EAGER: Understanding complex wind-driven wildfire propagation patterns with a dynamical systems approach

EAGER：通过动力系统方法了解复杂的风驱动野火传播模式

• 批准号: 2330212
• 负责人: Amirhossein Arzani
• 金额: $ 10万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330212&HistoricalAwards=false
• 关键词: EAGER; Understanding; complex; wind; driven

Intense and long wildfire seasons have unfortunately become a normal routine in certain parts of the US. Dangerous wildfires are often driven by intense winds. The chaotic nature of wind patterns makes prediction and fundamental understanding of wildfire growth a challenging task. Current wind-driven wildfire models are difficult to simulate and interpret. In this exploratory project, the key objective is to uncover hidden coherent patterns in wind data that could be used to better understand and predict wildfire growth driven by wind. The project will benefit society as it provides guidelines for wildfire management, which will save lives and reduce the financial burden on communities exposed to wildfires. In this study, dynamical systems theory will be employed to define coherent structures customized to the transport problems used to model wind-driven wildfire growth. A set of benchmark problems motivated by the field of dynamical systems and chaotic advection together with more complex realistic wind patterns will be leveraged to study the role of coherent structures in wildfire growth. Specifically, the hypothesis that generlized Lagrangian coherent structures could be defined to provide a template for wildfire growth under certain scenarios will be explored. Outreach activities in Utah will use visualization to demonstrate the importance of computer modeling in managing wildfires. This study will provide a new theory that not only simplifies our understanding of wildfire growth under complex wind patterns but also guides wildfire management and mitigation.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.

不幸的是，在美国某些地区，激烈而漫长的野火季节已成为常态。危险的野火往往是由强风驱动的。风模式的混沌性质使得预测和基本理解野火增长成为一项具有挑战性的任务。目前的风力驱动的野火模型很难模拟和解释。在这个探索性项目中，主要目标是发现风数据中隐藏的连贯模式，这些模式可用于更好地理解和预测风驱动的野火增长。该项目将造福社会，因为它为野火管理提供了指导方针，这将拯救生命，减轻遭受野火的社区的经济负担。在这项研究中，动力系统理论将被用来定义定制的传输问题，用于模拟风驱动的野火增长的相干结构。一组基准问题的动力系统和混沌平流领域的动机，再加上更复杂的现实风模式将被利用来研究野火增长的相干结构的作用。具体而言，广义拉格朗日相干结构可以定义为野火增长在某些情况下提供一个模板的假设将进行探讨。犹他州的外展活动将使用可视化来展示计算机建模在管理野火中的重要性。这项研究将提供一个新的理论，不仅简化了我们对复杂风模式下野火增长的理解，而且还指导野火管理和缓解。该奖项反映了NSF的法定使命，并被认为值得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来支持。