Optimizing the thermo-fluidic properties of ternary hybrid nanofluid for appliance of solar energy through an artificial intelligence integrated numerical solver technique

Yabin Shao, Amjad Ali Pasha, Muhammad Asif Zahoor Raja, Zohaib Arshad, Zahoor Shah, Imran Abbasi, Waqar Azeem Khan, Md Mottahir Alam and Mohammed Istafaul Haque Ansari

Chaos, Solitons & Fractals, 2025, vol. 192, issue C

Abstract: Optimizing heat transfer processes is paramount in contemporary engineering. Nanofluids, engineered by dispersing nanoparticles within a base fluid, have emerged as a promising medium to augment heat transfer capabilities. This investigation delves into the intricate dynamics of a three-dimensional natural convective Ternary Hybrid Nanofluid (THNF) flow across a stretching sheet. The nanofluid comprises a water Base Fluid (BF) fortified with a multilateral mixture of CuO, MgO, and TiO2 nanoparticles. To capture the complexities of the system, a mathematical model incorporating multiple slip conditions, a variable heat source (temperature-dependent and exponentially varying in space), nonlinear thermal radiation, and the influence of a magnetic field is developed. The subsequent Partial Differential Equations (PDEs) are transformed into a dimensionless form through suitable similarity transformations, yielding a system of nonlinear Ordinary Differential Equations (ODEs). These equations are then numerically addressed using the sophisticated Levenberg-Marquardt Backpropagation Scheme (L-MBPS) integrated with Artificially Intelligent Neural Networks (AI-NNs). The performance of the proposed AI-NNs with L-MBPS is evaluated through a series of performance metrics. Mean Squared Error (M2E), Histogram Error Analysis (HEA), and Regression Analysis Plots (RAPs) are employed to assess model accuracy under varying conditions. A comprehensive analysis of the impact of pertinent parameters on velocity and temperature profiles is conducted. Results disclose that x-direction velocity slip exerts a decelerating influence on the fluid flow, while an intensifying stretching ratio diminishes the momentum distribution. Conversely, the thermal field is amplified by both temperature-dependent and exponentially varying heat sources. Notably, THNF demonstrate superior heat conduction characteristics compared to their hybrid and single-nanoparticle counterparts. A positive correlation between the Prandtl number, Nusselt number, and thermal radiation parameter is established. The insights taken from this study hold significant implications for enhancing the performance of thermal systems, including solar energy applications, heat pumps, and heat exchangers.

Keywords: Ternary hybrid nanofluid; Natural convection; Levenberg-Marquardt backpropagation method; Artificial Intelligence Neural Networks; Thermal radiation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077924015133

DOI: 10.1016/j.chaos.2024.115961

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