Void fraction and flow regime determination by means of MCNP code and neural network

One of the non-intrusive and accurate methods of measuring void fraction in two-phase gas liquid pipe flows is the use of the gamma-transmission void fraction measurement technique. The goal of this study is to describe low-energy gamma-ray densitometry using an 241Am source for the determination of void fraction and flow regime in water/gas pipes. The MCNP code was utilized to simulate electron and photon transport through materials with various geometries. Then, a neural network was used to convert multi-beam gamma-ray spectra into a classification of the flow regime and void fraction. The simulations cover the full range of void fraction with Bubbly, Annular and Droplet flows. By using simulation data as input to the neural networks, the void fraction was determined with an error less than 3% regardless of the flow regime. It has thus been shown that multi-beam gamma-ray densitometers with a detector response examined by neural networks can analyze a two-phase flow with high accuracy