Numerical modeling of 2D sutterby fluid stagnation-point flow over a stretching surface using levenberg–marquardt backpropagation

Two-dimensional (2D) prediction of Sutterby fluid flow is important in polymer synthesis, industrial coatings, aviation and thermal regulation, and this work utilizes the Levenberg–Marquardt backpropagation (LMBP) method. The calculation of Sutterby nanofluid stagnation point problem over a stretching surface is solved by applying an artificial neural network (ANN) based on LMBP. In this case, the mathematical equations for the non-Newtonian Sutterby dynamic system are dimensionless velocity and energy equations. The key novelty in this investigation is the use of a very powerful soft-computing neural network framework for mapping this particular non-Newtonian configuration, for which the high-fidelity benchmark reference data sets are created using the classical numerical bvp4c solver. The data set is split into 76% for training the network, 13% for validating the network, and 11% for testing the network. The accuracy of the ANN-LMBP is shown by the absolute error charts, which show low mean squared error for both mass and heat transfer systems. The skin friction rises by up to 44.6289% from the angle of the inclined angle of the magnetic field parameter 30° to 90° and energy transfer decreases by 11.1167% respectively. On the other hand, the rate of energy propagation is enhanced by up to 14.4513% with the increment of the thermal radiation factor from 3.0 to 7.0. The error histograms, mean squared errors, correlation coefficients, state transition and regression metrics as a whole certify the accuracy, competency and reliability of the proposed ANN-LMBP approach.