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On sampling bias in multiphase flows: Particle image velocimetry in bubbly flows

机译:关于多相流中的采样偏差:气泡流中的粒子图像测速

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Measuring the liquid velocity and turbulence parameters in multiphase flows is a challenging task. In general, measurements based on optical methods are hindered by the presence of the gas phase. In the present work, it is shown that this leads to a sampling bias. Here, particle image velocimetry (PIV) is used to measure the liquid velocity and turbulence in a bubble column for different gas volume flow rates. As a result, passing bubbles lead to a significant sampling bias, which is evaluated by the mean liquid velocity and Reynolds stress tensor components. To overcome the sampling bias a window averaging procedure that waits a time depending on the locally distributed velocity information (hold processor) is derived. The procedure is demonstrated for an analytical test function. The PIV results obtained with the hold processor are reasonable for all values. By using the new procedure, reliable liquid velocity measurements in bubbly flows, which are vitally needed for CFD validation and modeling, are possible. In addition, the findings are general and can be applied to other flow situations and measuring techniques. (C) 2016 Elsevier Ltd. All rights reserved.
机译:测量多相流中的液体速度和湍流参数是一项艰巨的任务。通常,气相的存在阻碍了基于光学方法的测量。在目前的工作中,这表明导致采样偏差。在这里,对于不同的气体体积流量,粒子图像测速(PIV)用于测量气泡塔中的液体速度和湍流。结果,通过的气泡导致明显的采样偏差,该偏差由平均液体速度和雷诺应力张量分量评估。为了克服采样偏差,导出了依赖于局部分布的速度信息的等待时间的窗口平均过程(保持处理器)。演示了该程序的分析测试功能。使用保持处理器获得的PIV结果对于所有值都是合理的。通过使用新程序,可以进行气泡流中可靠的液体速度测量,这对于CFD验证和建模至关重要。此外,这些发现是一般性的,可以应用于其他流量情况和测量技术。 (C)2016 Elsevier Ltd.保留所有权利。

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