Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry

Abstract

This paper describes low-energy gamma-ray densitometry using a 241Am source for the determination of void fraction and flow regime in oil/gas pipes. Due to the reduced shielding requirements of this method compared to traditional gamma-ray densitometers using 137Cs sources, the low-energy source offers a compact design and the advantage of multi-beam configuration. One of the aims of this investigation was to demonstrate the use of a neural network to convert multi-beam gamma-ray spectra into a classification of the flow regime and void fraction, as well as to determine which detector positions best serve this purpose. In addition to spectra obtained from measurements on a set of phantom arrangements, simulated gamma-ray spectra were used. Simulations were performed using the EGS4 software package. Detector responses were simulated for void fractions covering the range from 0 - 100%, and the simulations were performed with homogeneous, annular and stratified flows. Neural networks were trained on the simulated gamma-ray data and then used to analyse the measured spectra. This analysis allowed determination of the void fraction with an error of 3% for all of the flow regimes, and the three types of flow regime were always correctly distinguished. It has thus been shown that multi-beam gamma-ray densitometers with detector responses examined by neural networks can analyse a two-phase flow with high accuracy.