مشخصات پژوهش

عنوان	Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Helically coiled tube, Artificial neural network, Heat transfer, Friction factor
چکیده	In this study, Artificial Neural Network (ANN) models were developed to predict the heat transfer and friction factor in helically coiled tubes. The experiments were carried out with hot fluid in coiled tubes which placed in a cold bath. Coiled tubes with various curvature ratios and coil pitches (nine Layouts) were used. The output data of the ANNs were Nusselt number and friction factor. The validity of the method was evaluated through a test data set, which were not employed in the training stage of the network. Moreover, the performance of the ANN model for estimating the Nusselt number and friction factor in the coiled tubes was compared with the existing empirical correlations. The results of this comparison show that the ANN models have a superior performance in predicting Nusselt number and friction factor in the coiled tubes.
پژوهشگران	مسعود رحیمی (نفر دوم)، رضا بیگزاده (نفر اول)