Two-phase flow simulation for distinguishing deformable particles with a LiMCA system

Guo, Xiaokang; Liu, Runcong; Wang, Xiaodong; Zhang, Zheyan; Isac, Mihaiela; Guthrie, R. I. L.

doi:10.1016/j.apm.2020.06.015

Cited by 10 publications

(3 citation statements)

References 40 publications

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“…This is due to the low surface tension of the bubble, which is subjected to the extensional flow and undergoes ellipsoidal deformation. This has a relatively low perturbation to the electric field, leading to a decrease in resistance [35]. In addition, the bubbles are susceptible to shape changes.…”

Section: Resultsmentioning

confidence: 99%

“…Besides, it is also difficult to obtain ideal waveforms after signal denoising treatment in real industrial environments. Related numerical simulation studies have been carried out by our team to elaborate and compare the transient motion of hard particles and various deformable particles with different capillary numbers, Reynolds numbers and confinement ratios [35]. For deformable and non-deformable particles, there are differences in the transient motion processes between them.…”

Section: Pulse Waveform Characterizationmentioning

confidence: 99%

“…Li et al suggested by using numerical simulations the differences in the motion of particles of different densities and sizes passing through a small aperture [31][32][33]. Our team [34,35] carried out numerical studies on the particle properties and the transient deformation of deformable particles such as bubbles. The findings of the above studies have guided the recognition of particle pulses in reality.…”

Section: Introductionmentioning

confidence: 99%

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A signal analysis and identification scheme for an online multiphase micron-sized particle analyzer system

Wang¹,

Cao²,

Wang³

et al. 2021

Meas. Sci. Technol.

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Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

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Section: Resultsmentioning

confidence: 99%

Section: Pulse Waveform Characterizationmentioning

confidence: 99%