Measurement of Flow Velocity Using Electrical Resistance Tomography and Cross-Correlation Technique

One of the major tasks in multiple-phase flow detection is flow velocity estimation. According to the cross-correlation (CC) measurement principle, the electrical resistance tomography (ERT) technique can estimate the flow velocity owing to its fast response, radiationless and non-invasiveness. However, the estimated values remain inaccurate in most cases due to unreasonable assumptions, natural ERT limitations, and similarity norm. In this paper, we propose a novel CC method to estimate the flow velocity based on ERT images. First, all the pixel coordinates of detected objects in any image are partitioned into a set according to the dispersed-phase fraction. Then, a new similarity norm is calculated between two comparing ERT image series. Finally, the length of these series is adaptively determined. The proposed method can greatly overcome the limitations of the existing CC methods and calculate the flow velocity more effectively and more robustly. According to the actual demands from dredging engineering, experiments were carried out under typical flow velocities, and results validated our proposed method. The novel CC method offers an effective way to apply the ERT technique in flow velocity estimation.

Automated summary

In this paper, a novel CC method based on ERT images is proposed for flow velocity estimation, which overcomes limitations of existing methods by calculating similarity norm and adaptively determining series length. Experiments validate the effectiveness of the proposed method in dredging engineering applications.