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 (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.
