Void fraction prediction using prompt gamma neutron activation analysis and artificial intelligence.

This paper presents the simulation of a water-gas biphasic system in annular and stratified flow regimes using a neutron beam from a241Am–Be radiation source. The objective is to evaluate the potential of prompt gamma neutron activation analysis (PGNAA) and artificial intelligence-based algorithms in flow measurements. A measurement geometry consisted of a neutron flat source with parallel emission and a spherical detector was developed using the MCNP6 code. Prompt gamma rays spectra were used as input data of a feed-forward multilayer perceptron (MLP) artificial neural network aiming to predict void fraction and to identify the investigated flow regimes. The results showed that the MLP was able to predict void fraction, for both flow regimes, with mean absolute percentage error within 1.58%. In addition, the same MLP achieved 100% of success in flow regime identification. Therefore, it is possible to combine PGNAA with MLP in order to investigate flow characteristics. • Simulated water-gas system using neutron beam from a241Am–Be radiation source. • Neutron source with parallel emission and a spherical detector using MCNP6 code. • Neutron spectra were used to predict void fraction and to identify flow regimes. • Prompt gamma neutron activation analysis and AI to investigate flow characteristics. [ABSTRACT FROM AUTHOR]