Wavelet analysis of dynamic behavior in fluidized beds
Wavelet analysis of dynamic behavior in fluidized beds
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DOI：
10.1016/s0009-2509(00)00313-4
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发表时间：
2001-02
影响因子：
4.7
通讯作者：
Jin Ren;Qiming Mao;Jinghai Li;Weigang Lin
中科院分区：
文献类型：
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作者：
Jin Ren;Qiming Mao;Jinghai Li;Weigang Lin
将小波分析应用于流化床的动态行为研究，证明了小波分析可以有效地将时间序列分解为具有不同结构的组分的不同尺度，并可以识别从浓相到稀相的过渡。通过对流化床各种动态信号的小波谱函数分析，表明信号可以被分解为三个分量尺度：微观尺度（粒度）、中观尺度（簇大小）和宏观尺度（单元大小）。采用主成分法对光探头测得的浓度信号进行相位分离。该方法选取小波谱函数的最大尺度参数50作为最优尺度参数。主成分方法可以显著减少计算时间，并保留我们之前出版物中描述的直接方法提供的好处（Ren & Li, in: l.s. Fan, t.m. Knowlton（主编），流化，卷九，工程基金会，纽约，1998,p. 629）。该方法还扩展到从流化床获得的二维数字图像中检测簇的边界。
Wavelet analysis has been used for studying dynamic behavior of fluidized beds, which proved effective in resolution of time series into different scales of components with distinct structure and in identification of transition from the dense phase to the dilute phase. By examining wavelet spectrum functions of various dynamic signals measured from fluidized beds, it is indicated that the signals can be decomposed into three scales of components: micro-scale (particle size), meso-scale (cluster size) and macro-scale (unit size). The principal component method was employed for phase separation from concentration signals measured by the optical probe. In this method, the maximum scale parameter s0of the wavelet spectrum function was chosen as the optimum scale parameter. The principal component method can reduce the computation time significantly and remain the benefit offered by the direct method described in our previous publication (Ren & Li, in: L. S. Fan, T. M. Knowlton (Eds.), Fluidization, Vol. IX, Engineering Foundation, New York, 1998, p. 629.). The method was also extended to detect the boundaries of clusters in 2-D digital images acquired from fluidized beds.