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Application of local optical flow methods to high-velocity free-surface flows: validation and application to stepped chutes

机译:局部光流方法在高速自由表面流中的应用:阶梯槽的验证和应用

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摘要

The entrainment of air on a high-velocity spillway leads to a rapid bulking in flow depth and developments of complex flow patterns downstream of the inception point of aeration. This study examines the feasibility of two local optical flow techniques – the Lucas-Kanade method and the Farneback method – applied to high-velocity air-water skimming flows above a stepped chute. Such methods are not widely known to the multiphase flow community. Experimental studies were undertaken in a relatively large-size stepped spillway model. Validation test cases were performed using a synchronised ultra-high-speed camera and phase-detection probe setup. The optical flow technique detected changes in brightness due to reflectance difference associated with passages of air-water interfaces. The standard deviation of luminance correlates with void fraction and bubble count rate, and may be used as a predictor for uncertainties in optical flow estimation. The streamwise optical flow properties were in close agreement with those determined by the phase-detection probe next to the sidewall, with increasing differences for void fraction greater than 50% (C > 0.5). In the sidewall region, however the bubble count rate and interfacial velocity distributions were underestimated compared to the channel centreline\u27s interfacial properties. The tests demonstrated that the optical flow methods can provide useful qualitative and quantitative information on complex air-water flow patterns.
机译:高速溢洪道上夹带的空气导致流量深度迅速膨胀,并在曝气起始点下游形成复杂的流量模式。这项研究研究了将两种局部光流技术(Lucas-Kanade方法和Farneback方法)应用于阶梯状斜槽上方的高速空气-水撇除流量的可行性。这样的方法对于多相流界不是很广为人知。在相对较大的阶梯式溢洪道模型中进行了实验研究。验证测试用例是使用同步超高速相机和相位检测探头设置进行的。光流技术检测到由于与空气-水界面的通道相关的反射率差异而导致的亮度变化。亮度的标准偏差与空隙率和气泡计数率相关,并且可以用作光流估计不确定性的预测指标。沿流方向的光流特性与侧壁附近的相检测探针所确定的特性非常一致,空隙率的差异增大,大于50%(C> 0.5)。然而,在侧壁区域,与通道中心线的界面特性相比,气泡计数率和界面速度分布被低估了。测试表明,光流方法可以提供有关复杂的空气-水流模式的有用的定性和定量信息。

著录项

  • 作者

    Zhang, Gangfu; Chanson, Hubert;

  • 作者单位
  • 年度 2018
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 入库时间 2022-08-31 14:44:04

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