EAGER: Exploring Machine Learning and Atmospheric Simulation to Understand the Role of Geomorphic Complexity in Enhancing Civil Infrastructure Damage during Extreme Wind Events

EAGER：探索机器学习和大气模拟，以了解地貌复杂性在加剧极端风事件期间民用基础设施损坏方面的作用

• 批准号: 1841979
• 负责人: Forrest Masters
• 金额: $ 30万
• 依托单位: University of Florida
• 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: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841979&HistoricalAwards=false
• 关键词: EAGER; Exploring; Machine; Learning; Atmospheric

Motivated by the extensive damage to Puerto Rico caused by Hurricane Maria's landfall in September 2017, this EArly-concept Grant for Exploratory Research (EAGER) will study how complex topography can accelerate wind and, ultimately, exacerbate damage to buildings and other constructed civil infrastructure. This research will utilize recent advancements in machine learning and weather forecasting to predict wind speed-up in mountainous terrain and other complex terrestrial environments. The project will leverage the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Terraformer Boundary Layer Wind Tunnel (BLWT) at the University of Florida to characterize the surface wind field over geometrically scaled models of Puerto Rico and the municipal Islands of Vieques and Culebra. This EAGER is a collaboration between the University of Florida (which serves the most hurricane prone state in the U.S.) and the University of Puerto Rico at Mayaguez (a Hispanic-serving institution still recovering from Hurricane Maria), and graduate and undergraduate students from both institutions will be actively involved in the experimental and computational work. Anticipated project outcomes will include important new insights about the influence of topography on the behavior of damaging winds, new scientific tools that fuse experimentation with advanced computing methods to study extreme wind effects on constructed civil infrastructure, and benchmark datasets that will be made available to other researchers in the NHERI Data Depot (https://www.DesignSafe-ci.org). Knowledge created by this project can inform future research studies and wind load provisions to improve the resilience of the U.S. to hurricane impacts, and thus better secure the nation's welfare and prosperity after windstorm events. This research will make knowledge advancements on multiple fronts. It will investigate topographic wind effects (i.e., speed-up) on Puerto Rico, with the goal of advancing understanding of how geomorphic complexity (topography) enhances surface winds and makes civil infrastructure more vulnerable to damage. Specifically, the research will explore and assess the predictive capability of machine learning and multi-scale atmospheric simulation, i.e., computational fluid dynamics nested within a numerical weather prediction (NWP) framework. To support this effort, high-resolution stereoscopic velocity fields over geometrically scaled models of Puerto Rico and the municipal Islands of Vieques and Culebra will be collected from a precision-guided particle image velocimetry system in the Terraformer BLWT. Experiments will be designed to yield critical insights for improving BLWT modeling, while producing foundational datasets to assess the efficacy of (a) supervised regression-based machine learning at predicting how changes in the upwind elevation modify flows and (b) supervised classification-based machine learning methods for determining where "special" wind regions should apply in structural wind load provisioning. Concurrently, NWP enhanced with large eddy simulation (LES) will be applied to demonstrate that NWP-LES can improve the hindcasting of a hurricane's wind field in the built environment. If successful, this effort can critically aid the engineering and atmospheric science fields in reaching consensus on standardizing approaches to predict the behavior of surface winds during an extreme wind event.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.

由于飓风玛丽亚在2017年9月登陆对波多黎各造成了广泛的破坏，这项早期概念的探索性研究补助金（EAGER）将研究复杂的地形如何加速风力，并最终加剧对建筑物和其他民用基础设施的破坏。 这项研究将利用机器学习和天气预报的最新进展来预测山区地形和其他复杂陆地环境中的风速。该项目将利用佛罗里达大学的自然灾害工程研究基础设施（NHERI）地形形成器边界层风洞（BLWT）来描述波多黎各和别克斯岛和库莱布拉自治岛的几何比例模型上的表面风场。这个EAGER是佛罗里达大学（为美国最容易发生飓风的州提供服务）和位于马亚圭斯的波多黎各大学（一所为西班牙裔学生服务的机构，仍在从飓风玛丽亚中恢复过来），这两个机构的研究生和本科生将积极参与实验和计算工作。 预期的项目成果将包括关于地形对破坏性风行为的影响的重要新见解，将实验与先进计算方法融合在一起的新科学工具，以研究极端风对建筑民用基础设施的影响，以及将提供给NHERI数据库（https：//www.example.com）中其他研究人员的基准数据集。www.DesignSafe-ci.org 该项目所创造的知识可以为未来的研究和风荷载规定提供信息，以提高美国对飓风影响的抵御能力，从而更好地确保国家在风暴事件后的福利和繁荣。 这项研究将在多个方面推动知识进步。 它将调查地形风的影响（即，在波多黎各，我们将进行一次加速试验，目的是加深对地貌复杂性（地形）如何增强地表风并使民用基础设施更容易受到破坏的理解。 具体而言，该研究将探索和评估机器学习和多尺度大气模拟的预测能力，即，嵌套在数值天气预报（NWP）框架内的计算流体动力学。为了支持这一努力，将从Terraformer BLWT中的精确制导粒子图像测速系统收集波多黎各和别克斯岛及库莱布拉自治岛的几何比例模型上的高分辨率立体速度场。实验将被设计为产生关键的见解，以改善BLWT建模，同时产生基础数据集，以评估（a）基于监督回归的机器学习在预测逆风海拔变化如何修改流量和（B）基于监督分类的机器学习方法的有效性，以确定“特殊”风区应适用于结构风荷载供应。 与此同时，数值预报增强大涡模拟（LES）将被应用于证明，NWP-LES可以改善后报的飓风的风场在建筑环境。如果成功的话，这一努力将极大地帮助工程和大气科学领域就极端风事件期间地面风行为预测的标准化方法达成共识。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。

项目成果

Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks

• DOI: 10.3389/fbuil.2022.762054
• 发表时间: 2022-05
• 作者: J. Santiago-Hernandez;A. R. Santiago;R. A. Catarelli;B. M. Phillips;L. D. Aponte-Bermúdez;F. Masters;Yanlin Guo;Guowei Qian