| This paper studies identification of two-phase flow patterns and represents a chaotic dynamic structure catastrophe detection method, also called dynamic exponent segmentation algorithm. Based on chaos and catastrophe theory, this method uses the state space reconstruction to calculate the dynamic structure difference of both the left and the right sides of the catastrophic points in different segments of same time series, consequently distinguishing inherent dynamic characteristics and indicating the changing tendency of the dynamic states of a system at random time.Typical chaotic series are selected to do numerical simulation experimentation for the algorithm,such as Henon,Lorenz,Rossler,Chen's. Using C-C method and FNN method to determine the optimal delay time and the embedding dimension. Parameters that will influence the result of dynamic exponent segmentation algorithm method is observed to find the proper length of original time series, the width of reference window, the threshold value, and to minimize the difference between the structure catastrophic point and the actual one. Then a noise-adding test is made to prove that the chaotic catastrophe detection has better anti-noise capability.Using chaotic catastrophe detection to analyze the conductance fluctuating signals of gas/liquid two-phase flow and oil/liquid two-phase flow in vertical upwards pipe, and the dynamic structure difference of every flow pattern is descending at the same time. The changing regularity of the two-phase is more clearly acknowledged , as well as the complicated dynamic characteristics and its dynamic system's movement, and the changing tendency of every flow's pattern at random time. Ultimately two-phase flow pattern is identified. Therefore chaotic dynamic structure catastrophe detection can be applied as a supplement tool for detecting two-phase flow pattern, and is stable and efficient to knowledge flow pattern's features of gas/liquid and oil/water two-phase flow.
|
PDF Full Text Request