August 9, 2022
Gholam Hossein Roshani

Gholam Hossein Roshani

Academic rank: Associate professor
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Education: Ph.D in Nuclear Engineering
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Faculty: Faculty of energy

Research

Title
Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows
Type Article
Keywords
GMDH neural networks;X-ray tube;Flow pattern;Volume fraction;Gas-oil–water;Three phase flow
Researchers M. M. Roshani، giang phan، Gholam Hossein Roshani، Robert Hanus، Behrooz Nazemi، Enrico Corniani، Ehsan Nazemi

Abstract

In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil–water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.