Evaluation of live Cryo-ECT system for liquid nitrogen–vapor nitrogen flow

Abstract

A cryogenic visible calibration and image evaluation facility (VCCIEF) was constructed to assess the effectiveness of electrical capacitance tomography systems in cryogenic conditions, known as Cryo-ECT. This facility was utilized to conduct dynamic, real-time imaging trials with liquid nitrogen (LN2). The actual flow patterns were captured using a camera and contrasted with the imaging outcomes. The capacitance data collected from these experiments were subsequently processed using three distinct methods: linear back projection, Landweber iteration, a fully connected deep neural network, and a convolutional neural network. This allowed for a comparative analysis of the performance of these algorithms in practical scenarios. The findings from the LN2 experiments demonstrated that the Cryo-ECT system, when integrated with the VCCIEF, was capable of successfully executing calibration, generating flow patterns, and performing imaging tasks. The system provided observable, clear, and precise phase distributions of the liquid nitrogen–vaporous nitrogenflow within the pipeline.