CAREER: Characterization of Turbulence in Urban Environments for Wind Hazard Mitigation

职业:城市环境湍流特征以减轻风灾

基本信息

  • 批准号:
    2340755
  • 负责人:
  • 金额:
    $ 58.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-04-01 至 2029-03-31
  • 项目状态:
    未结题

项目摘要

In the past decade, losses from extreme wind events have exceeded those from all other natural disasters combined. Among hurricanes, tornadoes, thunderstorm downbursts, and other phenomena, virtually every region of the U.S. is at risk of extreme winds. Predictions from climate models anticipate an escalation in the occurrence and severity of these hazards, underscoring the need for cost-effective design concepts to create wind-resistant buildings. The structural integrity and long-term performance of low-rise buildings and civil infrastructure are heavily influenced by atmospheric turbulence near ground level where this infrastructure exists. Yet, a solid grasp of this flow phenomenon is lacking, limiting the ability to create risk-consistent design guidance. This Faculty Early Career Development (CAREER) award will support research that attempts to address this knowledge gap by advancing the fundamental understanding of turbulence in densely populated environments and the associated wind loads on structures. The project will utilize a combination of wind tunnel experiments, computer simulations, and theoretical developments in fluid dynamics. Findings from this project will enable improvements in wind-resistant design standards, bolstering national welfare and prosperity. Research activities will be complemented by an educational and outreach program leveraging recent advances in virtual and augmented reality technology to enhance teaching and accessibility to engineering education. This award will contribute to the U.S. National Science Foundation (NSF) role in the National Windstorm Impact Reduction Program (NWIRP).The specific objective of this project is twofold: the first is to characterize turbulence and fundamental mechanisms responsible for extreme wind events in urban areas under stationary and non-stationary flow conditions; the second is to derive improved analytical formulations for flow statistics that are relevant to the precise characterization of wind-loading conditions. An extensive series of wind tunnel tests and high-fidelity computational fluid dynamics simulations of flow over idealized urban environments will form the basis for the analysis. The hypothesis is that analysis of Reynolds stress budget equations combined with modern model reduction and coherent-structures identification techniques will enable the development of conceptual formulations encoding the dependency of first and higher order flow statistics onto surface morphology and flow forcing conditions. The analytical models will be derived for integration within building design codes to improve the resilience of low-rise structures, with potential long-lasting impacts on large-scale community resilience to the changing landscape of wind hazards. The project will utilize the NSF-supported Natural Hazards Engineering Research infrastructure (NHERI) Boundary Layer Wind Tunnel at the University of Florida and will archive and make publicly available the project data in the NHERI Data Depot (https://www.DesignSafe-CI.org).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.
在过去十年中,极端风事件造成的损失超过了所有其他自然灾害的总和。在飓风、龙卷风、雷暴下击暴流和其他现象中,美国几乎每个地区都面临极端大风的风险。根据气候模型的预测,这些灾害的发生和严重程度将不断升级,这突出表明需要具有成本效益的设计理念来建造抗风建筑。低层建筑物和民用基础设施的结构完整性和长期性能受到基础设施所在地附近大气湍流的严重影响。然而,缺乏对这种流动现象的坚实把握,限制了创建风险一致的设计指导的能力。该学院早期职业发展(CAREER)奖将支持试图通过推进对人口稠密环境中湍流的基本理解以及结构上的相关风荷载来解决这一知识差距的研究。该项目将利用风洞实验,计算机模拟和流体动力学理论发展的结合。该项目的研究结果将有助于提高抗风设计标准,促进国家福利和繁荣。研究活动将通过教育和推广计划来补充,该计划利用虚拟和增强现实技术的最新进展,以提高工程教育的教学和可访问性。该奖项将有助于美国国家科学基金会(NSF)在国家风暴影响减少计划(NWIRP)中发挥作用。该项目的具体目标有两个:第一个是描述稳定和非稳定流动条件下城市地区极端风事件的湍流和基本机制;第二是推导出与风荷载条件的精确特性有关的流量统计的改进的分析公式。一系列广泛的风洞试验和高保真计算流体动力学模拟的流动在理想化的城市环境将形成分析的基础。我们的假设是,结合现代模型简化和相干结构识别技术的雷诺应力预算方程的分析将使概念配方的发展编码的依赖性的第一和高阶流统计表面形态和流动强迫条件。分析模型将被用于建筑设计规范的整合,以提高低层结构的复原力,对大规模社区应对不断变化的风灾景观的复原力产生潜在的长期影响。该项目将利用美国国家科学基金会支持的自然灾害工程研究基础设施(NHERI)边界层风洞在佛罗里达大学,并将存档和公开提供的项目数据在NHERI数据仓库(https:www.DesignSafe-CI.org)。该奖项反映了美国国家科学基金会的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量(0)
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Marco Giometto其他文献

