Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics

Knowledge of the flow regime and the volume fraction in multiphase flow is of fundamental importance in predicting the performance of many systems and processes. This study is based on gamma-ray pulse height distribution pattern recognition by means of an artificial neural network. The detection system uses appropriate one narrow beam geometry, comprising a gamma-ray source and a NaI(Tl) detector. The models for annular and stratified flow regimes were developed using MCNPX code, in order to obtain adequate data sets for training and testing of the artificial neural network. Several experiments were carried out in the stratified flow regime to validate the simulated results. Finally, Ansys-CFX was used as computational fluid dynamics software to simulate two different volume fractions, which were modeled and transformed in voxels and transferred to MCNPX code. The use of computational fluid dynamics is of great importance, because it brings the studies closer to the reality. All flow regimes were correctly recognized and the volume fractions were appropriately predicted with relative errors less than 1.1%. •Artificial neural network (ANN) training led to accurate prediction of volume fraction.•ANN training provided good accuracy in flow regime identification.•Computational fluid dynamics (CFD) helped to make the studies closer to reality.•The use of CFD was of great importance for flow study.•The results showed the viability of working with Monte Carlo code and CFD.