Abstract
A real-time measurement on the radiochemical processes in nuclear industry is crucial in order to monitoring the process frequently. Recently, a wireless electrical resistance tomography (WERT) with circumferential electrode was developed for the real-time measurement. However, due to the limitation in the circumferential section, we propose a linear sensor-wireless electrical resistance tomography (LS-WERT). This system is detecting the particle deposition thickness in the longitudinal positions. A coupling simulation of smoothed particle hydrodynamics and discrete element model (SPH-DEM) method is used to observe the particle-liquid behavior under centrifugal field. The distribution of particle and liquid phase in this simulation is then measured by an electrical impedance tomography (EIT) simulation. SPH-DEM-EIT coupling includes the converting process from DEM to FEM. An Artificial Neural Network (ANN) is applied with input from the electrical measurement results of the coupling. ANN for LS-WERT gives result in three categories of air, liquid, and particle phase. We evaluate the real-time particle deposition thickness by comparing the LS-WERT result to the high-speed camera images. As a result, LS-WERT has an average accuracy of 2.27% under rotating speed below 220 rpm. In overall, LS-WERT gives a good tendency to the high-speed camera images and effective for the application on real-time measurement of high-speed centrifuge.
