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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。该工程研究启动(ERI)奖将集中于近地表风场的特征，以调查低层建筑屋顶荷载缓解策略的有效性。风对低层建筑屋面构件的破坏主要是由于迎面而来的风冲击结构时产生的涡流所产生的极端吸力荷载，导致屋角和屋面边缘附近的气流脱落。以前的研究已经证明，在有限数量的理想化风洞流动条件下，几种风缓解策略对于缓解隆起屋顶压力是有效的。这项研究将利用佛罗里达大学自然危害工程研究基础设施(NHERI)实验设施中的一种新型流量控制仪器来物理模拟大型边界层风洞中的各种大气流动。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).)将提供大量的速度和压力数据这项研究将解决基本的流固耦合问题，同时提高屋顶系统的性能，并最终有助于提高低层建筑的抗风灾能力。该奖项将为国家科学基金会在国家减少风暴影响计划(NWIRP)中的作用做出贡献。这项研究的具体目标包括：(I)具有精确调制频率成分的自然风流的物理特征，包括低频(大尺度)湍流，这在大型低层建筑模型上进行的风洞测试中传统上是不足的；(Ii)在各种适当校准的上风地形条件和大尺度湍流结构下测试低层建筑的屋顶上抬载荷缓解策略的实验评估；(Iii)开发和完善深度神经网络(DNN)，以映射入射湍流与降低峰值吸力载荷的风缓解策略的有效性之间的复杂关系；(4)利用高保真流速和压力风洞数据对数值来流湍流模型进行定标。该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能工程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.