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Modeling of overall heat transfer coefficient of a concentric double pipe heat exchanger with limited experimental data by using curve fitting and artificial neural network combination

The modeling accuracy of artificial neural networks was evaluated by using limited heat exchanger data acquired experimentally. The artificial neural networks were used for predicting the overall heat transfer coefficient of a concentric double pipe heat exchanger where oil flowed inside the inner tube while the water flowed in the outer tube. In the cases of parallel and counter flows, the experimental data were collected by testing heat exchanger in wide range of operating conditions. Curve fitting and artificial neural network combination was used for the estimation of the overall heat transfer coefficient to compensate the experimental errors in the data. The curve fitting was used to detect the trend and generate data points between the experimentally collected points. The artificial neural network was trained better from the generated data set. The feed forward type artificial neural network was trained by using the Levenberg-Marquardt algorithm. Two backpropagation network type artificial neural network algorithms were also used, and their performance were compared with the estimation of the Levenberg-Marquardt algorithm. The average estimation error between the predictions and the experimental data were in the range of 1.31e-4 to 4.35e-2%. The study confirmed that curve fitting and artificial neural network combination could be used effectively to estimate the overall heat transfer coefficient of heat exchanger.
PAPER REVISED: 2018-02-28
PAPER ACCEPTED: 2018-03-12
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