On heat transfer in Carreau fluid flow with thermal slip : An artificial intelligence (AI) based decisions integrated with lie symmetry - Rehman, Khalil Ur; Shatanawi, Wasfi; Abdul Rahman, Mohd Kasim (Elsevier B.V., 2024)
It is well accepted that non-Newtonian fluid flow narrating differential systems with thermal slip conditions at the surface plays an important role in instabilities subject to polymer extrusion like stick–slip and gross-melt-fracture instabilities. Therefore, the present article contains the artificial neural networking evaluation of the friction at magnetized porous surface having thermal and velocity slip boundary conditions. The Carrea fluid flow is mathematically formulated at a porous surface. The novelty is enhanced by considering velocity slip boundary condition, thermal slip boundary condition, chemical reaction, magnetic field, and heat generation effects. The flow differential equations are reduced by using Lie symmetry analysis. The reduced equations are solved by using the shooting method. The neural networking model is constructed by engaging 132 values of SFC. 92 (70%) is marked for training, and 20 (15%) each is marked for validation and testing. 10 number of neurons are chosen in the hidden layer. Levenberg-Marquardt algorithm is carried out to train the network. The performance of the constructed neural networking model is evaluated by MSE and R. The developed ANN is the best to predict the friction values at the magnetized porous plate. Owning to predicted values of ANN, SFC shows inciting values towards the magnetic field parameter.