CAREER: Quantifying Wind Hazards on Buildings in Urban Environments

职业：量化城市环境中建筑物的风害

• 批准号: 1749610
• 负责人: Catherine Gorle
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749610&HistoricalAwards=false
• 关键词: CAREER; Quantifying; Wind; Hazards; Buildings; CAREER; Quantifying; Wind; Hazards; Buildings

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.

在过去十年中，美国发生的天气和气候灾难中有三分之二是极端风事件。在这些极端事件中，建筑物的防风设计在确保国家的福利和繁荣方面起着重要作用，在这些极端事件中降低了建筑物的损失，死亡，社会破坏和商业不连续性。随着国家建筑库存中越来越多的大部分位于城市环境中，面临着巨大的挑战。与不受干扰的大气边界层风速相比，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能会使建筑物的局部风速提高50％或更高。设计风负载的常规计算不能解释这些干扰效果，因此，显着低估了建筑物上的风负载，并导致建筑物设计不足。这个教师早期职业发展计划（职业）奖的目标是通过建立可以量化和减少这些现象计算预测的不确定性的计算框架来促进对风流现象的基本了解。基于计算预测和不确定性量化做出明智的决策的能力将导致优化的设计，以提供更多的风能，从而更安全的建筑物和城市。高中，本科，研究生和高中老师将参加研究计划。 这项研究产生的实验和数值数据集将被利用，为高中，本科生和研究生的风力工程建立主动学习模块。在奖项的第二年和五年内举行的研讨会将支持多元化工程师社区的教育，以了解城市流量和风载现象的复杂性以及计算模型，风洞测试和现场实验的优势和劣势。该研究将使用佛罗里达州国际大学的风能设施的NSF支持的自然危害工程研究基础设施（NHERI）和NHERI数据仓库中的档案项目数据（https://www.designsignsafe-ci.org）。这项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，以及由此产生的干扰对城市环境中建筑物的风负载的影响。 该研究计划将涉及一项综合计划，以实地测量，风洞测试和计算流体动力学（CFD）模拟，并具有不确定性量化和数据同化。代表城市环境的斯坦福大学校园的工程四边形将作为实施研究计划的测试床。将探索新颖的随机方法，而不是进行传统的确定性研究，以便与置信区间进行实验和数值结果的比较。 研究的特定重点将在：（1）使用数据同化算法减少与流入边界条件相关的不确定性，（2）（2）对几何简化效应的系统定量，以及（3）使用多足性算法来减少湍流模型的模型。研究结果将提供有关功能性能和具有不同忠诚度不同的模型集成的基本新信息，并将指示利用城市传感器网络以提高预测的准确性的潜力。该研究计划将从根本上提高人们对干扰效应的理解，并在城市流量和风载上为CFD建模带来大量进展。从更广泛的角度来看，这项研究产生的新型计算策略将使其他可持续的城市设计问题受到风，例如街道峡谷和建筑通风和建筑通风，室外和室内空气质量，收获可再生能源资源的收获，以及对热岛减轻的城市规划，这是对NSF的法定任务的审查，并通过评估了范围，这一奖项是通过评估范围的范围来进行的。