Research On Flow Pattern Identification Method Based On Pressure And Concentration Distribution Signal For Gas-solid Two-phase Flow

Flow pattern is an important parameter which influences the measurement of hee flow parameters of multi-phase flow. Comparing with dilute phase pneumatic conveyii, the flow pattern of dense phase is more complex and the variety of flow parameters is mo serious. It is particularly important to identify the flow pattern correctly. The pressure fl uation and phase concentration distribution signal are important flow parameters t(reflect the characteristics of flow pattern. At present, the identification of flow pattern mai y focuses on the cross section information, but how to identify the dynamic variety of axis flow patterns are not solved well.This dissertation proposed a new flow identification method which use the axial time series with radial cross section signals in view of the above problems, onsidering the experimental conditions, the characteristics of pressure signals from numeric simulation and phase concentration distribution signals from ECT system are studied to ide ify laminar flow and the slug flow, which are the typical flow pattern of dense phase pne natic conveying. The main works of the dissertation are as follows:(1) The axial pressure fluctuation time series from numerical simul on are analyzed to study the characteristics of the variety of dense phase pneumatic conveyi flow patterns such as the laminar, slug flow and so on.(2) According to time series characteristics theory, extracting w elet energy feature, partial slope and the front information of the slug flow as the character uc vector of the axial pressure fluctuation signals for dense phase pneumatic conveying, ar analyzing the change of characteristics along with Laminar flow and slug flow. Studying fl pattern identification method of the axial pressure fluctuation signal characteristics, roposing flow pattern identification method based on self-organizing competitive neural ne vork.(3) Based on the principle of electrical capacitance tomograph; analyzing characteristics of radial and axial distributed capacitance data which collected by CT system to simulate the dynamic flow pattern, researching on characteristics which reflect the characteristic of radial cross section and the axial flow type changing, extracting the Hur exponent of time series mean d of voltage value between plate, and Partial slope of 28 d voltage value as the feature vectors.(4) Studying the change of characteristics for phase concentration distribution data feature vector along with Laminar flow, slug flow. In the dense phase pneumatic conveying horizontal pipe, studying establish methods of model of the flow pattern identification based on the ECT data feature vectors, putting forward flow pattern identification method based on support vector machine.(5) The flow pattern identification software platform was designed based on the virtual instrument, In the laboratory environment, the system can accurately identify slug flow and laminar flow, checking the validation of identification algorithm.