立方rayleigh - bsamadard单元中的大尺度流动:长期湍流统计和宏观状态转变的马尔可夫性

P. Maity, P. Koltai, J. Schumacher
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引用次数: 3

摘要

在Rayleigh数Ra = 106时,采用直接数值模拟的方法研究了立方密闭对流池中湍流Rayleigh- bsamadard对流中的大尺度环流。对105次对流自由落体的单流轨迹进行了数值研究,以获得四种离散LSC状态的充分采样,这些状态可以总结为一个宏观状态,以及流动在短时间内采取的两种交叉构型。我们发现大尺度动力学很大程度上取决于流体的普朗特数Pr,其值分别为0.1、0.7和10。或者,我们运行3600个短期直接数值模拟的集合来研究离散LSC状态之间的转移概率。第二种方法也用于探索动力学的马尔可夫性质。我们的集合分析给出了从一个LSC状态到另一个LSC状态过渡过程的马尔可夫性的强有力的指示,即使数据仍然伴随着相当大的噪声。该方法基于转移概率矩阵的特征值谱,进一步基于持续时间的分布和两个连续微态持续时间的联合分布。本文是主题问题“物理流体动力学中的数学问题(第一部分)”的一部分。
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Large-scale flow in a cubic Rayleigh–Bénard cell: long-term turbulence statistics and Markovianity of macrostate transitions
We investigate the large-scale circulation (LSC) in a turbulent Rayleigh-Bénard convection flow in a cubic closed convection cell by means of direct numerical simulations at a Rayleigh number Ra = 106. The numerical studies are conducted for single flow trajectories up to 105 convective free-fall times to obtain a sufficient sampling of the four discrete LSC states, which can be summarized to one macrostate, and the two crossover configurations which are taken by the flow in between for short periods. We find that large-scale dynamics depends strongly on the Prandtl number Pr of the fluid which has values of 0.1, 0.7, and 10. Alternatively, we run an ensemble of 3600 short-term direct numerical simulations to study the transition probabilities between the discrete LSC states. This second approach is also used to probe the Markov property of the dynamics. Our ensemble analysis gave strong indication of Markovianity of the transition process from one LSC state to another, even though the data are still accompanied by considerable noise. It is based on the eigenvalue spectrum of the transition probability matrix, further on the distribution of persistence times and the joint distribution of two successive microstate persistence times. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 1)’.
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