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Generation of temperature profile by cascade-forward type artificial neural network in Non-Newtonian fluid flow problem under noisy data
V.K. Mishra, S. Patra, , S. Chaudhuri
Published in American Institute of Physics Inc.
Volume: 2341
Cascade-forward type artificial neural network (CFANN) is explored to generate temperature profile for a non-Newtonian third grade fluid flowing through two parallel plates. Uniform and constant heat fluxes are supplied to both the plates. A semi analytical approach (Least Square Method LSM) is used to solve the governing equations under 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, the perturbed velocity and temperature profiles are fed into CFANN for training. In CFANN, Scaled Conjugate Gradient (SCG) algorithm is used for training the neurons. Once training of CFANN is completed, a velocity profile (not part of the training data) is fed as input, and the temperature profile is obtained as output. The temperature profile obtained from CFANN found to be in very good agreement with the LSM results. This approach is suitable to solve the present types problem with small alterations, and removing the need to solve such problems by LSM or any other time consuming methods. This leads to time savings, and is useful for industries involved in non-Newtonian fluid like polymer, paints, blood, grease etc. © 2021 Author(s).
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JournalData powered by TypesetAIP Conference Proceedings
PublisherData powered by TypesetAmerican Institute of Physics Inc.