青岛科技大学  English 
樊春玲
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教师拼音名称:fanchunling

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Bayesian neural networks for life modeling and prediction of dynamically tuned gyroscopes

关键字:Flow characteristics; gas-liquid two-phase flow; multi scale decomposition; order recurrence characteristics

摘要:Based on the conductance fluctuating signals of the gas-liquid two-phase flow measured in a vertical upward pipe, the multi-scale order recurrence quantification analysis method is used to research the dynamic characteristics of the gas-liquid two-phase typical flow patterns. We obtain each scale signal of the conductance fluctuating signals of the gas-liquid two-phase flow by ensemble empirical mode multi-scale decomposition, and then draw the order recurrence plot (ORP) of each scale signal. In order to test the effect of the ORP, we conduct quantitative analysis of the ORP through the averaged diagonal length. The results show that the ORP method can characterize the dynamic characteristics of the two-phase flow pattern. Compared with the recurrence plot (RP), the ORP clearly shows the entire texture of blocks, and it is more intuitive and effective than the linear texture of the RP visually. It is shown that multi-scale order recurrence analysis is an efficient method for understanding the characteristics of the gas-liquid two-phase flow patterns.

卷号:37

期号:6

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