Collaborative Research: RAPID: Rapid computational modeling of wildfires and management with emphasis on human activity

合作研究：RAPID：野火和管理的快速计算建模，重点关注人类活动

• 批准号: 2345257
• 负责人: Hannah Kerner
• 金额: $ 2.5万
• 依托单位: Arizona State University
• 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: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345257&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Rapid; computational

Accurate short-term predictions of wildfire spread are essential to inform people, minimize the loss of lives and mitigate damage and cost of wildfire through effective suppression activities. It is critical to improve on these processes in the aftermath of the devastation of the Lahaina Fires. Before details are forgotten, it is critical to identify all the factors influencing wildfire spreads and to determine how to incorporate them into the rapid prediction process. This project will train Ph.D. students in computational science and modeling. This project will involve high school and community college students from the `Aina Data Stewards program on Maui. This project will develop wildfire models that have the potential to save human lives and infrastructure in future wildfires using level-set methods and Hamilton-Jacobi equations to model wildfire spread coupled to human activity during and after wildfire activity in residential zones. While level-set methods are relatively well known for wildfire modeling with coupling to data assimilation methods for real-time analysis, there is need to understand their interaction with human activity especially as it relates to evacuation and protection of property immediately after a wildfire event. A product of this research will be a new model to provide an understanding of the complex algorithmic and mathematical basis for wildfire response that can aid in resource allocation in a virtually real-time disaster situation such as the Lahaina firestorm. This project will show how to immediately deploy this model to avert the bottlenecks leading to tragedy and the required technological advances necessary to implement paradigm-shifting solutions in fire management techniques.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.

准确的野火蔓延短期预测对于通过有效的扑灭活动通知人们、尽量减少生命损失和减轻野火的损害和成本至关重要。在拉海纳大火造成破坏之后，改善这些进程至关重要。在细节被遗忘之前，确定影响野火蔓延的所有因素并确定如何将它们纳入快速预测过程是至关重要的。该项目将培养计算科学和建模方面的博士生。该项目将包括来自毛伊岛“Aina数据管家”项目的高中生和社区大学生。该项目将开发有可能在未来野火中拯救人类生命和基础设施的野火模型，使用水平集方法和汉密尔顿-雅可比方程来模拟居民区野火活动期间和之后与人类活动耦合的野火蔓延。虽然水平集方法在野火建模方面与实时分析的数据同化方法相结合，但需要了解它们与人类活动的相互作用，特别是在野火事件发生后立即疏散和保护财产时。这项研究的成果将是一个新的模型，为野火响应提供复杂的算法和数学基础，有助于在诸如拉海纳火灾风暴等几乎实时的灾害情况下进行资源分配。本项目将展示如何立即部署该模型，以避免导致悲剧的瓶颈，以及在火灾管理技术中实施范式转换解决方案所需的技术进步。该奖项反映了美国国家科学基金会的法定使命，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。