ISSN Online: 2377-424X
International Heat Transfer Conference 12
Prediction of void fraction in air/water two-phase flows at elevated temperatures
Abstract
Radial basis function neural networks have been used to interpolate within the range of available experimental void fraction data for a two-phase air/water flow passing through an upward vertical pipe. The independent parameters are in terms of dimensionless groups such as volumetric flow ratio, density ratio, and Weber number. A comparison between the experimental and predicted data reveals an overall average error of 3.6% for training and 5.8% for unseen data. In addition, the trend of both predicted results and experimental data are consistent.