基于三流体模型物理引导神经网络的扰动波速预测  

Physical-guided neural network based on three-fluid model for disturbance wave velocity prediction

作　　者：李金霞 茹浩然 刘文凯 孙宏军 丁红兵 LI Jinxia;RU Haoran;LIU Wenkai;SUN Hongjun;DING Hongbing(College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]中国民航大学电子信息与自动化学院,天津300300 [2]天津大学电气自动化与信息工程学院,天津300072

出　　处：《化工进展》2025年第4期1815-1824,共10页Chemical Industry and Engineering Progress

基　　金：国家自然科学基金(52276159,62373270);中央高校基本业务费项目(3122025062);天津市自然科学基金(23JCQNJC00060);天津市教委科研计划(2022KJ065)。

摘　　要：扰动波广泛存在于蒸发器、天然气等环雾状两相流工业环境中,其中扰动波速是气液界面动量传递、传热和摩擦压降的重要参数。为提高扰动波大范围预测精度,本文以气相-液滴-液膜三流体模型为基础,考虑夹带液滴影响对气芯参数和液膜参数进行修正,提出了基于物理引导神经网络(PGNN)的扰动波速预测方法。利用环雾状流实验装置进行不同载气工况和液相流量实验,利用双电导环液膜传感器获得扰动波速,利用液膜提取装置测量液滴夹带率,并以气相韦伯数和液相雷诺数为参数建立了夹带率关联式。对提出的物理引导神经网络进行了超参数优化,利用不同工况(口径、压力、介质物性、流动方向)下的288组公开扰动波数据进行预测,76.74%的数据点在±5.0%误差带以内,94.10%的数据点在±15.0%误差带以内,相较于其他扰动波关联式和机器学习模型,本文提出的PGNN模型预测精度和可拓展性均大大提升。Disturbance waves widely exist in evaporators,natural gas and other industrial environments in annular mist flow pattern,among which the disturbance wave velocity is an important parameter for gas-liquid momentum transfer,heat-transfer and friction pressure drop prediction.To improve the predicted accuracy,the physical-guided neural network(PGNN)was proposed based on the three-fluid model,where the gas core mixture parameters and liquid film parameter were revised by considering the effect of droplet entrained in the gas core.For the experiments,flow tests on various carrier gas parameter and liquid flow rate were conducted,the wave velocities were obtained by using dual conductivity ring liquid film sensor,and the droplet entrainment was measured by using the developed liquid film extraction and metering device.The entrainment correlation was developed with the parameters of gas Weber number and liquid Reynolds number.The hyperparameters of the proposed physical-guided neural network was optimized,and the database of 288 set covering various two-phase flow conditions(pipe diameter,operational pressure,physical properties of the medium,flow direction)was used for the prediction.The results indicated that 76.74%data were within±5.0%error bands and 94.10%data were within±15.0%error bands.The predicted accuracy and scalability could be largely enhanced for the proposed PGNN method.

关 键 词：预测 气液两相流 神经网络 扰动波速 夹带率 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]