Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement
CHEN Qian
YU Baodi
QIN Yanwei
WANG Sunyang
SU Xiaohui
JIN Xin
MENG Fanyong
Abstract：Multiphase-flow measurement cannot effectively capture mesoscale dynamic structures owing to limitations of spatial and temporal resolutions of current measuring techniques.Dynamic X-ray computed tomography(CT),as a non-invasive multiphase-flow measurement technique,is promising for measuring the dynamic structures of multiphase flow.Focusing on the gas-liquid two-phase flow in multiphase flow,this paper addresses limited angle artifacts and excessive reconstruction time in mesoscale dynamic structures and proposes a U-Net-enhanced simultaneous iterative reconstruction technique(SIRT)reconstruction algorithm for bubble-structure measurements based on gas-liquid two-phase flow.Subsequently,based on the hardware design of a flowfield dynamic measurement system,which is a limited-angle dynamic X-ray CT system,a simulated gas-liquid two-phase flow dataset for training the deep-learning model is constructed from three-dimensional bubble structures obtained from hydrogel phantoms.The proposed method yields good results in the training and testing of the constructed dataset and significantly reduces the reconstruction time,thus providing a new technical approach for the high-spatiotemporal-resolution measurement of multiphase-flow mesoscale structures.
Keywords：deep learningdynamic CTmultiphase flow measurementlimited-anglereconstruction algorithm
Publication Date：2025-05-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 419-426 )