Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique
Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique
复制标题
时域特征提取方法和人工智能在基于伽马射线吸收技术的两相流量计中的适用性
DOI：
10.1016/j.measurement.2020.108474
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发表时间：
2021
期刊：
影响因子：
5.6
通讯作者：
Ehsan Nazemi
中科院分区：
文献类型：
--
作者：
Mohammad Amir Sattari;G. Roshani;R. Hanus;Ehsan Nazemi
高精度确定流型类型和气体体积百分比是该领域研究人员的重要课题之一。为此，在本文中，模拟了三种不同类型的液气两相流，即环空流、分层流和均相流，气体体积百分比从5%到90%不等。采用蒙特卡罗N粒子（MCNP）程序进行了仿真。计量系统包括一个137cs源，一个耐热玻璃，和两个NaI探测器来记录传输的光子。由于从MCNP模拟中接收到的信号中含有高频噪声，因此采用Savitzky-Golay滤波器来解决这一问题。然后，从两个探测器的记录数据中提取了13个时域特征。由于没有一种特征能够完全分离两种流型，提出了“从两个检测器的记录数据中提取两个不同特征”和“从两个检测器的记录数据中提取三个特征”两种方法。使用这些方法，发现了许多不同的分离案例，并通过S参数区分出最佳的分离案例。最后，利用多层感知器（MLP）的两个人工神经网络（ANN）模型对每种方法进行流态识别和气体体积百分比近似。所提出的方法和网络可以正确诊断所有流型，并预测体积百分比，均方根误差（RMSE）小于0.60。通过时域特征提取和信号处理技术提高两相流量计的精度是本研究的最大优势。
Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid–gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%. Simulations were performed by Monte Carlo N Particle (MCNP) code. Metering system includes one137Cs sources, one Pyrex glass, and two NaI detectors to register the transmitted photons. Because the signals which are received from the MCNP simulations contain high-frequency noises, the Savitzky-Golay filter has been applied to solve this problem. Then, thirteen characteristics in time domain were extracted from the recorded data of both detectors. Since none of features were capable of completely separating the flow regimes, two methods as “extracting two different features from the recorded data of both detectors” and “extracting three features from the recorded data of both detectors” were proposed. Using these methods, many different separator cases were found and the best separator cases were distinguished via S parameter. Finally, two artificial neural network (ANN) models of multilayer perceptron (MLP) were implemented for each method to identify the flow regimes and approximate the gas volumetric percentages. The proposed methodology and networks could diagnose all flow patterns properly, and also predict the volumetric percentage with a root mean square error (RMSE) of less than 0.60. Increasing the precision of two-phase flow meter by extracting time-domain features and signal processing techniques is the most important advantage of this study.