Optimized Wavy Thermal Radiative Analysis of Electrochemically Ternary Hybrid Nanomaterials Affected by an Induced Magnetic Force: An ANN‐Based Levenberg–Marquardt Backpropagation Approach

ABSTRACT This study is motivated by the growing demand for highly efficient thermal management fluids in modern energy systems, microelectronics and industrial heat flow processes, where conventional fluids fail to meet rising performance requirements. To address these limitations, we present a comprehensive analysis of ternary hybrid nanofluids (THNFs) based on sodium alginate (NaC 6 H 7 O 6 ) flowing over a vertically wavy‐type shape under the combined effects of an induced magnetic field and dual‐mode convection. The novelty of this work lies in the systematic, stepwise formulation of nanofluids, progressing from mono‐nanofluids containing single‐wall carbon nanotubes (SWCNTs), to binary hybrids (SWCNT + Ag) and ultimately to ternary systems (SWCNT + Ag + MoS 2 ). This hierarchical approach enables a deeper understanding of the incremental impact of nanoparticle combinations on thermal and mass transport mechanisms. The governing nonlinear partial differential equations (PDEs) are transformed into a system of ordinary differential equations (ODEs) using local similarity transformations. These equations are solved numerically via the bvp4c solver and further analyzed using an artificial neural network (ANN) model based on the Levenberg–Marquardt backpropagation algorithm. The predictive capability and robustness of the ANN are validated through close agreement with the numerical results. The results reveal that the incorporation of ternary nanoparticles significantly enhances both temperature and concentration distributions, indicating superior heat and mass flow characteristics compared to mono‐ and binary nanofluids. These findings underscore the strong potential of THNFs for next‐generation applications in thermal engineering, energy conversion technologies, and high‐performance industrial systems, offering an effective pathway to overcome the limitations of conventional heat transfer fluids.

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Publication Details

Journal
Advanced Physics Research
Published
2026-09-07
DOI
https://doi.org/10.1002/apxr.70183
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimized Wavy Thermal Radiative Analysis of Electrochemically Ternary Hybrid Nanomaterials Affected by an Induced Magnetic Force: An ANN‐Based Levenberg–Marquardt Backpropagation Approach

Umair Khan, Syed M. Hussain, Latif Ahmad, S. Javed et al.
Advanced Physics Research
Nanofluid Flow and Heat Transfer
article

Optimized Wavy Thermal Radiative Analysis of Electrochemically Ternary Hybrid Nanomaterials Affected by an Induced Magnetic Force: An ANN‐Based Levenberg–Marquardt Backpropagation Approach

Umair Khan, Syed M. Hussain, Latif Ahmad, S. Javed, Muhammad Iqbal
article en

Abstract

ABSTRACT This study is motivated by the growing demand for highly efficient thermal management fluids in modern energy systems, microelectronics and industrial heat flow processes, where conventional fluids fail to meet rising performance requirements. To address these limitations, we present a comprehensive analysis of ternary hybrid nanofluids (THNFs) based on sodium alginate (NaC 6 H 7 O 6 ) flowing over a vertically wavy‐type shape under the combined effects of an induced magnetic field and dual‐mode convection. The novelty of this work lies in the systematic, stepwise formulation of nanofluids, progressing from mono‐nanofluids containing single‐wall carbon nanotubes (SWCNTs), to binary hybrids (SWCNT + Ag) and ultimately to ternary systems (SWCNT + Ag + MoS 2 ). This hierarchical approach enables a deeper understanding of the incremental impact of nanoparticle combinations on thermal and mass transport mechanisms. The governing nonlinear partial differential equations (PDEs) are transformed into a system of ordinary differential equations (ODEs) using local similarity transformations. These equations are solved numerically via the bvp4c solver and further analyzed using an artificial neural network (ANN) model based on the Levenberg–Marquardt backpropagation algorithm. The predictive capability and robustness of the ANN are validated through close agreement with the numerical results. The results reveal that the incorporation of ternary nanoparticles significantly enhances both temperature and concentration distributions, indicating superior heat and mass flow characteristics compared to mono‐ and binary nanofluids. These findings underscore the strong potential of THNFs for next‐generation applications in thermal engineering, energy conversion technologies, and high‐performance industrial systems, offering an effective pathway to overcome the limitations of conventional heat transfer fluids.

Advanced Physics Research
Sakarya University (TR), Shaheed Benazir Bhutto University (PK), Islamic University of Madinah (SA), Lebanese American University (LB), Saveetha University (IN)
Islamic University of Madinah
Affordable and clean energy
Openalex Percentile: Top 20%
Nanofluid Flow and Heat Transfer
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