CAREER: Quantifying Wind Hazards on Buildings in Urban Environments

在过去十年中，美国发生的三分之二的天气和气候灾害是极端风事件。建筑物的抗风设计在确保国家的福利和繁荣方面发挥着重要作用，通过减少这些极端事件期间的建筑物损坏，死亡，社会混乱和业务中断。随着全国越来越大的建筑库存位于暴露于极端风事件的城市环境中，出现了相当大的挑战。由不同建筑物几何形状之间的相互作用引起的干扰效应可以使建筑物上的局部风速比未受干扰的大气边界层风速增加50%或更多。设计风荷载的常规计算不考虑这些干扰效应，因此，可能会大大低估建筑物上的风荷载，导致建筑设计不合理。这个教师早期职业发展计划（CAREER）奖的目标是通过建立计算框架，可以量化，并在可能的情况下，减少这些现象的计算预测的不确定性，促进对风流动现象的基本理解，用于弹性和可持续建筑和城市环境的设计。基于计算预测和不确定性量化做出明智决策的能力将导致优化设计，以实现更好的抗风能力，从而更安全的建筑物和城市。高中，本科，研究生和高中教师将参加研究计划。 从这项研究中产生的实验和数值数据集将被用来建立积极的学习模块风工程高中，本科和研究生。在该奖项的第二年和第五年举行的研讨会将支持对多元化工程师社区的教育，以了解城市流动和风荷载现象的复杂性以及计算模型，风洞测试和现场实验的优缺点。该研究将使用NSF支持的自然灾害工程研究基础设施（NHERI）在佛罗里达国际大学的风墙设施，并将项目数据存档在NHERI数据仓库（www.DesignSafe-ci.org）。本研究将建立计算模型，以量化周围建筑环境对当地风速和湍流的影响，以及由此产生的对城市环境中建筑物风荷载的干扰效应。 该研究计划将涉及一个全面的现场测量，风洞试验和计算流体动力学（CFD）模拟与不确定性量化和数据同化计划。斯坦福大学校园内的工程四合院是城市环境的代表，将作为实施研究计划的试验台。而不是追求传统的确定性调查，新的随机方法将被探索，使实验和数值结果与置信区间的比较。 研究的具体重点将是：（1）使用数据同化算法来减少与流入边界条件相关的不确定性，（2）几何简化效果的系统量化，以及（3）使用多保真度算法来减少湍流模型形式的不确定性。研究结果将提供关于不同保真度模型的适用性和集成的重要新信息，并将表明利用城市传感器网络提高预测准确性的潜力。该研究计划将从根本上改善对干扰效应的理解，并使城市流动和风荷载的CFD建模取得相当大的进展。更广泛地说，这项研究产生的新的计算策略将有利于其他受风影响的可持续城市设计问题，如街道峡谷和建筑通风，室外和室内空气质量，收获可再生能源，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

Two-thirds of the weather and climate disasters that have occurred in the United States over the past ten years were extreme wind events. Wind-resistant design of buildings plays an important role in securing the nation's welfare and prosperity through reduced building damage, fatalities, societal disruptions, and business discontinuities during these extreme events. Considerable challenges arise as an increasingly large portion of the nation's building inventory is located in urban environments with exposure to extreme wind events. Interference effects, caused by interactions between different building geometries, can increase the local wind speed on buildings by 50% or more compared to the undisturbed atmospheric boundary layer wind speed. Routine calculations for design wind loads do not account for these interference effects and, therefore, significantly can underestimate the wind loading on buildings and result in inadequate building design. The goal of this Faculty Early Career Development Program (CAREER) award is to advance fundamental understanding of wind flow phenomena for the design of resilient and sustainable buildings and urban environments through establishment of computational frameworks that can quantify and, where possible, reduce the uncertainty in computational predictions of these phenomena. The capability to make well-informed decisions based on computational predictions and uncertainty quantification will lead to optimized designs for more wind-resilient and, thus, safer buildings and cities. High school, undergraduate, and graduate students and high school teachers will participate in the research program. The experimental and numerical data sets resulting from this research will be leveraged to establish active learning modules for wind engineering for high school, undergraduate, and graduate students. Workshops held during years two and five of the award will support the education of a diverse community of engineers to understand the complexity of urban flow and wind loading phenomena and the strengths and weaknesses of computational models, wind tunnel tests, and field experiments. The research will use the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Wall of Wind facility at Florida International University and archive project data in the NHERI Data Depot (https://www.DesignSafe-ci.org). This research will establish computational models to quantify the influence of the surrounding built environment on the local wind speed and turbulence and the resulting interference effects on the wind loads on buildings in urban environments. The research plan will involve a comprehensive program of field measurements, wind tunnel tests, and computational fluid dynamics (CFD) simulations with uncertainty quantification and data assimilation. The Engineering Quad on Stanford's campus, which is representative of an urban environment, will serve as a test bed for implementing the research plan. Rather than pursuing a traditional deterministic investigation, novel stochastic methods will be explored to enable comparison of experimental and numerical results with confidence intervals. Specific focus of the research will be on: (1) using data assimilation algorithms to reduce uncertainty related to the inflow boundary conditions, (2) systematic quantification of the effect of geometrical simplifications, and (3) using multi-fidelity algorithms to reduce turbulence model form uncertainties. The research results will provide essential new information on the fitness-for-purpose and integration of models with different levels of fidelity and will indicate the potential of leveraging urban sensor networks to improve the accuracy of the predictions. The research program will result in a fundamentally improved understanding of interference effects and enable considerable advances in CFD modeling for urban flow and wind loading. More broadly, the novel computational strategies resulting from this research will benefit other sustainable urban design problems influenced by wind, such as street canyon and building ventilation, outdoor and indoor air quality, harvesting renewable energy resources, and urban planning for heat island mitigation.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.