12 مهر 1401
غلامحسين روشني

غلامحسین روشنی

مرتبه علمی: دانشیار
نشانی:
تحصیلات: دکترای تخصصی / مهندسی هسته ای
تلفن:
دانشکده: دانشکده انرژی

مشخصات پژوهش

عنوان
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
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Recognition of flow regime · Two phase flow · X-ray tube · Radial basis function · photon energy spectrum
پژوهشگران صبا امیری (نفر اول)، پیشه وا جمال محمد علی (نفر دوم)، شیوان محمد (نفر سوم)، رابرت هنوس (نفر چهارم)، لقمان عبدالکریم (نفر پنجم)، عدنان الانزی (نفر ششم به بعد)، احسان افتخاری زاده (نفر ششم به بعد)، غلامحسین روشنی (نفر ششم به بعد)، احسان ناظمی (نفر ششم به بعد)، مصطفی کالمون (نفر ششم به بعد)

چکیده

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.