计量学报2025，Vol.46Issue(11)：1591-1597,7.DOI:10.3969/j.issn.1000-1158.2025.11.06
基于SA-VAE-LSTM的气液两相流气含率及气相流速测量
Measurement of Gas Volume Fraction and Gas Velocity in Gas-liquid Two-phase Flow Based on SA-VAE-LSTM
摘要
Abstract
A self-attention variational autoencoder long short-term memory network(SA-VAE-LSTM)model is proposed for the measurement of gas volume fraction and gas velocity in gas-liquid two-phase flow.Firstly,the model utilizes a 16-electrode array conductivity sensor to acquire real-time flow signals.Secondly,a variational autoencoder(VAE)is employed to extract representative features from the multi-channel input signals,followed by a parallel self-attention mechanism to adaptively enhance key flow-related features.Finally,a long short-term memory(LSTM)network is used to capture the temporal dependencies of the extracted features,enabling accurate prediction of gas volume fraction and gas velocity.Experimental results demonstrate that the proposed SA-VAE-LSTM model achieves excellent performance in both prediction tasks,with coefficients of determination reaching 0.999 9 and mean absolute errors of 0.000 5 and 0.000 4,respectively.Compared with baseline models such as VAE-LSTM,the proposed approach exhibits superior feature representation and temporal modeling capabilities,leading to significantly improved predictive accuracy.关键词
流量计量/气液两相流/SA-VAE-LSTM模型/阵列电导传感器/气含率/流速/测量Key words
flow metrology/gas-liquid two-phase flow/SA-VAE-LSTM model/electrode array conductivity sensor/gas volume fraction/liquid velocity/measurement分类
通用工业技术引用本文复制引用
GU Tianwen,ZHANG Lifeng..基于SA-VAE-LSTM的气液两相流气含率及气相流速测量[J].计量学报,2025,46(11):1591-1597,7.基金项目
国家自然科学基金(61973115) （61973115）
