EAGER: Advanced Digital Twin Capability for Turbulent Wind Fields in the NHERI Boundary Layer Wind Tunnel at the University of Florida

EAGER：佛罗里达大学 NHERI 边界层风洞中湍流风场的先进数字孪生能力

• 批准号: 2302650
• 负责人: Catherine Gorle
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302650&HistoricalAwards=false
• 关键词: EAGER; Advanced; Digital; Twin; Capability

This EArly-concept Grant for Exploratory Research (EAGER) will establish, validate, and disseminate an advanced digital twin capability for the National Science Foundation (NSF)-supported Natural Hazards Engineering Research Infrastructure (NHERI) boundary layer wind tunnel (BLWT) at the University of Florida (UF). Wind tunnel testing remains the most common approach for assessing wind loads on structures and informing wind resistant design to reduce the cost of damage. However, wind tunnel experiments have limitations, such as the measurement resolution and the challenge of obtaining simultaneous records of wind velocity and pressure fields. Numerical simulations, such as Large Eddy Simulation (LES), offer an opportunity to fill in these gaps, but such simulation capabilities are currently not optimally leveraged by the research community. An important barrier is that current numerical modeling capabilities are mostly tailored to stationary, standard neutral wind profiles; in contrast, wind tunnels such as the BLWT at UF are increasingly implementing advanced capabilities to reproduce more complex turbulent wind fields that cause structural damage. This research project will establish numerical simulation capabilities for these complex wind fields. To maximize the potential impact of the project, validation test cases and a corresponding digital twin tool set and tutorial for the simulation capabilities will be defined through structured interviews with the current UF BLWT user base. The resulting digital twin capability will make it possible to jointly leverage numerical and experimental models to improve understanding of the turbulent wind loads that drive damage to buildings and civil infrastructure and to advance wind resilient design. Simulation data and documented source codes will be archived and made publicly available in the NHERI Data Depot (https://www.DesignSafe-ci.org). This EAGER will contribute to the NSF role in the National Windstorm Impact Reduction Program. The specific goal of the research is to establish and disseminate a numerical modeling strategy for reproducing complex turbulent wind fields generated in the UF BLWT. For standard neutral log-law wind fields, inflow boundary conditions commonly employ artificial turbulence generation methods. Since the velocity statistics of artificial turbulence evolve within the computational domain, some form of calibration is required to ensure that the target wind field is correctly reproduced. This calibration challenge is exacerbated when the objective is to model more complex turbulent wind fields, such as the boundary layer with a pronounced roughness sublayer that can be produced in the UF BLWT, which is important for low-rise buildings, where the building is immersed in the roughness sublayer (the roughness height of the boundary layer is on the order of the building height). The first objective of this project is to explore computationally efficient and accurate methods for numerically reproducing these roughness sublayers. Different combinations of artificial turbulence inflow generators, upstream roughness resolving simulations, source term forcing methods, and machine learning approaches will be investigated and validated against experimental data. The second objective of this project is to support broad dissemination of the resulting turbulence generation method by co-designing a digital twin tool set and a tutorial with the BLWT user base. The digital wind tunnel can also help identify optimal measurement locations for physical testing and potentially support data infilling where there are limits on the spatial resolution of physical measurements.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.

这项早期概念探索性研究资助 (EAGER) 将为美国国家科学基金会 (NSF) 支持的自然灾害工程研究基础设施 (NHERI) 边界层风洞 (BLWT) 建立、验证和传播先进的数字孪生功能。佛罗里达大学（UF）。风洞测试仍然是评估结构风荷载和通知抗风设计以减少损坏成本的最常见方法。然而，风洞实验存在局限性，例如测量分辨率以及获得风速和压力场同时记录的挑战。大涡模拟 (LES) 等数值模拟提供了填补这些空白的机会，但研究界目前尚未充分利用此类模拟功能。一个重要的障碍是，当前的数值模拟能力大多是针对固定的、标准的中性风廓线而定制的；相比之下，UF 的 BLWT 等风洞越来越多地采用先进功能来重现更复杂的湍流风场，从而导致结构损坏。该研究项目将为这些复杂的风场建立数值模拟能力。为了最大限度地发挥该项目的潜在影响，将通过与当前 UF BLWT 用户群的结构化访谈来定义验证测试用例以及相应的数字孪生工具集和仿真功能教程。由此产生的数字孪生功能将能够联合利用数值和实验模型，以提高对导致建筑物和民用基础设施损坏的湍流风荷载的理解，并推进抗风设计。仿真数据和记录的源代码将在 NHERI 数据仓库 (https://www.DesignSafe-ci.org) 中存档并公开提供。该 EAGER 将有助于 NSF 在国家风暴影响减少计划中发挥作用。该研究的具体目标是建立并传播一种数值模拟策略，用于再现 UF BLWT 中产生的复杂湍流风场。对于标准中性对数律风场，流入边界条件通常采用人工湍流生成方法。由于人工湍流的速度统计在计算域内演变，因此需要某种形式的校准以确保正确再现目标风场。当目标是模拟更复杂的湍流风场（例如可在 UF BLWT 中生成的具有明显粗糙度子层的边界层，这对于建筑物浸入水中的低层建筑非常重要）时，这种校准挑战会加剧。在粗糙度子层中（边界层的粗糙度高度约为建筑物高度）。该项目的第一个目标是探索计算高效且准确的方法来数字再现这些粗糙度子层。将根据实验数据研究和验证人工湍流流入发生器、上游粗糙度解析模拟、源项强迫方法和机器学习方法的不同组合。该项目的第二个目标是通过与 BLWT 用户群共同设计数字孪生工具集和教程，支持由此产生的湍流生成方法的广泛传播。数字风洞还可以帮助确定物理测试的最佳测量位置，并有可能支持物理测量空间分辨率有限的数据填充。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。