Generation of Temperature Profile by Artificial Neural Network in Flow of Non-Newtonian Third Grade Fluid Through Two Parallel Plates Under Noisy Data

Abstract

Artificial neural network (ANN) is explored to generate temperature profile for a non-Newtonian third grade fluid flowing through two parallel plates. Both the plates are supplied with a constant and uniform heat flux. A semi-analytical approach (Least Square Method LSM) is used to solve the governing equations under with required boundary conditions. The velocity and the temperature profile obtained from the LSM are perturbed by different levels of noise to mimic error in measurement. Thus, velocity and temperature profiles are fed into ANN for training. In ANN, scaled conjugate gradient (SCG) algorithm is used for training the neurons. Once training of ANN is completed, an unknown velocity profile is fed as input, and the temperature profile is obtained as output. The temperature profile obtained from ANN found to be in very good agreement with the LSM results. This approach is suitable to solve the present problem with small alterations, and removing the need to solve such problems by LSM. This leads to time saving and useful for industries involved in non-Newtonian fluid like polymer, paints, blood, grease, etc.