论文标题
湍流雷利 - 纳德流动中固体颗粒的停留时间的随机模型
A stochastic model for the residence time of solid particles in turbulent Rayleigh-Bénard Flow
论文作者
论文摘要
PI室位于密歇根州技术大学，产生潮湿的湍流雷利 - 纳德流，以复制稳态云状况。我们从这种设置中汲取灵感，并使用直接数值模拟（DNS）考虑具有粒子，对流驱动的湍流。我们研究的目的是开发一个简单的随机模型，该模型可以准确地描述流动中颗粒的停留时间，这次是由颗粒的重力沉降之间的复杂竞争确定的，以及颗粒与流中湍流结构的相互作用。一个简单的概念图是随机模型的基础，即颗粒在顶部和底部边界之间重复旅行，这是由雷利 - 贝纳德（Rayleigh-Bénard）湍流中发生的对流单元驱动的，并且他们的住所时间是由完成这些旅行的一次旅行所需的时间来确定的，这是从一次旅行到另一个旅行的可能性，以及每次跌落到底部的旅行范围。尽管模型很简单，但它可以在流量中粒子停留时间的分布进行定量准确的预测。我们独立地改变了斯托克数字和沉降速度，以阐明重力和惯性在管理这些停留时间中发挥的独立作用。
The Pi Chamber, located at Michigan Technological University, generates moist turbulent Rayleigh-Bénard flow in order to replicate steady-state cloud conditions. We take inspiration from this setup and consider a particle-laden, convectively-driven turbulent flow using direct numerical simulation (DNS). The aim of our study is to develop a simple stochastic model that can accurately describe the residence times of the particles in the flow, this time being determined by the complex competition between the gravitational settling of the particles, and the interaction of the particles with the turbulent structures in the flow. A simple conceptual picture underlies the stochastic model, namely that the particles take repeated trips between the top and bottom boundaries, driven by the convective cells that occur in Rayleigh-Bénard turbulence, and that their residence times are determined by the time it takes to complete one of these trips, which varies from one trip to another, and the probability of falling out to the bottom boundary after each trip. Despite the simplicity of the model, it yields quantitatively accurate predictions of the distribution of the particle residence times in the flow. We independently vary the Stokes numbers and settling velocities in order to shed light on the independent roles that gravity and inertia play in governing these residence times.