Wind Extremes over Built Terrain: Characterization and Geometric Determinants
  • DOI:
    10.1007/s10546-025-00899-9
  • 发表时间:
    2025-02-07
  • 期刊:
  • 影响因子:
    2.200
  • 作者:
    Jing Wang;Maider Llaguno-Munitxa;Qi Li;Marco Giometto;Elie Bou- Zeid
  • 通讯作者:
    Elie Bou- Zeid
Data-driven met-ocean model for offshore wind energy applications
用于海上风能应用的数据驱动的气象海洋模型
  • DOI:
    10.1088/1742-6596/2767/5/052005
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kianoosh Yousefi;G. S. Hora;Hongshuo Yang;Marco Giometto
  • 通讯作者:
    Marco Giometto
Path-conservative well-balanced high-order finite-volume solver for the volume-averaged Navier–Stokes equations with discontinuous porosity
用于具有不连续孔隙率的体积平均纳维 - 斯托克斯方程的路径守恒的良好平衡高阶有限体积求解器
  • DOI:
    10.1016/j.jcp.2025.113978
  • 发表时间:
    2025-07-15
  • 期刊:
  • 影响因子:
    3.800
  • 作者:
    Jaeyoung Jung;Manuel Schmid;Jacob Fish;Ensheng Weng;Marco Giometto
  • 通讯作者:
    Marco Giometto
Introducing new morphometric parameters to improve urban canopy air flow modeling: A CFD to machine-learning study in real urban environments
  • DOI:
    10.1016/j.uclim.2024.102173
  • 发表时间:
    2024-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Jonas Wehrle;Christopher Jung;Marco Giometto;Andreas Christen;Dirk Schindler
  • 通讯作者:
    Dirk Schindler

Marco Giometto的其他文献

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{{ truncateString('Marco Giometto', 18)}}的其他基金

Collaborative Research: Sea-state-dependent drag parameterization through experiments and data-driven modeling
合作研究:通过实验和数据驱动建模进行与海况相关的阻力参数化
  • 批准号:
    2404369
  • 财政年份:
    2024
  • 资助金额:
    $ 58.5万
  • 项目类别:
    Standard Grant
Development of a Physics-Data Driven Surface Flux Parameterization for Flow in Complex Terrain
开发物理数据驱动的复杂地形流动表面通量参数化
  • 批准号:
    2336002
  • 财政年份:
    2024
  • 资助金额:
    $ 58.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: Snow Transport in Katabatic Winds and Implications for the Antarctic Surface Mass Balance: Observations, Theory, and Numerical Modeling
合作研究:下降风中的雪输送及其对南极表面质量平衡的影响:观测、理论和数值模拟
  • 批准号:
    2035078
  • 财政年份:
    2021
  • 资助金额:
    $ 58.5万
  • 项目类别:
    Standard Grant

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Collaborative Research: TRTech-PGR TRACK: Discovery and characterization of small CRISPR systems for virus-based delivery of heritable editing in plants.
合作研究:TRTech-PGR TRACK:小型 CRISPR 系统的发现和表征,用于基于病毒的植物遗传编辑传递。
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使用一氧化氮进行热层估计和表征 (TECHNO)
  • 批准号:
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  • 财政年份:
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