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
Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra
Type Article
Keywords
Recognition of flow regime · Two phase flow · X-ray tube · Radial basis function · photon energy spectrum
Researchers saba Amiri، Peshawa Jammal Muhammad Ali، shiavn mohammed، Robert Hanus، Lokman Abdulkareem، Adnan Alhathal Alanezi، Ehsan Eftekhari zadeh، Gholam Hossein Roshani، Ehsan Nazemi، El Mostafa Kalmoun

Abstract

Two phase flows are of particular importance in various research fields. In the current article, a novel system consists of an X-ray tube and one sodium iodide crystal detector with ability of determining type of flow regime as well as void fraction percentage of a two phase flow, is proposed. MCNP-X code was used for physical modelling of the proposed system and its performance. Radial basis function (RBF) was also implemented for analyzing and classifying the obtained data from the proposed system. Counts in each 1 keV energy bin of photon energy spectra in the detector were inserted in RBF as inputs data set and flow regime and void fraction percentage were obtained as the two outputs. After training the RBF network, the system could simultaneously recognize all the flow regimes and predict the void fraction percentage of a modelled liquid–gas two-phase flow with an acceptable error. The proposed methodology in the present paper has three main novelties and advantages over former studies. Firstly, in this system an X-ray tube is used compared to previous studies where one or more radioisotope sources served as radiation source in a radiation based multi-phase flow meter. Secondly, in former works at least two detectors were used to recognize type of flow pattern and meter volume fractions simultaneously, while in this study only one detector is utilized. Thirdly, in this study just one neural network is used, while in other studies more than one network was used.