Nonlinear characteristics analyses of particle motion for predicting flow regimes

Kang, Panxing; Lu, Yifeng; Yang, Lei; Liu, Libin; Hu, Xin‐Sheng; Luo, Xiao; Chen, Hongbo; Zhou, Yefeng; Zhang, Rui

doi:10.1016/j.partic.2020.03.008

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2022

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“…exhibits different limitations. For example, acoustic emissions are significantly affected by external noise [7,8]. X-ray and gamma-ray techniques are radioactive imposing health risks [9,10].…”

Section: Introductionmentioning

confidence: 99%

A gas–solid flow pattern identification algorithm based on cross-rod electrostatic sensor array

Wang¹,

Cheng²,

Li³

2022

Meas. Sci. Technol.

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The accurate identification of gas-solid two-phase flow patterns is an important but challenging subject for pneumatic conveying. In this study, the sensitivity deficiencies of a single electrode were analysed via finite element analysis (FEA) and a more sensitive cross-rod electrostatic sensor array structure was designed to measure the flow pattern signals. The experiment used Geldart D particles to verify the feasibility of the designed sensor array. Three types of feature vectors were extracted: the mean value, variance, and energy ratio. To identify the flow pattern accurately, the sine-cosine algorithm (SCA) is exploited to optimise the smoothing factor critical for a probabilistic neural network (PNN), namely SCA-PNN. The identification results show that the identification accuracy of the proposed algorithm outperforms the traditional PNN, the back propagation neural network (BPNN) and the support vector machine (SVM).

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