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.

这项探索性研究的早期概念赠款（急切）将建立，验证和传播国家科学基金会（NSF）支持的自然危害工程研究基础设施（NHERI）边界层风隧道（BLWT）（佛罗里达大学（UF）（UF））。风洞测试仍然是评估结构上的风负载并告知风力设计以降低损害成本的最常见方法。但是，风洞实验具有局限性，例如测量分辨率以及获得风速和压力场同时记录的挑战。数值模拟，例如大型涡流模拟（LES），提供了填补这些空白的机会，但是研究社区目前并未最佳地利用此类模拟功能。一个重要的障碍是，当前的数值建模能力主要是针对固定的标准中性风轮廓量身定制的。相反，诸如UF的BLWT之类的风隧道正在越来越多地实施高级功能，以再现更复杂的湍流风场，从而造成结构性损害。该研究项目将为这些复杂的风场建立数值模拟功能。为了最大程度地利用项目的潜在影响，将通过与当前UF BLWT用户群进行结构化访谈来定义验证测试用例和相应的数字双工具集和对模拟功能的相应数字双重工具集和教程。由此产生的数字双胞胎能力将使共同利用数值和实验模型，以提高对驱动建筑物和民用基础设施损害的动荡的风负载的理解，并提高风能弹性设计。模拟数据和记录的源代码将在NHERI数据仓库（https://www.designsignsafe-ci.org）中进行存档并公开提供。这种渴望将有助于NSF在国家风暴影响减少计划中的作用。该研究的具体目标是建立和传播一种数值建模策略，以重现UF BLWT中产生的复杂湍流风场。对于标准的中性日志律风场，流入边界条件通常采用人造湍流生成方法。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复制目标风场。当目标是建模更复杂的湍流风场时，这种校准挑战会加剧，例如，可以在UF BLWT中产生的明显粗糙度sublayer的边界层，这对于低层建筑物对建筑物的重要性很重要，其中建筑物沉浸在粗糙度sublayers sublayer（建筑物的粗糙度高度上）。该项目的第一个目的是探索计算上有效且准确的方法，以数值重现这些粗糙度的子层。人造湍流流入器，上游粗糙度解决模拟，源术语强迫方法和机器学习方法的不同组合将得到研究并根据实验数据进行验证。该项目的第二个目标是通过共同设计数字双工具集和与BLWT用户群的教程来支持所得湍流生成方法的广泛传播。数字风隧道还可以帮助确定物理测试的最佳测量位置，并潜在地支持数据填充的位置限制了物理测量的空间分辨率。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。