CAREER: Remote Sensing for Enhanced Understanding of Tornado Actions and Broadened STEM Education in Rural West Texas

这个教师早期职业发展计划（CAREER）奖的目标是通过在小规模和大规模使用遥感技术来促进对龙卷风对结构影响的理解。虽然龙卷风是地球上最强的风之一，但对这些强风的强度，频率和结构的充分了解仍然是难以捉摸的。为了有效地设计为生命和财产提供保护的结构，有必要更好地了解龙卷风中的风速，龙卷风风与结构的相互作用以及龙卷风发生的可能性。 在小范围内，这项研究将采用激光扫描和数字成像等尖端技术，为龙卷风破坏的结构创建高度详细的3D数字模型。 这项研究还将通过使用地球观测卫星图像来检查大规模的龙卷风现象，以帮助检测“失踪”（未观察到的）龙卷风。改进区域龙卷风气候、风结构和风速的表征将改进龙卷风预测模型和基于风险的设计方法。提高对龙卷风危害的认识将提高社会对灾害的规划能力，并迅速作出反应和从灾害中恢复，从而促进国家福利和繁荣。该项目还将提供一个平台，通过鼓励本科生与美国和国际研究机构进行研究合作，以及一个广泛的自然灾害研讨会/研讨会系列，鼓励追求研究生教育，以加强西德克萨斯州大部分农村地区的STEM教育。和工业到校园，激励学生追求研究和创新，为具有挑战性的自然灾害缓解问题。 该项目的数据将在NSF支持的自然灾害工程研究基础设施（NHERI）DesignSafe数据库（https：//www.example.com）中存档和共享。www.DesignSafe-ci.org该职业奖的目标是通过遥感实现快速和全面的多尺度风灾评估，提高对龙卷风结构相互作用的理解，并激励和培训学生，通过最大限度地减少风灾造成的生命和财产损失，在确保国家福利和繁荣方面发挥重要作用。通过战略研究合作，首席研究员和本科生研究人员将通过遥感技术在多个尺度上探索龙卷风的行动。 在小尺度上，工程钢结构龙卷风引起的故障的现实捕捉增强建模将利用3D现实捕捉平台（摄影测量和激光雷达）和这些平台的优化，以平衡受损结构的详细法医分析的广度和深度（与德克萨斯理工大学国家风力研究所合作）。然后，通过测量挠度和研究失效模式，将该模型用于调查龙卷风对结构的影响。在大规模上，该项目将研究通过多光谱图像分析探测“失踪”龙卷风的方法，以加强人口稀少和森林地区的龙卷风气候学（与加拿大西部大学合作）。该研究将推进复杂的龙卷风结构相互作用的知识，龙卷风强度的估计，并通过安全，可靠，高效和尖端的遥感技术在空间尺度上检测龙卷风的发生，从微小的结构变形到整体龙卷风路径水平。该研究将为物理和数值龙卷风负荷模拟提供验证方法，促进对增强型藤田尺度风速估计的必要调整，并探索高分辨率成像平台的优化，以最有效地促进对龙卷风对建筑环境影响的理解。对“失踪”龙卷风的探测将加强气候学研究，为改进预报、警报和风险模型提供基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this Faculty Early Career Development Program (CAREER) award is to advance understanding of the impacts of tornadoes on structures through the use of remote sensing technologies at both small and large scales. Although tornadoes are known to produce some of the strongest winds on earth, sufficient understanding of the magnitude, frequency, and structure of these violent winds remains elusive. To effectively design structures that provide protection for life and property against tornadoes, it is necessary to better understand the wind speeds in tornadoes, the interaction of tornado winds with structures, and the likelihood of tornado occurrences. At the small scale, this research will employ cutting-edge technologies, such as laser scanning and digital imaging, to create highly detailed, 3D digital models of tornado-damaged structures. The research will also examine large-scale tornado phenomena by using earth-observing satellite images to help detect "missing" (unobserved) tornadoes. Improved characterization of regional tornado climates, wind structures, and wind speeds will improve tornado forecast models and risk-based design methodologies. Heightened understanding of tornado hazard will advance society's ability to plan for, and rapidly respond and recover from, disasters, thus promoting national welfare and prosperity. This project also will provide a platform to enhance STEM education in a large rural portion of West Texas by encouraging the pursuit of graduate education through engaging undergraduate students in research collaborations with U.S. and international research institutions and an expansive Natural Hazards Workshop/Symposium series that will bring researchers from academia, government, and industry to campus to inspire students to pursue research and innovation for challenging natural hazard mitigation problems. Data from this project will be archived and shared in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) DesignSafe Data Depot (https://www.DesignSafe-ci.org). The objectives of this CAREER award are the achievement of rapid and comprehensive multi-scale wind damage assessments through remote sensing, advancement of understanding of tornado-structure interaction, and inspiration and training of students for vital roles in securing national welfare and prosperity through minimizing life and property losses from wind hazards. Through strategic research collaborations, the Principal Investigator and undergraduate student researchers will explore tornado actions via remote sensing technologies at multiple scales. At small scales, reality-capture-enhanced modeling of tornado-induced failures of engineered steel structures will utilize 3D reality-capture platforms (photogrammetry and lidar) and optimization of these platforms to balance the breadth and depth of detailed forensic analyses of damaged structures (in collaboration with the Texas Tech University National Wind Institute). This modeling will then be used to investigate tornado impacts on structures through the measurement of deflections and study of failure patterns. At the large scale, the project will investigate a methodology to detect "missing" tornadoes via multi-spectral image analysis to enhance tornado climatology in sparsely populated and forested areas (in collaboration with Western University in Canada). The research will advance the knowledge of complex tornado-structure interactions, estimation of tornado intensity, and detection of tornado occurrences via safe, reliable, efficient, and cutting-edge remote sensing technologies at spatial scales ranging from minute structural deformations to the overall tornado-path level. The research will provide validation methods for physical and numerical tornado load simulations, facilitate necessary adjustments to wind speed estimates in the Enhanced Fujita Scale, and explore optimization of high-resolution imaging platforms to most effectively advance the understanding of tornado effects on the built environment. Detection of "missing" tornadoes will enhance climatology studies, providing a basis to improve forecasts, warnings, and risk models.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.