| The phenomenon of the two-phase flow is widly found in nature and modern industrial production process, and it is closely related to the human life and production. The measurement of the flow parameters is of great significance for industrialproducing process and the optimization of process. Because of complex interfacial effect and relative motion between the phases, it is difficult to identify accurately the two-phase flow patterns. In particular the mechanism of the transition from flow patterns to dynamics has not yet been very clear. And the lack of theory and technology, the measurement of two phase flow parameters failed to get very good solved, especially the complex fluid flow measurement and flow patterns' identification problem.Image processing technology is a new measurement technology, in recent years the image processing was applicated to the industrial measurement more and more by people. Image processing technology was also be taked a wide range of applications in the two phase flow system.Therefore it's an effective approach to solve the measurement of complex two phase flow parameters by the fusion of image processing and modern information processing method and theory.the nonlinear dynamic characteristics of two phase flow patterns were studied by using multiscale nonlinear analyzing method and image processing.The flowrate of two phase flow was measured by soft measurement method based on characteristics extracting and information fusion.And the innovative research fruits are shown as below:In so many image edge division algorithms, the paper improved the contour method and applicated in the images of gas-liquid two-phase flow patterns. To get effective extraction from the main objective of the gas-liquid two-phase flow images, in this paper, the2-Dimensional Gaussian kernel gradient, the multi-scale image decomposition techniques, the opening and closing operations, and the contour theory are combined together to put forward a way of the expression of the image characteristics. The new method can be described as follows:first, the decomposition of the empirical mode is based on the gradient mode enhancing image edge to get the high-frequency information sub-images and superpose them; then, the application of the opening and closing operation is to reduce the details'noise and repair the edge; finally extracting and expressing the features are by using the contour theory. By comparison, this way of expression can not only get the edge features of the image very well, but also retain some details, which can not be reserved by some classical algorithm. This method applied here can provide a new idea for extracting features from gas-liquid two-phase flow images.Each frame of the video signal is divided into smaller areas by a new method for extracting time series.The gray similar values and the maximum gray scale difference of each two adjacent frame are calculated,then formed the time series.The largest Lyapunov exponents of time series are respectively extracted,and the largest Lyapunov exponent matrix is composed.The videos of gas-liquid two-flow patterns are divided into different chaotic characteristic areas by the characteristics of Lyapunov exponent.Then they are respectively analyzed from overall and details by zero and one distribution map and3D map. The flowing mechanism of gas-liquid two-phase flow is analyzed,combined the fractal box dimension and Shannon entropy of the largest Lyapunov exponent matrix.The results show that the method of the gray similar values series of small areas combined extracting the largest Lyapunov exponent can distinguish the flowing characteristics of different flow patterns;the background and changed phase interface of gas-liquid two-phased flow video have chaotic charateristics of different intensity,which is an effective method for analyzing the gas-liquid two-phase flow signals.Empirical Mode Decomposition (EMD) method, HURST exponents and Recurrence Plot (RP) were combined together to analyze the gas-liquid two-phase flow from the whole to the details. At the same time the various signals are checked in the chaotic recursion chart by which the two typical characteristics (diagonal average length's and Shannon entropy) are obtained. The results show that the change regularity of the characteristic of dual-fractal is more obvious with the increase of gas surface measured velocity. In the high frequency section and low frequency section, all of the three flow patterns have simple fractal characteristic. But in the middle frequency section, the flow patterns appear to be dual fractal characteristic. The flow mechanism of the three flow patterns is analyzed partly and wholly.The Movement Law of bubble, gas slug and the whole bubble group present the change of two characteristics that are diagonal average length's and recursive Shannon entropy's. After the decomposition by the EMD method the slug flow and the mist flow in the high frequency section have obvious peaks.Anyway, it is an effective way to understand and characterize the dynamic characteristics of two-phase flow patterns that the multi-scale non-linear analysis method is based on image gray-scale fluctuation signals.The Stochastic subspsace parameter identification(SSI) which was used in the analysis of the construction of the bridge will be applized in the analysis of the grey fluctuation signals of gas-liquid two-phase flow patterns'images. And the stability graph which can determine the order of the system also be used in the analysis of the fluctuation signals of gas-liquid two-phase flow patterns'images.Complex time series can be quantitative characterized combining straightness eigenvalue.The feature vector of the pressure fluctuation sequence of three typical gas-liquid two-phase flow were extracted by application of random sub-space. It can be distinguished by amplitude and phase angle, of which the phase angle feature is the most reliable. The phase angle of mist flow is between1.35to2.07. The phase angle of bubbly flow is between4.01to4.36. The phase angle of slug flow is between-0.52to0. Changes of experimental conditions will not bring any effects. Determined entirely by flow pattern internal modal structure, the flow pattern signal can be accurately distinguished.
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