|
|||||
|
|
Void fraction prediction in two-phase flows independent of the liquid phase density changes |
|
| Institution: | 1. Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran;2. Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran;1. Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran;2. Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran;1. Nuclear Safety Research Center, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea;2. Institute for Nuclear Science and Technology, Jeju National University, Arail-dong, Jeju-si, Jeju-do 690-756, Korea;3. Department of Nuclear and Energy Engineering, Jeju National University, Arail-dong, Jeju-si, Jeju-do 690-756, Korea;4. School of Energy Systems Engineering, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea;1. Radiation Application Department, Shahid Beheshti University, G.C., Iran;2. Department of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Iran;3. Computer Department, Engineering Faculty, Islamic Azad University, Kermanshah, Iran |
| Abstract: | Gamma-ray densitometry is a frequently used non-invasive method to determine void fraction in two-phase gas liquid pipe flows. Performance of flow meters using gamma-ray attenuation depends strongly on the fluid properties. Variations of the fluid properties such as density in situations where temperature and pressure fluctuate would cause significant errors in determination of the void fraction in two-phase flows. A conventional solution overcoming such an obstacle is periodical recalibration which is a difficult task. This paper presents a method based on dual modality densitometry using Artificial Neural Network (ANN), which offers the advantage of measuring the void fraction independent of the liquid phase changes. An experimental setup was implemented to generate the required input data for training the network.ANNs were trained on the registered counts of the transmission and scattering detectors in different liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.45% in density variations range of 0.735 up to 0.98 gcm?3. Applying this method would improve the performance of two-phase flow meters and eliminates the necessity of periodical recalibration. |
| Keywords: | Artificial neural network Two-phase flow Multi-layer perceptron Prediction Density independent Void faction |
| 本文献已被 ScienceDirect 等数据库收录！ | |