Bubble Reconstruction Algorithm Based On Double Vision Imaging In Gas-liquid Two Phase Flow

The gas-liquid two-phase flow exists widely in process industry with characteristic multi-scale structure,and it's a typical complex system.The dynamic behavior of bubbles is an important meso-structure in gas-liquid system.The bubble-bubble coalescence and break-up has great effects on the mass & heat transfer of the reactor.Non-invasive measurement and description of such meso-structure is the key to fully understanding of such a complex system.The double vision imaging technique is quite promising in bubble study.However,the traditional CT reconstruction algorithm such as FBP(Filtered Back Projection)can not meet the requirement of bubble reconstruction with only two projections.CT reconstruction algorithm based on compressed sensing is widely used in fan-beam and cone-beam scan mode.It is hopeful to improve the accuracy by improving reconstruction algorithm based on CS.In this thesis,TV(Total Variation)minimum reconstruction method which based on CS as optimization model and SD-POCS algorithm was used to solve TV model.The following studies were carried on:(1)Projection data was generated by the simulation of measurement of gas-liquid two-phase flow system based on double x ray sources and flat panel detectors.(2)Bubble reconstruction based on D2 D was Modeled as systems of linear equations.An optimization model was designed based on TV minimum to solve the systems of linear equations.The improved SD-POCS is used to solve TV minimum and design program based on this algorithm to reconstruction the image of bubble.(3)The bubble recognition algorithm was put forward and was proved to meet the requirements of bubble detection.Simulation experiments proved that the optimization models and improved algorithm is effective for bubble image reconstruction,which formed the basis for the development of double vision imaging technique.