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

University Of Florida, Gainesville FL

Investigators

Abstract

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.