EXTENSION OF THE NEURAL NETWORKS OPERATING RANGE BY THE APPLICATION OF DIMENSIONLESS NUMBERS IN PREDICTION OF HEAT TRANSFER COEFFICIENTS

2000 ◽  
Vol 18 (3) ◽  
pp. 649-660 ◽  
Author(s):  
Ireneusz ZBICIŃSKI ◽  
Krzysztof CIESIELSKI
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Transfer Coefficients ◽  
Dimensionless Numbers ◽  
Operating Range ◽  
Heat Transfer Coefficients ◽  
The Neural Networks

scholarly journals Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 689 ◽  
Author(s):  
Arianna Parrales ◽  
José Hernández-Pérez ◽  
Oliver Flores ◽  
Horacio Hernandez ◽  
José Gómez-Aguilar ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Nusselt Number ◽  
Artificial Neural Networks ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Artificial Neural ◽  
Input Variables ◽  
Hidden Layer ◽  
Double Pipe

In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg–Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.

Prediction of Two-Phase Heat Transfer Coefficients in a Horizontal Pipe for Different Inclined Positions With Artificial Neural Networks

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Najmeh Sobhanifar ◽  
Ebrahim Ahmadloo ◽  
Sadreddin Azizi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Neural Networks ◽  
Two Phase Flow ◽  
Reynolds Numbers ◽  
Phase Flow ◽  
Transfer Coefficients ◽  
Two Phase ◽  
Heat Transfer Coefficients

This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air–water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results showed that the accuracy between the neural network predictions and experimental data was achieved with mean relative error (MRE) of 2.92% and correlation coefficient (R) that was 0.997 for all datasets, which suggests the reliability of the ANNs as a strong tool for predicting HTCs with two-phase flows.

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