System With Ag Nanofluidsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Evaluation of the potential of nanofluids containing different Ag nanoparticle size distributions for enhanced solar energy conversion in hybrid photovoltaic-thermal (PVT) applications

2022

View full text Add to dashboard Cite

show abstract

“…Mixture ratio is the ratio of individual nanoparticle concentration in the total mix of the hybrid nanofluid. Experimental studies [45][46][47] have shown that to retrieve optimum thermophysical behaviors of hybrid nanofluids, with different nanoparticle types, an ideal mixture ratio should be known. The effect of mixture ratio on thermophysical properties of hybrid nanofluids was first explained by Hamid et al [45].…”

Section: Introductionmentioning

confidence: 99%

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Adun

Wole‐Osho

Okonkwo

et al. 2022

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

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 R 2 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 nanofluidbased solar energy technologies more cost-effective.

show abstract

“…Their study observed a 30% increase in the overall efficiency of the PV/T system by using 0.026wt% Ag-SiO 2 nanofluid compared to when using water alone. A study by Wole-Osho et al [44] investigated the efficiency of a PV/T collector based on the effect of the mixture ratio of Al 2 O 3 -ZnO nanofluids. From the result, it was seen that at a mixture ratio of 0.47, an optimum thermal and electrical efficiency of 55.9% and 13.8%, respectively, were obtained.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and Application of Ternary Nanofluid for Photovoltaic-Thermal System: Comparative Analysis of Energy and Exergy Performance with Single and Hybrid Nanofluids

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Effect of hybrid nanofluids mixture ratio on the performance of a photovoltaic thermal collector

Cited by 57 publications

References 63 publications

Evaluation of the potential of nanofluids containing different Ag nanoparticle size distributions for enhanced solar energy conversion in hybrid photovoltaic-thermal (PVT) applications

Evaluation of the potential of nanofluids containing different Ag nanoparticle size distributions for enhanced solar energy conversion in hybrid photovoltaic-thermal (PVT) applications

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Synthesis and Application of Ternary Nanofluid for Photovoltaic-Thermal System: Comparative Analysis of Energy and Exergy Performance with Single and Hybrid Nanofluids

Contact Info

Product

Resources

About