Mass Flow Rate Measurement of Gas-Liquid Two-Phase Flow Using Multi-Sensor Data Fusion and Soft Computing Model
文献类型：期刊论文
| 作者 | Suo, Peng4; Sun, Jiangtao3,4; Shi SY(史仕荧)2; Lu, Fanghao4; Shen, Mengxian1; Zhang, Xiaokai1; Liang, Te1; Li, Xiaolin1; Zhu, Zihan4; Sun, Shijie3,4 |
| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 25期号:13页码:25314-25323 |
| 关键词 | Sensors Computational modeling Accuracy Capacitive sensors Ultrasonic variables measurement Data integration Support vector machines Eigenvalues and eigenfunctions Data models Sun Flow rate measurement gas-liquid two-phase flow machine learning multiple sensors soft computing model |
| ISSN号 | 1530-437X |
| DOI | 10.1109/JSEN.2025.3574085 |
| 通讯作者 | Sun, Jiangtao(jiangtao_sun@buaa.edu.cn) ; Xu, Lijun(lijunxu@buaa.edu.cn) |
| 英文摘要 | This article presents a novel method for measuring the mass flow rate of gas-liquid two-phase flow based on the multi-sensor data fusion and soft computing model. A multi-sensor system comprising a throat-extended Venturi tube (TEVT) and a dual-modality electrical sensor (DMES) has been developed for gas-liquid two-phase flow measurement. Soft computing models are employed to address the intricate non-linear mapping between the measurement data and flow parameters. Initially, flow regimes are identified based on the time-domain features of the multi-sensor data using a support vector machine (SVM). Subsequently, mass quality is derived from the multi-differential pressure fluctuations and the eigenvalue sequence of the normalized electrical matrices, employing a hybrid neural network comprising a convolution neural network and a deep neural network (DNN). Ultimately, gas/liquid over-reading (OR) is predicted via extreme gradient boosting (XGBoost) using multi-differential pressure ratios. The gas and liquid mass flow rates are subsequently derived from the preceding results. The proposed method addresses the issue that the parameters measurement of gas-liquid two-phase flow is significantly influenced by the flow regimes, and achieves accurate flow rate measurement under the diverse flow regimes. Experimental validation confirms the method's effectiveness and superior performance compared to conventional approaches. |
| 分类号 | 二类/Q1 |
| WOS关键词 | PREDICTION |
| 资助项目 | National Natural Science Foundation of China[U21B2011] ; Postdoctoral Research Funding of Hangzhou International Innovation Institute of Beihang University[2025BKZ014] ; Research Funding of Hangzhou International Innovation Institute of Beihang University[2024KQ150] ; Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF)[GZB20240935] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001522959400019 |
| 资助机构 | National Natural Science Foundation of China ; Postdoctoral Research Funding of Hangzhou International Innovation Institute of Beihang University ; Research Funding of Hangzhou International Innovation Institute of Beihang University ; Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) |
| 其他责任者 | Sun, Jiangtao，Xu, Lijun |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/102292]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 3.Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China; 4.Beihang Univ, Hangzhou Int Innovat Inst, Hangzhou 311115, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Suo, Peng,Sun, Jiangtao,Shi SY,et al. Mass Flow Rate Measurement of Gas-Liquid Two-Phase Flow Using Multi-Sensor Data Fusion and Soft Computing Model[J]. IEEE SENSORS JOURNAL,2025,25(13):25314-25323. |
| APA | Suo, Peng.,Sun, Jiangtao.,史仕荧.,Lu, Fanghao.,Shen, Mengxian.,...&Xu, Lijun.(2025).Mass Flow Rate Measurement of Gas-Liquid Two-Phase Flow Using Multi-Sensor Data Fusion and Soft Computing Model.IEEE SENSORS JOURNAL,25(13),25314-25323. |
| MLA | Suo, Peng,et al."Mass Flow Rate Measurement of Gas-Liquid Two-Phase Flow Using Multi-Sensor Data Fusion and Soft Computing Model".IEEE SENSORS JOURNAL 25.13(2025):25314-25323. |
入库方式： OAI收割
来源：力学研究所
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