计量学报2025，Vol.46Issue(11)：1574-1580,7.DOI:10.3969/j.issn.1000-1158.2025.11.04
基于CNN与虚高特性的长喉径文丘里管湿气模型
Lengthened Throat Venturi Wet Gas Metering Model Based on CNN Classification and Over-reading Characteristics
摘要
Abstract
Multiple sets of single-phase and wet gas tests were conducted using a long-throat Venturi meter on a wet gas metering standard device under pipeline pressures of 2.0～3.0 MPa.A convolutional neural network(CNN)algorithm was employed to analyze multidimensional time-series signals,establishing a high-precision neural network algorithm for identifying single-phase/multiphase flow states in the pipeline.Based on the characteristics of signals in long-throat Venturi meters for wet gas flow,iterative prediction algorithms for gas and liquid flow rates were developed using the concept of virtual height.Consequently,a wet gas flow model capable of real-time identification and precise classification and measurement of industrial wet gas flow was constructed.The model testing results showed that for single-phase gas,the model significantly reduced measurement deviations caused by flow pattern misjudgment(improving gas phase accuracy by up to 9% and correcting liquid phase misjudgment by 0.6 m³/h).Under wet gas conditions,the mean absolute percentage errors(MAPE)for gas and liquid flow rate predictions were 4.9%and 12.45%,respectively.关键词
湿气流量计量/多相流计量/长喉径文丘里管/卷积神经网络/虚高理论/流态判别Key words
wet gas flow measurement/multiphase flow metering/lengthened throat Venturi/convolutional neural networks/virtual height theory/flow pattern discrimination分类
通用工业技术引用本文复制引用
YU Peining,WEI Lai,LU Xing,LI Yi,ZOU Haixin,ZHANG Qiang..基于CNN与虚高特性的长喉径文丘里管湿气模型[J].计量学报,2025,46(11):1574-1580,7.基金项目
国家自然科学基金(61603207) （61603207）
