Collaborative Research: Wind Tunnel Modeling of Higher-Order Turbulence and its Effects on Structural Loads and Response

合作研究：高阶湍流的风洞建模及其对结构载荷和响应的影响

• 批准号: 1930625
• 负责人: Kurtis Gurley
• 金额: $ 43.08万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930625&HistoricalAwards=false
• 关键词: Collaborative; Research; Wind; Tunnel; Modeling

Wind hazards are among the most destructive and costly natural forces confronting civil infrastructure. To mitigate risk and business interruptions, reduce damage, and save lives it is essential that we understand the basic nature of wind forces. One important tool for assessing wind loads on structures is the Boundary Layer Wind Tunnel (BLWT), which simulates the effects of high intensity wind field on scaled structures in a controlled environment. The University of Florida Boundary Layer Wind Tunnel (UF-BLWT) is a shared-use facility supported by the National Science Foundation (NSF) Natural Hazards Engineering Research Infrastructure (NHERI) program. The UF-BLWT is a state-of-the-art facility capable of rapidly modifying wind behavior in an automated fashion to investigate the effects of terrain on wind fields and resulting forces on structures. This study will conduct a set of novel experiments at the UF-BLWT to understand the influence of terrain variations on peak wind pressures that load structures during storms. The experimental outcomes will provide a precise description of how a building?s surroundings affect wind pressures and enabling engineers to cost effectively design to survive extreme winds. The novel experiments proposed herein will be further leveraged to support unique educational initiatives including a student exchange program between University of Florida and Johns Hopkins University, and a NSF NHERI workshop on advanced cyber-physical, data-driven, and active learning experimental designs applied to BLWT modeling. A trove of data generated through this study will be curated and published for public access, and will serve as a resource for the wind engineering and machine learning communities as well as an educational resource for teaching wind engineering courses. The project will also support the PIs to continue individual outreach activities at their home institutions that include afterschool programs and internships for underprivileged youth.The Boundary Layer Wind Tunnel (BLWT) is a commonly used tool for assessing wind loads on structures. Existing BLWT facilities routinely match first- and second-order wind field models that have been validated with full-scale wind measurements. However, a growing body of evidence suggests that winds in the roughness sublayer and the inertial sublayer exhibit non-Gaussian higher-order properties in both full-scale wind measurements and BLWT wind fields. These non-Gaussian properties can strongly influence peak wind pressures, which govern certain structural limit states and play an important role in design. To date, no systematic study has been conducted to investigate the influence of these higher-order features in a BLWT, let alone understand their connection to the BLWT roughness elements in an effort to control the higher-order properties of mechanically induced turbulence. We propose a BLWT effort derived from two fundamental hypotheses: 1. Second-order equivalent wind fields can possess different higher-order properties and these properties can be linked to surface roughness features; 2. Differences in higher-order properties of the wind field can significantly influence peak pressures and consequently the response of structures. These hypotheses will be tested through a sequence of four tasks that will systematically modify the automated roughness element array (terraformer) unique to the University of Florida BLWT to identify roughness arrays that create second-order equivalent, but higher-order divergent wind fields. Machine learning methods will be employed to identify relationships between roughness element configurations and higher-order statistical properties of the wind field. The effect of these higher-order wind fields on structures will be studied by investigating peak pressures on two low-rise bluff bodies and measuring the dynamic response of a single degree of freedom flexible structure with tunable nonlinear response.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.

风灾害是民用基础设施面临的最具破坏性和代价最高的自然力量之一。为了降低风险和业务中断，减少损失并挽救生命，我们必须了解风力的基本性质。边界层风洞（BLWT）是评估结构风荷载的一个重要工具，它模拟了受控环境中高强度风场对缩尺结构的影响。佛罗里达大学边界层风洞（UF-BLWT）是由美国国家科学基金会（NSF）自然灾害工程研究基础设施（NHERI）计划支持的共享使用设施。UF-BLWT是一种最先进的设施，能够以自动化的方式快速修改风行为，以研究地形对风场的影响以及对结构的作用力。这项研究将在UF-BLWT进行一系列新颖的实验，以了解地形变化对风暴期间加载结构的峰值风压的影响。实验结果将提供一个精确的描述如何建设？的环境影响风压，使工程师能够以成本效益的设计，以生存极端的风。本文提出的新实验将进一步利用，以支持独特的教育举措，包括佛罗里达大学和约翰霍普金斯大学之间的学生交流计划，和NSF NHERI研讨会先进的网络物理，数据驱动，和主动学习的实验设计应用于BLWT建模。通过这项研究产生的大量数据将被整理和发布，供公众访问，并将作为风力工程和机器学习社区的资源，以及风力工程课程教学的教育资源。该项目还将支持PI继续在其家乡机构开展个人外展活动，包括为贫困青年提供课后计划和实习机会。边界层风洞（BLWT）是评估结构风荷载的常用工具。现有的BLWT设施通常匹配一阶和二阶风场模型，这些模型已经过全尺寸风测量的验证。然而，越来越多的证据表明，在粗糙子层和惯性子层的风表现出非高斯高阶特性，在全尺度风测量和BLWT风场。这些非高斯特性可以强烈地影响峰值风压，这决定了某些结构的极限状态，并在设计中发挥重要作用。到目前为止，还没有系统的研究已经进行调查的影响，这些高阶功能的BLWT，更不用说了解他们的连接BLWT粗糙度元素，努力控制机械诱导湍流的高阶特性。我们提出了一个BLWT的努力来自两个基本假设：1。二阶等效风场可以具有不同的高阶特性，这些特性可以与表面粗糙度特征相关联; 2.风场高阶特性的差异会显著影响峰值压力，从而影响结构的响应。这些假设将通过一系列四项任务进行测试，这些任务将系统地修改佛罗里达大学BLWT独有的自动粗糙度元素阵列（terraformer），以识别创建二阶等效但更高阶的粗糙度阵列发散风场。机器学习方法将被用来识别粗糙度元素配置和风场的高阶统计特性之间的关系。这些高阶风场对结构的影响将通过调查两个低层海崖体上的峰值压力和测量具有可调非线性响应的单自由度柔性结构的动态响应来研究。该奖项反映了NSF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而被认为值得支持。