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被称为湍流的错误现象。尽管有几个世纪的研究，但被认为是物理学中最令人困惑的问题之一，在许多领域仍然是研究领域。然而，社会存在于大气风的动荡之内。例如，这些风与地面的相互作用会影响局部，短期天气和全球，长期气候，决定了污染物和其他化学物质在大气中的分散，改变了有机物（例如灰尘和种子）的运输，并影响蒸发造成的生态系统水安全性。声明湍流对我们所知的地球生命至关重要。传统的大气模型在统计数据（如平均值和标准出发）方面说明了湍流的不稳定行为，但是这些模型与瞬时特征（例如持续的旋转涡流）或导致统计数据的动力学无关。本项目使用湍流的最新可视化来识别代表性的湍流特征或“构建块”（一种涡流的DNA），以表示湍流为一系列这些构建块。该研究将解决湍流现象学如何驱动复杂过程（例如污染物运输）的机制。该基础框架还将为与天气和气候有关的大规模模拟中实施的建模方法提供信息。截止研究表明，剪切驱动的大气湍流主要是自组织为相对均匀的流动区域，这些流动区域由较小尺度的浓缩剪切和涡旋分隔。这项研究将基于这些研究，以研究代表湍流的合并确定性和随机框架。该框架将弥合观察到的自组织结构，时间平均和规模依赖性统计以及大气湍流中的相似关系。尽管确定流入这两种结构类型的组织，即确定构建块，但结构的大小和强度将随机描述。该项目包括模拟稳定的大气边界层，以探索布洛兰对自组织结构的影响。这项研究将导致对经验相似性关系的现象学的理解，特别是如何对浮力导致的瞬时结构进行修改导致重要时间平均统计数据的定量变化，例如平均速度概况，例如平均速度的概况以及动量，能量和能源的动荡运输，这些奖项通过评估NSF的合法宣传和众多奖项，这表明了NSF的众多成就，这是由众多奖励所产生的，这些奖项的始终是众多的支持。 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