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Xinming Wang
Publications - 5
Citations - 18
Xinming Wang is an academic researcher. The author has contributed to research in topics: Draft tube & Vortex. The author has an hindex of 2, co-authored 4 publications receiving 17 citations.
Papers
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Further investigation on the pressure fluctuations caused by cavitation vortex rope in an elbow draft tube
TL;DR: In this article, the authors demonstrate the experimental results of the pressure fluctuations with irregular nature, which are induced by the vortex rope in an elbow draft tube having a longer inlet once, and analyze the fluctuating wall-pressures measured at both inlet cone and elbow sections of the draft tube.
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Analysis of Swirling Flow with Spiral Vortex Core in a Pipe
Xinming Wang,Michihiro Nishi +1 more
TL;DR: In this paper, a quasi-three dimensional analytical model is proposed to simulate swirling flow with spiral vortex core in a pipe, where the vortex core is expressed as a helical vortex filament, and the boundary condition of zero normal velocity at the pipe wall is specified by distributing image helicical vortex filaments on a cylindrical surface which is located outside the pipe.
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Pressure Fluctuations Having Random Nature Caused by Cavitated Spiral Vortex Core in Elbow Draft Tube.
TL;DR: In this article, the dominant frequencies derived from the cross spectrum of fluctuating pressure were classified and correlated with the behavior of the vortex core inside the tube, which was observed by using a high-speed video system.
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Time-Averaged Flow Pattern of Swirl Flow and Pressure Recovery in an Elbow Draft Tube.
TL;DR: In this paper, a series of time-averaged measurements in a conventional draft tube, which was installed in an air test rig, by using a five-hole probe, was carried out to investigate the surge phenomena in the elbow draft tubes.
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Hierarchical modeling of microstructural images for porosity prediction in metal additive manufacturing via two-point correlation function
TL;DR: In this article , a three-level hierarchical mixed-effects modeling approach is proposed to characterize the relationship between microstructural images and process parameters for porosity prediction and microstructure reconstruction.