Effects of Salinity on Measurement of Water Volume Fraction and Correction Method Based on RBF Neural Networks

摘要

The gamma ray dual modality densitometry was presented to measure salinity independent of water volume fraction in pipe flows. The simulation geometries of the dual modality densitometry were built using Monte Carlo software Geant4. Computer simulations were carried out with different types of salt and various salinity. The results show that type of salt and salinity have significant effects on the water volume fraction measured by dual modality densitometry. By means of measuring attenuation of transmitted and scattered radiation of dual modality densitometry, the information about the salinity changes can be obtained. But it is difficult to calculate WVF and salinity from dual modality densitometry models. The RBF neural networks were used to predict salinity and water volume fraction. The results show that the predicting values fit true values well. It was demonstrated that the water volume fraction measuring errors caused by salinity can be reduced by using RBF neural networks.