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Genetic algorithm‐assisted artificial neural network for retrieval of a parameter in a third grade fluid flow through two parallel and heated plates
Author(s) -
Mishra Vijay K.,
Chaudhuri Sumanta
Publication year - 2021
Publication title -
heat transfer
Language(s) - English
Resource type - Journals
eISSN - 2688-4542
pISSN - 2688-4534
DOI - 10.1002/htj.21970
Subject(s) - artificial neural network , conjugate gradient method , algorithm , genetic algorithm , flow (mathematics) , inverse , computer science , inverse problem , boundary value problem , division (mathematics) , square (algebra) , artificial intelligence , mathematics , machine learning , geometry , mathematical analysis , arithmetic
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.