Hydrodynamically and thermally fully developed flow of a Sisko fluid through a cylindrical tube has been investigated considering the effect of viscous dissipation. The effect of the convective term in the energy equation has been taken into account, which was neglected in the earlier studies for Sisko fluid flow. This convective term can significantly affect the temperature distribution if the radius of the tube is relatively large. The equations governing the flow and heat transfer are solved by the least square method (LSM) for both heating and cooling of the fluid. The results of the LSM solution are compared with that of the closed form analytical solution of the Newtonian fluid flow case and are found to match exactly. The results indicate that Nusselt number decreases with the increase in Brinkman number and increases with the increase in the Sisko fluid parameter for the heating of the fluid. In case of cooling, Nusselt number increases with the increase in the Brinkman number asymptotically to a very large value, changes its sign, and then decreases with the increase in Brinkman number. With the increase in the non-Newtonian index, Nusselt number is observed to increase.

Genetic algorithm (GA) has been used to determine important attributes of artificial neural network (ANN), such as number of neurons in different hidden layers and division of data for training, validation, and testing. The GA‐assisted ANN (GAAANN) model was used to retrieve third grade fluid (TGF) parameter (A) in a TGF flow problem. The TGF was allowed to flow through two parallel plates, which were subjected to uniform heat flux. The least square method (LSM) was used to solve the governing equations, for specified boundary conditions. In this way, temperature profiles for different values of A were computed by LSM, constituting the direct part of the problem. In the inverse part, the GAAANN model was fed with a temperature profile as input and the corresponding value of A was obtained as output. Four different GAAANN model were developed, and a detailed analysis was done in retrieving the value of A by different GAAANN models. Two very important and commonly used algorithms: Levenberg‐Marquardt (LM) and scaled conjugate gradient are explored for training of the neurons. The entire four GAAANN model were able to retrieve the value of A with different levels of accuracy.

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This study deals with the heat transfer characteristics of magnetohydrodynamic (MHD) flow of a third-grade fluid through parallel plates, subjected to a uniform wall heat flux, but of different magnitudes. The effect of viscous dissipation has been included for both heating and cooling of the fluid. The least square method (LSM) has been adopted for solving the nonlinear equations. The expressions for the velocity and temperature fields have been derived which, in turn, is utilized to evaluate the Nusselt number. The results indicate an increase in Nusselt number for higher values of the third-grade fluid parameter during heating and indicate a reverse trend for cooling. Nusselt number increases with an increase in Hartmann number during heating, whereas it decreases with increasing values of the Hartmann number while cooling the fluid.