化工进展2025，Vol.44Issue(4)：1786-1793,8.DOI:10.16085/j.issn.1000-6613.2024-1730
基于XGBoost的气液两相流流型超声识别方法
Ultrasonic identification of gas-liquid two-phase flow patterns based on XGBoost
摘要
Abstract
The phenomenon of gas-liquid two-phase flow widely exists in many fields such as petroleum extraction and transportation,energy and chemical industry,aerospace and so on.Based on the finite element multiphysics coupling simulation technology,a two-dimensional geometric section simulation model of typical gas-liquid two-phase full-steady-state flow was established to recognize the gas-liquid two-phase flow pattern.The design of the ultrasonic transducer transmitting and receiving modes with two transmitters and four receivers and the sampling mode of three-time combined sampling was used to test the gas-liquid two-phase flow patterns,and the feature mapping of the sound pressure signals combined with the ultrasonic propagation mechanism in gas-liquid fluids was used as the input parameter of the extreme gradient boosting(XGBoost)classification algorithm to realize the classification of the gas-liquid two-phase flow patterns into four types:laminar flow,vesicular flow,annular flow,and plug flow.On this basis,the two types of laminar flow and plug flow were subdivided by exploiting the ultrasonic mechanism,i.e.,smooth laminar flow,undulating laminar flow and plug flow,segmental plug flow,so as to realize the classification of all flow types of gas-liquid two-phase flow.Comparison of ultrasonic propagation mechanism features and time-frequency domain features showed that the ultrasonic-based multi-reception distributed ultrasonic testing system constructed in this study was able to extract ultrasonic mechanism feature parameters with better flow pattern recognition,and had a higher recognition rate compared with the time-frequency features.The recognition rate of gas-liquid two-phase flow,laminar flow,vesicular flow,annular flow,and plug flow,was 98.5％.Among these,the highest recognition rate of the smooth laminar flow and undulating laminar flow was 96.15％,while that of plug flow and segmental plug flow reached 96.85％.关键词
超声测试/有限元仿真/气液两相流/极限梯度提升树/流型识别Key words
ultrasonic testing/finite element simulation/gas-liquid two-phase flow/extreme gradient boosting(XGBoost)/flow pattern identification分类
能源科技引用本文复制引用
苏茜,白凡,刘振兴,刘彰..基于XGBoost的气液两相流流型超声识别方法[J].化工进展,2025,44(4):1786-1793,8.基金项目
国家自然科学基金(61903281,61901423,51907144) （61903281,61901423,51907144）
湖北省自然科学基金(2019CFB145) （2019CFB145）
中国博士后科学基金(2018M642932) （2018M642932）
武汉市知识创新专项曙光计划(2022010801020311). （2022010801020311）
