Work

Thermal optimization of electrically conducting Ag/Al2O3 Ostwald fluid across solar surface using ANN for solar thermal application

Year: 2025

Type: article

Abstract: This study develops an artificial neural network framework to model the thermo-hydrodynamic behavior of an electrically conducting Ostwald–de Waele hybrid nanofluid over a solar-heated surface under mixed convection, nonlinear thermal radiation, Joule heating, and dissipative effects. The governing pressure-dependent, time-varying flow equations are solved using a finite difference method, and the resulting data are employed to train and validate the artificial neural network model. The analysis shows that velocity increases with a higher Grashof number and temperature decreases with the radiation parameter. The ANN demonstrates excellent predictive accuracy, with mean squared error values on the order of regression coefficients above 0.996, and absolute errors confined within across training, testing, and validation phases. These findings confirm the strong correlation between numerical and ANN-based predictions, validating the robustness of the proposed framework. This work is significant for solar thermal applications by accurately predicting non-Newtonian nanofluid behavior, with novelty in coupling ANN and finite difference simulations to deliver an efficient, precise, and practical tool for optimizing next-generation collectors.

Source: Journal of Materials Research and Technology

DOI: 10.1016/j.jmrt.2025.10.053

Authors: Malik Zaka Ullah, Muhammad Shakib Arslan, Zaheer Abbas, Monairah Alansari

Institutions: King Abdulaziz University, Islamia University of Bahawalpur

Language: English

Topic: Nanofluid Flow and Heat Transfer

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,640