AGS-PRF: The Building Blocks of Shear-Driven Atmospheric Turbulence

AGS-PRF：剪切驱动大气湍流的组成部分

• 批准号: 2031312
• 负责人: Michael Heisel
• 金额: $ 19万
• 依托单位: Heisel, Michael
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031312&HistoricalAwards=false
• 关键词: AGS; PRF; Building; Blocks; Shear

Irregular wind velocities and swirling motions contribute to an erratic phenomenon known as turbulence. Considered one of the most confounding problems in physics, turbulence remains a research frontier in many fields despite centuries of study. Yet society exists within the turbulence of atmospheric winds. For instance, the interaction of these winds with the earth surface affects local, short-term weather and global, long-term climate, determines the dispersion of pollutants and other chemicals in the atmosphere, alters the transport of organic matter such as dust and seeds, and influences the water security of ecosystems stressed by evapotranspiration. It suffices to state that turbulence is crucial to life on Earth as we know it. Traditional atmospheric models account for the erratic behavior of turbulence in terms of statistics such as averages and standard deviations, but these models have no connection to the instantaneous features – such as persistent swirling vortices – or the dynamics that lead to the statistics. The present project uses recent visualizations of turbulent flows to identify a representative turbulent feature or “building block” (a sort of DNA of eddies) to represent turbulence as a series of these building blocks. The research will address how turbulence phenomenology drives the mechanisms of complex processes such as pollutant transport. The building block framework will also inform modeling approaches that are implemented in large-scale simulations pertaining to weather and climate.Recent studies revealed that shear-driven atmospheric turbulence is predominately self-organized into relatively uniform flow regions separated by smaller-scale layers of concentrated shear and vorticity. The research will build on these studies to investigate a combined deterministic and stochastic framework for representing turbulence. This framework will bridge the observed self-organized structures, time-averaged and scale-dependent statistics, and similarity relations in atmospheric turbulence. While the organization of the flow into these two structural types, i.e. the building blocks, is deterministic, the size and intensity of the structures will be described stochastically. The project includes simulating a stably stratified atmospheric boundary layer to explore the effects of buoyancy on the self-organized structures. The research will lead to an improved phenomenological understanding of empirical similarity relations, specifically how modifications to the instantaneous structures due to buoyancy lead to quantitative changes in important time-averaged statistics such as the mean velocity profile and turbulent transport of momentum, energy, and mass.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

不规则的风速和旋转运动导致了一种称为湍流的不稳定现象。湍流被认为是物理学中最令人困惑的问题之一，尽管经过了几个世纪的研究，但它仍然是许多领域的研究前沿。然而，社会存在于大气风的动荡之中。例如，这些风与地球表面的相互作用影响当地的短期天气和全球的长期气候，决定污染物和其他化学品在大气中的扩散，改变灰尘和种子等有机物质的迁移，并影响因蒸发蒸腾而受到压力的生态系统的水安全。湍流对地球上的生命至关重要，这一点足以说明这一点。传统的大气模型用平均值和标准差等统计数据来解释湍流的不稳定行为，但这些模型与瞬时特征（如持续旋转的涡旋）或导致统计数据的动力学没有联系。本项目使用最近的湍流可视化来识别代表性的湍流特征或“构建块”（一种漩涡DNA），以将湍流表示为一系列这些构建块。该研究将解决湍流现象如何驱动污染物传输等复杂过程的机制。构建块框架还将通知建模方法，在大规模模拟有关天气和气候。最近的研究表明，剪切驱动的大气湍流主要是自组织成相对均匀的流动区域，由较小尺度的集中剪切和涡度层分隔。这项研究将建立在这些研究的基础上，调查一个综合的确定性和随机性框架来表示湍流。这个框架将连接所观察到的自组织结构，时间平均和尺度相关的统计，以及大气湍流中的相似关系。虽然将流组织成这两种结构类型（即构建块）是确定性的，但将随机描述结构的大小和强度。该项目包括模拟稳定分层的大气边界层，以探索浮力对自组织结构的影响。该研究将导致对经验相似关系的现象学理解的提高，特别是由于浮力对瞬时结构的修改如何导致重要的时间平均统计数据的定量变化，例如平均速度分布和动量，能量，该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Self-similar geometries within the inertial subrange of scales in boundary layer turbulence

• DOI: 10.1017/jfm.2022.409
• 发表时间: 2021-08
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: M. Heisel;C. D. de Silva;G. Katul;M. Chamecki

Turbulence Organization and Mean Profile Shapes in the Stably Stratified Boundary Layer: Zones of Uniform Momentum and Air Temperature

• DOI: 10.1007/s10546-022-00771-0
• 发表时间: 2022-07
• 期刊: Boundary-Layer Meteorology
• 影响因子: 4.3
• 作者: M. Heisel;P. Sullivan;G. Katul;M. Chamecki

Prograde vortices, internal shear layers and the Taylor microscale in high-Reynolds-number turbulent boundary layers

高雷诺数湍流边界层中的顺行涡流、内部剪切层和泰勒微尺度

• DOI: 10.1017/jfm.2021.478
• 发表时间: 2021
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Heisel, Michael;de Silva, Charitha M.;Hutchins, Nicholas;Marusic, Ivan;Guala, Michele
• 通讯作者: Guala, Michele

Effect of finite Reynolds number on self-similar crossing statistics and fractal measurements in turbulence

有限雷诺数对湍流自相似交叉统计和分形测量的影响

• DOI: 10.1103/physrevfluids.7.014604
• 发表时间: 2022
• 期刊: Physical Review Fluids
• 影响因子: 2.7
• 作者: Heisel, Michael

Evidence of Mixed Scaling for Mean Profile Similarity in the Stable Atmospheric Surface Layer

稳定大气表层平均剖面相似性混合标度的证据

• DOI: 10.1175/jas-d-22-0260.1
• 发表时间: 2023
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Heisel, Michael;Chamecki, Marcelo
• 通讯作者: Chamecki, Marcelo