What question did this study set out to answer?

This research aims to enhance the performance of photovoltaic-thermal systems using advanced nanofluids and a predictive neural network model.

February 28, 2026Modern Physics Letters B0 citations

View Full Paper

A model development of High-Efficiency Photovoltaic Thermal Collectors Using artificial neural network to improved Solar Thermal Radiation

SCSaleh ChebaaneUniversity of Ha'il NAN. ALSAIFPrincess Nourah bint Abdulrahman University SKSana Ben KahlifaBuraydah Colleges

Key Points

This research aims to enhance the performance of photovoltaic-thermal systems using advanced nanofluids and a predictive neural network model.
Developed a ternary hybrid nanofluid with ZrO2, Cu, and Al2O3 nanoparticles in CMC-water.
Applied the Cattaneo-Christov model for heat transport effects.
Numerically solved governing equations using the Laplace-Hankel transform method.
Trained an artificial neural network with a comprehensive dataset from numerical solutions.
Achieved a 34.8% increase in the average Nusselt number with the ternary hybrid nanofluid.
Enhanced heat transfer rate by 29.6% compared to the base fluid.
Improved temperature distribution by 27.4% with thermal radiation inclusion.

Abstract

The continuous rise in global energy demand and the thermal degradation of photovoltaic modules under high solar irradiation have created a strong need for advanced photovoltaic-thermal (PV/T) systems with improved heat removal and energy conversion efficiency. Conventional working fluids exhibit limited thermal conductivity, which restricts the overall performance of PV/T collectors and shortens module lifespan. To address this challenge, the present study investigates the use of a ternary hybrid nanofluid based on Carboxymethylcellulose (CMC)-water containing ZrO 2 , Cu, and Al 2 O 3 nanoparticles, combined with the Cattaneo-Christov thermal relaxation model to capture non-Fourier heat transport effects. The model incorporates the influence of magnetohydrodynamic (MHD) flow, solar thermal radiation, and internal heat generation. The governing nonlinear partial differential equations were transformed and solved numerically using the using the Laplace-Hankel transform method, with gridindependence and convergence verification. The artificial neural network (ANN) demonstrated excellent predictive capability, with high correlation coefficients for the training, validation, and testing datasets. The model was trained using a comprehensive dataset generated from the numerical LHTM solution and optimized with the Levenberg-Marquardt backpropagation algorithm. The strong agreement between predicted and target values, together with low error metrics, confirms the reliability and generalization ability of the proposed ANN model. The Manuscript (.tex, .docx, .doc) Click here to access/download;Manuscript (.tex, .docx, .doc);correction Manuscript 15-2-2026 updated.docx results reveal that, at a total nanoparticle volume fraction of 𝜙 = 0.03, the ternary hybrid nanofluids increases the average Nusselt number by 34.8% and the heat transfer rate by 29.6% compared with the CMC-water base fluid. The inclusion of thermal radiation enhances the temperature distribution by 27.4%.

Perguntar à IA

Bookmark

View Full Paper

Cite This Study

Chebaane et al. (Fri,) studied this question.

synapsesocial.com/papers/69a288170a974eb0d3c04219 https://doi.org/https://doi.org/10.1142/s0217984926500946

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Perguntar à IA

Bookmark

View Full Paper