ERI: Physical Simulation of Terrain-Induced and Large-Scale Turbulence Effects on the Effectiveness of Wind Mitigation Strategies for Low-Rise Buildings

ERI：地形诱发和大规模湍流对低层建筑防风策略有效性影响的物理模拟

• 批准号: 2138414
• 负责人: Pedro Fernandez-Caban
• 金额: $ 19.92万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138414&HistoricalAwards=false
• 关键词: ERI; Physical; Simulation; Terrain; Induced

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This Engineering Research Initiation (ERI) award will focus on the characterization of near-surface wind fields to investigate the effectiveness of roof load mitigation strategies for low-rise buildings. Wind-induced damage to roof components of low-rise buildings is predominantly attributed to extreme suction loads caused by vortices that develop when the oncoming wind flow impinges on the structure, resulting in flow detachment near roof corners and edges. Previous research has demonstrated the effectiveness of several wind mitigation strategies for alleviating uplift roof pressures under a limited number of idealized wind tunnel flow conditions. This research will leverage a novel flow-control instrument at the University of Florida Natural Hazards Engineering Research Infrastructure (NHERI) Experimental Facility to physically simulate a wide range of atmospheric flows in a large boundary layer wind tunnel. The project will integrate machine learning and computational modeling to predict extreme wind loading for unexplored wind tunnel configurations and fill critical knowledge gaps associated with the complex relation between atmospheric turbulence and aerodynamic loading. The large volumes of velocity and pressure data will be made available in the NHERI Data Depot (https://www.DesignSafe-ci.org). The research will address fundamental fluid-structure interaction questions while enhancing the performance of roof systems, and ultimately contribute to increasing wind hazard resilience of low-rise buildings. This award will contribute to the National Science Foundation role in the National Windstorm Impact Reduction Program (NWIRP). The specific goals of this research include: (i) the physical characterization of natural wind flows with precisely modulated frequency content, including low-frequency (large-scale) turbulence, which is traditionally deficient in wind tunnel tests conducted on large low-rise building models; (ii) experimental assessment of roof uplift load mitigation strategies for low-rise buildings tested under a wide range of properly calibrated upwind terrain conditions and large-scale turbulent structures; (iii) development and refinement of a deep neural network (DNN) to map complex relationships between incident turbulence and the effectiveness of wind mitigation strategies for reducing peak suction loads; and (iv) the calibration of numerical inflow turbulence models using high-fidelity flow velocity and pressure wind tunnel data. The datasets will offer a reliable calibration tool to enhance the accuracy of numerical models, and ultimately help advance computational wind engineering.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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。 该工程研究启动 (ERI) 奖项将重点关注近地表风场的特征，以研究低层建筑屋顶荷载缓解策略的有效性。低层建筑屋顶构件的风致损坏主要是由于迎面风流撞击结构时产生的涡流引起的极端吸力载荷，导致屋顶角落和边缘附近的气流脱离。先前的研究已经证明了几种减风策略在有限数量的理想风洞流动条件下减轻顶板抬升压力的有效性。这项研究将利用佛罗里达大学自然灾害工程研究基础设施 (NHERI) 实验设施的新型流量控制仪器，对大型边界层风洞中的各种大气流动进行物理模拟。该项目将整合机器学习和计算建模，以预测未探索的风洞配置的极端风载荷，并填补与大气湍流和空气动力载荷之间的复杂关系相关的关键知识空白。 NHERI 数据仓库 (https://www.DesignSafe-ci.org) 将提供大量速度和压力数据。该研究将解决基本的流体-结构相互作用问题，同时提高屋顶系统的性能，并最终有助于提高低层建筑的风灾抵御能力。该奖项将有助于国家科学基金会在国家风暴影响减少计划（NWIRP）中发挥作用。 这项研究的具体目标包括：（i）具有精确调制频率内容的自然风流的物理特征，包括低频（大规模）湍流，这在大型低层建筑上进行的风洞测试中传统上是存在缺陷的模型； (ii) 在各种适当校准的逆风地形条件和大型湍流结构下测试的低层建筑屋顶抬升荷载缓解策略的实验评估； (iii) 开发和完善深度神经网络（DNN），以绘制入射湍流与减少峰值吸力负荷的减风策略有效性之间的复杂关系； (iv) 使用高保真流速和压力风洞数据校准数值流入湍流模型。这些数据集将提供可靠的校准工具，以提高数值模型的准确性，并最终帮助推进计算风工程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。