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This paper investigates the steady, and three-dimensional non-Newtonian and Newtonian ternary nanofluid flow toward a bidirectionally stretching surface. The non-Newtonian model is developed using the Maxwell fluid model with engine oil and the Newtonian model is developed with water as a base fluid. The analysis is done with Aluminium oxide (Al₂O${}_3)$ (spherical-shaped), Carbon nanotubes (CNT) (cylindrical-shaped), and Graphene (platelet-shaped) as nanoparticles. The mathematical model is articulated and it is further solved with similarity transformations. Furthermore, the analysis considers the influence of the Cattaneo–Christov model, magnetic field, and thermal radiation. A dataset is generated by adjusting relevant parameters through the bvp4c function in MATLAB. Then two different artificial intelligence computing techniques using fuzzy particle swarm optimization (FPSO) and artificial neural network (ANN) are developed to analyze the Nusselt number for Newtonian and non-Newtonian flow. The dominant values of the radiation parameter and thermal relaxation parameter have a significant impact on the HT rate, causing an increase in HT rate. The HT rate is seen to be higher for non-Newtonian TNF. The results of this model can be used in various fields including energy generation, air conditioning, nuclear reactor cooling, electronic device cooling, tissue heat conduction, desalination, crop preservation from freezing, and food processing.

This study investigates the influence of an axial magnetic field on the flow and Heat Transfer (HT) of a Ternary Hybrid Nanofluid in a rotating horizontal annulus between two coaxial tubes, considering the impact of viscous dissipation and thermal radiation. The study examines the use of spherical aluminum oxide (Al₂O$3_{})$ nanoparticles, platelet-shaped graphene and Cylindrical Carbon Nanotubes (CNTs). The velocity and temperature fields were transformed to get a system of Ordinary Differential Equations (ODEs) representing the flow and HT governing equations. A dataset is produced by systematically varying key parameters using the bvp4c function in MATLAB. Following this, two advanced artificial intelligence techniques, Artificial Neural Networks (ANNs) and Fuzzy Particle Swarm Optimization (FPSO), are utilized to forecast the Nusselt number. The effects of various parameters, including the radiation parameter, Reynolds number and Hartmann number on HT and flow characteristics were analyzed. Analysis of data presents that the Nusselt number increases with higher radiation parameter and falls with higher Hartmann number (Ha), Reynolds number and Eckert number. Furthermore, Nusselt number achieves higher values for Quadratic Thermal Radiation (QTR) case in comparison to Linear Thermal Radiation (LTR). It is observed that when value of radiation parameter is increased from 5.5 to 11.5, the Nusselt number values rises by 216.19% (when $\mathrm{\text{Ha}}=4$), 231.34% (when $\mathrm{\text{Ha}}=4.5$) and 249.82% (when $\mathrm{\text{Ha}}=5$) for LTR, whereas the Nusselt number values rises by 153.68% (when $\mathrm{\text{Ha}}=4$), 157.01% (when $\mathrm{\text{Ha}}=4.5$) and 160.70% (when $\mathrm{\text{Ha}}=5$) for QTR. The applications of the nanofluid flow inside a rotating horizontal annulus are seen in heat exchangers, gas turbines, rotating machinery, cylindrical solar collectors, medical devices like centrifuge and rotating drum dryers in food processing.