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.
Although curved pipes are used in a wide range of applications, flow in curved pipes is relatively less well known than that in straight ducts. This paper presents a computational fluid dynamics study of isothermal laminar single-phase flow of water in a hollow helical pipe at various Reynolds numbers. The ranging of Reynolds numbers of fluid was from 703.2 to 1687.7. The three dimensional governing equations for mass and momentum have been solved. It was found that with increasing Reynolds number and creation of centrifugal forces, a high velocity and pressure region occurs between two tubes, at the outer side of the hollow helical pipe walls. Friction factor decreases as the tendency for turbulence increases.
Flow patterns and local heat transfer coefficients were measured for air–water flow in a horizontal pipe. A technique based upon a cascade neural network was developed for simultaneously recognition of the flow pattern (FP) and the corresponding heat transfer coefficient (hTP) for each FP. The results show good agreement between the estimated and the experimental values with 98.35% accuracy for FP and 95.6% accuracy for hTP. The results were compared with the Kim and Ghajar heat transfer correlation. The findings revealed that the proposed model is efficient and predicts flow more accurately than the Kim and Ghajar correlation.
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