4.7 Article

Cross-Correlation Sensitivity-Based Electrostatic Direct Velocity Tomography

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3001412

关键词

Electrodes; Tomography; Atmospheric measurements; Particle measurements; Sensitivity; Velocity measurement; Electrostatic measurements; Cross-correlation (CC) sensitivity; electrostatic tomography (EST); gas-solid flow; velocity distribution; velocity estimation

资金

  1. National Natural Science Foundation of China [61673291, 61627803, 61871366]

向作者/读者索取更多资源

Electrostatic tomography (EST) has been widely used in gas-solid two-phase flows to measure particle velocity in many industrial processes. However, EST is a passive measurement technique, and this results in rare independent measurements compared with other tomographic modalities. In this article, an electrostatic direct velocity tomography (EDVT) method is proposed to increase the number of independent measurements and to improve the accuracy of velocity estimation. For a two-plane electrostatic sensor, the cross-correlation (CC) velocities between the electrodes from the two planes at different circumferential positions can represent the particle velocities in different regions. First, the CC velocities between the electrodes on different planes are calculated and used as new measurements. In this way, the number of independent measurements of a 16-electrode EST sensor can be easily increased from 16 to 120. Second, by analyzing the relationship between the CC velocities and the particle charge and particle velocity distribution, a CC sensitivity matrix is established. Finally, a tomographic model to represent the velocity distribution of charged particles can be established based on the new measurements and the CC sensitivity matrix. Simulation and experimental results show that the velocity distribution reconstructed by EDVT can represent the particle velocity distribution in the pipeline and is promising to provide a method to study the mechanism of gas-solid two-phase flows.

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