Two-phase fluid flow pattern identification method based on time sequence and neural net pattern identification
A neural network and time series technology, applied in flow characteristics, measurement devices, instruments, etc., can solve the problems of low imaging accuracy, difficult measurement of small signals, poor real-time performance, etc., to improve accuracy, fast learning speed, pattern recognition and The effect of strong classification ability
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[0025] The specific implementation of the present invention will be further described below in conjunction with the real-time online flow pattern identification of oil-gas two-phase flow in horizontal pipes carried out on a large-scale multiphase flow experimental simulation device.
[0026] (1) Fix and change the liquid phase flow and gas phase flow respectively to obtain various flow patterns of different flow conditions, different gas and liquid flows: such as laminar flow, stirred flow, slug flow and annular flow. The differential pressure signal time series of the gas-liquid two-phase flow in the horizontal pipe is collected by a differential pressure sensor. The sampling point spacing is 10 times the pipe diameter, the sampling frequency is 200Hz, and the sampling time is 30 seconds.
[0027] (2) After the wavelet transform method is used to denoise the obtained time series signals, and through data processing, the probability density function curves of differential pres...
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