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    Prediction of heat transfer in a circular tube with aluminum and Cr-Ni alloy pins using artificial neural network
    (Taylor & Francis Inc, 2021) Berber, Adnan; Gurdal, Mehmet; Bagirsakci, Kazim
    This work aims to estimate the experimental heat transfer coefficients of a circular channel using artificial neural network. The experiments are carried out at a forced turbulent flow regime of 10,000 < Re <50,000. The obtained experimental Nusselt numbers are compared using the ANN (Artificial Neural Network). In the developed ANN structure are showed mean square error (MSE), average relative deviation (ARD %), and correlation coefficient (R-2) in modeling of overall experimental datasets of Nusselt number. As a result, it is observed that the heat transfer correlation predicted by ANN are sufficiently consistent with the experimental results.