The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow...The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.展开更多
Axial cyclone separator has been widely applied in chemical production as an efficient gas-liquid separation device.In this study,a new axial cyclone separator with integrated swirler and exhaust pipe is designed to a...Axial cyclone separator has been widely applied in chemical production as an efficient gas-liquid separation device.In this study,a new axial cyclone separator with integrated swirler and exhaust pipe is designed to achieve the development goal of compact structure for advanced engine,and the distribution characteristics of swirling flow patterns as well as the variation in separation characteristics are investigated under slug flow pattern.Based on flow visualizations and fluctuation characteristics of pressure signals,three typical flow patterns,namely,slug flow,swirling intermittent flow,and swirling annular flow,in the horizontal swirling separation flow are characterized.It is investigated how the inlet conditions affect the separation characteristic parameters.The separation purity and extreme points of the air separation efficiency are independent of the inlet liquid flow rate.The separation pressure drop is quadratically related to the inlet air flow rate.Based on the drift-flux model and other methods,the prediction methods for the air separation efficiency and pressure drop are proposed,and the prediction accuracy is within±20%,which may provide instructions for the practical application of axial cyclone separator.展开更多
文摘The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.
基金supported by the National Natural Science Foundation of China(Grant Nos.51888103 and 52076175)the Fundamental Research Funds for the Central Universities。
文摘Axial cyclone separator has been widely applied in chemical production as an efficient gas-liquid separation device.In this study,a new axial cyclone separator with integrated swirler and exhaust pipe is designed to achieve the development goal of compact structure for advanced engine,and the distribution characteristics of swirling flow patterns as well as the variation in separation characteristics are investigated under slug flow pattern.Based on flow visualizations and fluctuation characteristics of pressure signals,three typical flow patterns,namely,slug flow,swirling intermittent flow,and swirling annular flow,in the horizontal swirling separation flow are characterized.It is investigated how the inlet conditions affect the separation characteristic parameters.The separation purity and extreme points of the air separation efficiency are independent of the inlet liquid flow rate.The separation pressure drop is quadratically related to the inlet air flow rate.Based on the drift-flux model and other methods,the prediction methods for the air separation efficiency and pressure drop are proposed,and the prediction accuracy is within±20%,which may provide instructions for the practical application of axial cyclone separator.
