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.

该奖项将重点关注高层建筑形状的优化设计，以满足多个利益相关者相互竞争的性能目标，包括其在风荷载下的性能，建筑物的形状是最早的设计决策之一，对建筑物的底层结构系统具有决定性影响。 、使用和极端负载下的性能、生命周期成本和建筑吸引力在当前实践中，设计通常基于历史上提供良好性能的形状，并在其中进行一些测试。风洞，留下很大一部分搜索空间为了解决这些缺点，该奖项将开发一种自动化方法，将数值搜索算法、实验风洞测试和先进制造结合起来，对设计空间进行系统和详尽的搜索。这项研究将有助于推动工程设计的未来走向优化和自动化，同时解决风工程的基础研究问题。提供独特的机会吸引来自以下国家的学生项目数据将在 NSF 支持的自然灾害工程中存档并公开提供。研究基础设施 (NHERI) 数据仓库 (https://www.DesignSafe-CI.org) 该奖项将有助于 NSF 在国家风暴影响减少计划 (NWIRP) 中发挥作用。这项研究将汇集传统的风洞实验方法。和自动化设计技术来测试高层建筑风荷载设计的三个基本假设：（i）可以利用智能计算、网络物理测试和混合制造来有效地探索几何设计空间，（ii）几何设计空间可以作为一个连续体进行探索，从根本上改变优化结果，并且（iii）优化问题的制定将对最佳形状产生重大影响。这项研究将利用混合制造来创建和精确修改风洞样本，从而实现。形状优化和风洞测试的紧密结合。将使用佛罗里达大学 NSF 支持的 NHERI 边界层风洞来完成，将产生新知识，包括：（i）适合探索最佳结构形状的启发式优化算法，（ii）可以减少的替代模型。风洞实验的数量，(iii) 混合制造系统，将增材加工和减材加工相结合，以高效且经济高效地修改建筑模型，以及 (iv) 参数化方法，允许发现非直观的空气动力学特征，以减少这项研究将能够对候选设计进行智能实验探索，因此有可能发现新的创新解决方案，以建造更高、更轻、更可持续的建筑。该奖项反映了 NSF 的法定奖项。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。