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:
  • 金额:
    $ 50.25万
  • 依托单位:
  • 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

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理解湍流在粗糙表面上的运输,因为这种理解对于广泛的科学和工程应用非常重要,例如污染物运输,城市流动,花粉分散,空气质量,用于通信的电磁波传播。该项目将重点研究沿海地区海洋气溶胶的输送,考虑海岸粗糙度和海浪产生的气溶胶。湍流扩散输运在自然界中很常见,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未来几代湍流扩散过程模型的发展。该综合教育计划处于STEM、边界层流动、湍流的界面,将通过以下方式产生广泛的影响:1)教育学生大气湍流对环境流动的作用;2)让高中生和本科生团队参与研究相关的设计项目;3)通过为弱势青少年服务的社区图书馆举办的激光雷达夏令营和轮转展览,提高人们对边界层流动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-沃斯堡大都会区多样化的人口统计数据。该项目将包括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野外活动,在海洋大气边界层进行同时和共同定位的风和海洋气溶胶测量;ii)应用机器学习模型来描述、分类和预测海洋气溶胶运输的各种湍流过程;iii)建立了在没有表层平衡条件下标量输运的全张量涡旋扩散模型。拟建项目的目标是:1)描述和模拟高度和流向上的标量浓度作为表面粗糙度变化和局部源通量的函数;2)在无平衡条件下,研究大型含能湍流结构对气溶胶浓度在表层重新组织中的作用;3)建立基于非均质表面气溶胶通量和粗糙度统计的标量输运涡旋扩散模型。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识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)
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
Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality
  • DOI:
    10.1002/we.2865
  • 发表时间:
    2023-10
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    M. Puccioni;C. Moss;G. Iungo
  • 通讯作者:
    M. Puccioni;C. Moss;G. Iungo
A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics
Data-driven wind turbine wake modeling via probabilistic machine learning
  • DOI:
    10.1007/s00521-021-06799-6
  • 发表时间:
    2022-01
  • 期刊:
  • 影响因子:
    6
  • 作者:
    S. Ashwin Renganathan;R. Maulik;S. Letizia;G. Iungo
  • 通讯作者:
    S. Ashwin Renganathan;R. Maulik;S. Letizia;G. Iungo
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
  • DOI:
    10.1017/jfm.2022.1080
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    M. Puccioni;M. Calaf;E. Pardyjak;S. Hoch;T. Morrison;A. Perelet;G. Iungo
  • 通讯作者:
    M. Puccioni;M. Calaf;E. Pardyjak;S. Hoch;T. Morrison;A. Perelet;G. Iungo
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Giacomo Valerio Iungo其他文献

Augmenting insights from wind turbine data through data-driven approaches
  • DOI:
    10.1016/j.apenergy.2024.124116
  • 发表时间:
    2024-12-15
  • 期刊:
  • 影响因子:
  • 作者:
    Coleman Moss;Romit Maulik;Giacomo Valerio Iungo
  • 通讯作者:
    Giacomo Valerio Iungo

Giacomo Valerio Iungo的其他文献

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{{ truncateString('Giacomo Valerio Iungo', 18)}}的其他基金

Genesis and Dynamics of very-large-scale Motions in the Atmospheric Boundary Layer and their Interactions with Utility-scale Wind Turbines
大气边界层超大规模运动的成因和动力学及其与公用事业规模风力涡轮机的相互作用
  • 批准号:
    1705837
  • 财政年份:
    2017
  • 资助金额:
    $ 50.25万
  • 项目类别:
    Standard Grant

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