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Ternary hybrid nanofluids offer strong potential for heat management due to their thermophysical properties. This numerical research analyses the unsteady flow of these nanofluids across a heated sheet, accounting for magnetic effects, chemical reactions, variable viscosity, and thermal conductivity. The fluid dynamics problems were solved using physics-informed neural networks, which provide a mesh-free computational approach. Among the tested mixtures, a combination of multi-walled carbon nanotubes, aluminium oxide, and titanium dioxide delivered the highest thermal performance improvement at 15.097 per cent. Statistical and sensitivity analyses revealed that temperature-dependent viscosity is the primary driver of fluid momentum and transport behaviours, followed by flow unsteadiness and thermal conductivity. The neural network methodology proved reliable and accurate for modelling these intricate heat transfer scenarios.
Better cooling and thermal management technologies are essential across modern engineering systems. By showing how specific nanoparticle combinations boost heat transfer, and by proving that machine learning can accurately simulate these fluid behaviours without traditional computational grids, this work helps improve simulation efficiency for advanced cooling applications.
This work relates to advanced thermal management systems, potentially benefiting designers of industrial cooling systems and thermal engineers. Because the findings are entirely computational and focus on mathematical modelling alongside sensitivity analysis, the research sits at an early theoretical stage. Moving towards real-world adoption will require physical experimental validation and practical testing within hardware cooling architectures.
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The investigation of ternary hybrid nanofluids (THNFs) has garnered significant attention due to their exceptional thermophysical properties and potential applications in advanced thermal management systems. This study presents a comprehensive numerical investigation of unsteady stagnation-point flow of a ternary hybrid nanofluid over a convectively heated sheet, incorporating temperature-dependent viscosity and thermal conductivity variations, magnetohydrodynamic (MHD) effects, Brownian motion, thermophoresis, viscous dissipation, and chemical reaction. The governing partial differential equations are transformed into a system of coupled nonlinear ordinary differential equations using similarity transformations. Physics-Informed Neural Networks (PINNs) are employed to obtain accurate numerical solutions, with the framework incorporating the governing equations, boundary conditions, and informed initial guesses into the loss function. The thermal performance of various ternary nanofluid compositions is evaluated, with MWCNTs/Al₂O₃/TiO₂ demonstrating the highest efficiency enhancement of 15.097%. A comprehensive parametric analysis reveals that the viscosity variation parameter (Λ) exerts the most dominant influence on momentum and transport characteristics, followed by unsteadiness (β) and thermal conductivity (ε). Response Surface Methodology (RSM) coupled with Central Composite Design (CCD) establishes quadratic regression models for skin friction, Nusselt, and Sherwood numbers, with ANOVA confirming statistical significance (R² > 0.987). Sensitivity analysis quantifies the relative impact of parameters, showing that Λ positively influences all transport quantities, while ε negatively affects skin friction and Nusselt number. This study demonstrates that PINNs provide a robust, mesh-free computational framework for complex nanofluid flows, offering superior accuracy and flexibility compared to traditional numerical methods.
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DOI: 10.64388/irev10i2-1722176
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