A numerical and artificial intelligence approach to study flow of ternary hybrid nanofluid (Al 2 O 3 -graphene-CNT/water) inside an anisotropic porous channel

This paper examines the effects of inclined magnetic field, channel rotation and viscous dissipation on the fluid flow of the ternary hybrid nanofluid (THNF) inside an anisotropic porous channel. The resulting nonlinear partial differential equations are further reduced to a set of ordinary differential equations under similarity transformations. These equations are solved numerically via MATLAB's ‘bvp4c’ function and results are further analysed using artificial neural network (ANN) model. The effects of key parameters in velocity profiles, temperature profiles, local skin friction and Nusselt number are discussed for the following: Hartmann number, Taylor's number, Darcy numbers, Eckert number and nanoparticles volume fraction. From the finding it is seen that the highest value of the correlation coefficient using ANN are R2 = 0.99999999 and R2 = 999999987, while the lowest value of MSE using both MSE = 5.7503 × 10−8 and MSE = 3.5238 × 10−7 respectively, signifying the prediction with high precision. The application of flow within a channel holds significant applicability in systems involving physiological flows such as blood transport through anisotropic biological tissues or fluid motion within rotating organs.