算法复杂性在气固流化床动力学中的多尺度分析

提出了对从时间序列粗粒化得到的符号序列进行细化的方法,首次在不同尺度下,利用算法复杂性,对不同流型、静床高、平均粒径等操作条件下的压力脉动时间序列进行分析。研究结果表明,不同尺度下的算法复杂性不同的变化趋势,对应了不同尺度下气固两相运动的特征,能够从多角度反映流化床的运动特性,比单一尺度提供更多的信息,为流化床压力脉动时问序列非线性分析提供了新的思路。

ANALYSIS OF PRESSURE FLUCTUATION IN FLUIDIZED BED THROUGH ALGORITHM COMPLEXITY IN VARIOUS SCALES

Roughing method to obtain symbol series of time series is refined in this research. Pressure fluctuation time series under various operating conditions, such as different flow regimes, static bed heights and average particle diameters, are analyzed in various scales through algorithm complexity for the first time. The result indicates that different trends of algorithm complexity in different scales correspond to the dynamic characteristics in fluidized bed. Algorithm Complexity C ( n ) is an explicit indicator of different fluidization regimes and different scales are distinct expressions of different fluidized phases. The larger scales are sensitive to bubble phase, and the smaller scales are sensitive to milk phase. Therefore, it reflects the dynamics of fluidized beds in various dimensions and gives more information than that of single scale. It is a new method for nonlinear analysis of pressure fluctuation time series.