CAREER: Scalar Transport in High Reynolds Number Boundary Layer with Heterogeneous Roughness and Source Flux: Modeling Marine Aerosol in Coastal Regions

职业：具有异质粗糙度和源通量的高雷诺数边界层中的标量传输：模拟沿海地区的海洋气溶胶

基本信息

批准号：
2046160
负责人：
Giacomo Valerio Iungo
金额：
$ 50.25万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046160&HistoricalAwards=false
关键词：
CAREER Scalar Transport Reynolds Number

项目摘要

The goals of this research is to better understand transport by turbulent flow over rough surfaces since this understanding is important for a broad range of scientific and engineering applications, such as pollutant transport, urban flows, pollen dispersion, air quality, propagation of electromagnetic waves for communication. This project will focus on the transport of marine aerosol in coastal regions, accounting for shore roughness and aerosol generation from waves. Transport by turbulent diffusion is common in nature, yet current understanding is based on simplistic flow cases rather than real applications. This project will drive future generations of models for turbulent diffusion processes through a physics-based approach. The integrated education plan is at the interface of STEM, boundary-layer flows, turbulence, and will have a broad impact by: 1) educating students about the role of atmospheric turbulence for environmental flows; 2) involving teams of high school and undergraduate students in research-related design projects; 3) promoting awareness about the role of boundary-layer flows for the environment through the LiDAR summer camp and exhibits for rotation in community libraries serving disadvantaged youth. This work will involve traditionally under-represented students and reflect the diverse demographics of the Dallas-Fort Worth metroplex area.This project will encompass three interrelated tasks: i) A LiDAR field campaign at the coast of Galveston Bay in Texas to perform simultaneous and co-located wind and marine aerosol measurements in the marine atmospheric boundary layer; ii) Application of machine learning models to characterize, classify and predict various turbulent processes for marine-aerosol transport; iii) Development of fully tensorial eddy-diffusivity models for scalar transport in absence of equilibrium condition in the surface layer. The goals of the proposed project are: 1) Characterize and model scalar concentration over height and streamwise direction as a function of surface roughness variability and local source flux; 2) Investigate the role of large energy-containing turbulent structures in the re-organization of aerosol concentration within the surface layer in absence of equilibrium condition; 3) Develop eddy-diffusivity models for scalar transport as a function of statistics of heterogeneous surface aerosol flux and roughness.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.

这项研究的目标是更好地了解粗糙表面上湍流的传输，因为这种理解对于广泛的科学和工程应用非常重要，例如污染物传输、城市流动、花粉扩散、空气质量、电磁波传播沟通。该项目将重点关注沿海地区海洋气溶胶的输送，考虑海岸粗糙度和波浪产生的气溶胶。湍流扩散传输在自然界中很常见，但目前的理解是基于简单的流动案例而不是实际应用。该项目将通过基于物理的方法推动下一代湍流扩散过程模型。该综合教育计划涉及 STEM、边界层流、湍流，并将通过以下方式产生广泛影响：1) 教育学生大气湍流对环境流的作用； 2）让高中生和本科生团队参与研究相关的设计项目； 3) 通过激光雷达夏令营和为弱势青少年服务的社区图书馆轮换展品，提高人们对边界层流对环境的作用的认识。这项工作将涉及传统上代表性不足的学生，并反映达拉斯-沃斯堡都会区的多样化人口统计数据。该项目将包含三个相互关联的任务：i）在德克萨斯州加尔维斯顿湾海岸进行激光雷达现场活动，以同时和联合进行- 海洋大气边界层中的风和海洋气溶胶测量； ii) 应用机器学习模型来表征、分类和预测海洋气溶胶传输的各种湍流过程； iii) 在表面层不存在平衡条件的情况下，开发用于标量输运的全张量涡流扩散率模型。拟议项目的目标是： 1) 对高度和流向方向上的标量浓度进行表征和建模，作为表面粗糙度变化和局部源通量的函数； 2）研究在不存在平衡条件的情况下，大的含能湍流结构在表层内气溶胶浓度重组中的作用； 3) 开发标量传输的涡流扩散率模型，作为异质表面气溶胶通量和粗糙度统计的函数。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind LiDAR measurements

通过风激光雷达测量识别与近中性大气表层中的附壁涡流和超大规模运动相关的能量贡献

DOI：
10.1017/jfm.2022.1080
发表时间：
2023-01
期刊：
Journal of Fluid Mechanics
影响因子：
3.7
作者：
Puccioni, Matteo;Calaf, Marc;Pardyjak, Eric R.;Hoch, Sebastian;Morrison, Travis J.;Perelet, Alexei;Iungo, Giacomo Valerio
通讯作者：
Iungo, Giacomo Valerio

Grand-scale Atmospheric Imaging Apparatus (GAIA) and Wind LiDAR Multi-scale Measurements in the Atmospheric Surface Layer

大尺度大气成像仪（GAIA）和测风激光雷达在大气表层的多尺度测量

DOI：
10.1175/bams-d-23-0066.1
发表时间：
2023-11-20
期刊：
Bulletin of the American Meteorological Society
影响因子：
8
作者：
G. Iungo;Michele Guala;Jiarong Hong;Nathaniel Bristow;M. Puccioni;Peter Hartford;Roozbeh Ehsani
通讯作者：
Roozbeh Ehsani

Data-driven wind turbine wake modeling via probabilistic machine learning

通过概率机器学习进行数据驱动的风力涡轮机尾流建模

DOI：
10.1007/s00521-021-06799-6
发表时间：
2022-04
期刊：
Neural Computing and Applications
影响因子：
6
作者：
Ashwin Renganathan, S.;Maulik, Romit;Letizia, Stefano;Iungo, Giacomo Valerio
通讯作者：
Iungo, Giacomo Valerio

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

机器学习识别陆上风力涡轮机产生的尾流的平均速度和湍流强度的变化：风力激光雷达测量的聚类分析