A Physics-Informed Neural Network Framework for MHD Casson Ternary and Tetra Hybrid Nanolubricant Flow
Keywords:
PINN, Nanolubricants, MHD Flow, Joule Heating, Thermal Radiation.Abstract
The heat and mass transport properties of Casson hybrid nanofluids flowing across a stretched surface in the presence of thermal radiation, Joule heating, and a magnetic field are examined in this work. We look at two sophisticated nano-lubricant arrangements. , ZnO, and SiC nanoparticles suspended in engine oil make up the first ternary hybrid nanofluid. Graphene nanoplatelets (GNPs) are added to the ternary mixture to create the second tetra hybrid nanofluid. Comparing the effects of nanoparticle composition on energy dissipation mechanisms, flow behavior, and thermal conductivity is the aim. Joule heating, radiative heat flux, thermo-diffusion, and chemical reaction effects are all included in the mathematical formulation. The controlling nonlinear partial differential equations are reduced to a linked system of ordinary differential equations by means of appropriate similarity transformations. A Physics Informed Neural Network (PINN) method designed especially for nanofluid lubrication systems is used to solve these equations. By directly integrating the governing physical laws into the loss function, the suggested PINN architecture enables the simultaneous elimination of boundary condition errors and equation residuals. Computational efficiency and solution stability are improved by this two-way optimization. Also wed did Numerical Validation of the PINN Solver Comparing the tetra hybrid nanofluid to the ternary formulation, numerical results show that the former offers noticeably greater thermal enhancement and lower entropy generation. GNPs' remarkable heat conductivity and enormous surface area are primarily responsible for this performance enhancement. On the other hand, the ternary hybrid nanofluid shows moderate temperature gradients and comparatively constant viscosity behavior. For complicated nonlinear thermal-fluid problems in lubrication applications, the PINN framework provides a dependable computational tool with good convergence and prediction accuracy overall.
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