Flow regime identification in gas-solid two-phase fluidization via acoustic emission technique
Flow regime identification in gas-solid two-phase fluidization via acoustic emission technique
复制标题
声发射技术识别气固两相流化流态
DOI：
10.1016/j.cej.2017.11.050
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发表时间：
2018
影响因子：
15.1
通讯作者：
Chen Hongbo
中科院分区：
文献类型：
--
作者：
Zhou Yefeng;Yang Lei;Lu Yujian;Hu Xiayi;Luo Xiao;Chen Hongbo
为了研究气固两相流化中的流型转变过程，采用了一种非侵入式、实时、环保的声发射技术和一种辅助的压力脉动方法。通过标准差分析得到床层中颗粒的活度和碰撞强度。在此基础上，发现声信号测量能更有效地反映主要流型之间的过渡速度，包括从鼓泡到湍流流化，从湍流到快速流化，从快速流化到密相气力输送。同时，通过Hurst分析和小波分析，可以获得流型转换过程中声波信号的多尺度分辨率。具体而言，微观尺度信号反映颗粒间碰撞和颗粒与壁面碰撞，介观尺度信号反映颗粒团簇与气相的相互作用和行为，宏观尺度信号反映平均流动行为。通过对实验结果的比较，由声信号测量得到的转捩速度的实验值更接近于相应的经验值。换句话说，标准偏差和多尺度分析与声学信号测量的集成可以有效地实时识别流型转变。
To explore flow regime transition processes in gas-solid two-phase fluidization, a non-intrusive, real-time and environment-friendly acoustic emission technique and an auxiliary pressure fluctuation method were applied in this work. Particle activity and collision intensity in the bed are obtained through standard deviation analysis. On that basis, it is found that the acoustic signal measurements can more effectively reflect the transition velocities among major flow regimes, includinguc(from bubbling to turbulent fluidization),uk(from turbulent to fast fluidization), anduFD(from fast fluidization to dense phase pneumatic conveying). Meanwhile, the multi-scale resolution of acoustic signals during regime transitions can be obtained through Hurst and wavelet analyses. To be specific, micro-scale signals reflect the inter-particle collision and the particle-wall collision, meso-scale signals indicate the interactions and behaviors of particle clusters and gas phase, and macro-scale signals represent the average flow behavior. According to the comparison of experimental results, the experimental values of transition velocities obtained from acoustic signal measurement are closer to corresponding empirical values. In other words, the integration of standard deviation and multi-scale analyses with acoustic signal measurement can effectively identify flow regime transitions in real-time.