Collaborative Research: Aerodynamic Shape Optimization of Tall Buildings using Automated Cyber-Physical Testing

合作研究：利用自动化网络物理测试对高层建筑进行空气动力学形状优化

• 批准号: 2028647
• 负责人: Zhaoshuo Jiang
• 金额: $ 28.55万
• 依托单位: San Francisco State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028647&HistoricalAwards=false
• 关键词: Collaborative; Research; Aerodynamic; Shape; Optimization

This award will focus on the optimal design of a tall building’s shape to meet competing performance objectives from multiple stakeholders, including its performance under wind loads. A building’s shape is one of the earliest design decisions and has a decisive impact on the building’s underlying structural system, performance under service and extreme loads, life-cycle costs, and architectural appeal. In current practice, design is often based on shapes that have historically provided good performance. Trial-and-error approaches are used with a few tests carried out in a wind tunnel, leaving significant portions of the search space unexplored, and therefore, design favors conventional shapes over innovative solutions. To address these shortcomings, this award will develop an automated approach that brings together numerical search algorithms, experimental wind tunnel testing, and advanced manufacturing for a systematic and exhaustive search of the design space. This research will help drive the future of engineering design as it trends toward optimization and automation while also addressing fundamental research questions in wind engineering. The collaboration in this project between a research-intensive university and a Hispanic-serving institution/primarily undergraduate institution will provide a unique opportunity to engage students from underrepresented minority groups in cutting-edge research, thus increasing the diversity of professionals in the field and producing globally competitive engineering graduates to match the demand for skilled STEM professionals. Project data will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-CI.org). This award will contribute to NSF's role in the National Windstorm Impact Reduction Program (NWIRP). This research will bring together traditional wind tunnel experimental methods and automated design techniques to test three fundamental hypotheses on the design of tall buildings for wind loading: (i) intelligent computing, cyber-physical testing, and hybrid manufacturing can be leveraged to efficiently explore the geometric design space, (ii) the geometric design space can be explored as a continuum to fundamentally change the optimization outcomes, and (iii) the formulation of the optimization problem will have a significant impact on the optimal shape. This research will leverage hybrid manufacturing to create and precisely modify wind tunnel specimens, enabling a close integration of shape optimization and wind tunnel testing. Testing will be done using the NSF-supported NHERI boundary layer wind tunnel at the University of Florida. New knowledge will be generated, including: (i) heuristic optimization algorithms that are suitable for exploring optimal structural shapes, (ii) surrogate models that can reduce the number of wind tunnel experiments, (iii) hybrid manufacturing systems that combine additive and subtractive machining to efficiently and cost-effectively modify building models, and (iv) parameterization methods that allow for discovery of non-intuitive aerodynamic features to reduce along-wind and across-wind structural responses. This research will enable the intelligent experimental exploration of candidate designs and, therefore, has the potential to discover new and innovative solutions to deliver taller, lighter, and more sustainable buildings.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.

该奖项将着重于高大建筑物的最佳设计，以实现来自多个利益相关者的竞争性能目标，包括其在风载下的性能。建筑物的形状是最早的设计决策之一，对建筑物的基本结构系统，服务和极端负载，生命周期成本和建筑外观有决定性的影响。在目前的实践中，设计通常基于历史上提供良好性能的形状。试验方法与在风洞中进行的一些测试一起使用，使搜索空间的大部分出乎意料，因此，设计偏爱常规形状而不是创新的解决方案。为了解决这些缺点，该奖项将开发一种自动化方法，该方法将数值搜索算法，实验风洞测试和高级制造汇集在一起​​，以系统地搜索设计空间。这项研究将有助于推动工程设计的未来，因为它趋向于优化和自动化，同时还解决了风力工程中的基本研究问题。研究密集型大学与西班牙裔服务机构/主要是本科机构之间的该项目的合作将为吸引来自代表性不足的少数群体的学生提供独特的机会，从而增加了领域的专业人员的多样性，从而增加了全球竞争性的工程毕业生，以适应熟练的STEM Professive的需求。项目数据将在NSF支持的自然危害工程研究基础设施（NHERI）数据仓库（https://www.designsignsafe-ci.org）中归档并公开提供。该奖项将有助于NSF在国家风暴减少影响计划（NWIRP）中的作用。这项研究将汇集传统的风洞实验方法和自动设计技术，以测试有关高大建筑物设计的三个基本假设：（i）智能计算，网络物理测试和混合制造可以有效地探索几何设计空间，（ii）不断探索的几何设计空间，（ii）构建的范围，（ii）构成了一定的变化。 （iii）优化问题的公式将对最佳形状产生重大影响。这项研究将利用混合动力制造来创建和精确修改风洞标本，从而可以密切整合形状优化和风洞测试。使用佛罗里达大学的NSF支持的NHERI边界层风洞进行测试。将生成新知识，包括：（i）适合探索最佳结构形状的启发式优化算法，（ii）可以减少风洞实验数量的代孕模型，（III）混合制造系统，这些系统，结合添加剂和缩写机械，使其有效地和成本效果构建构建模型和（IV），以实现构建模型和（iv），以实现构建模型，并构建了（IV）。减少伴随和各个结构反应。这项研究将使人们能够对候选设计进行智能实验探索，因此有可能发现新的和创新的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响审查标准评估来反映了更高，更轻，更可持续的建筑物。