Abstract
This study presents a method based on gamma-ray densitometry using only one multilayer perceptron artificial neural network (ANN) to identify flow regime and predict volume fraction of gas, water, and oil in multiphase flow, simultaneously, making the prediction independent of the flow regime. Two NaI(Tl) detectors to record the transmission and scattering beams and a source with two gamma-ray energies comprise the detection geometry. The spectra of gamma-ray recorded by both detectors were chosen as ANN input data. Stratified, homogeneous, and annular flow regimes with (5 to 95%) various volume fractions were simulated by the MCNP6 code, in order to obtain an adequate data set for training and assessing the generalization capacity of ANN. All three regimes were correctly distinguished for 98% of the investigated patterns and the volume fraction in multiphase systems was predicted with a relative error of less than 5% for the gas and water phases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.