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: 2345255
• 负责人: Alice Koniges
• 金额: $ 7.46万
• 依托单位: University of Hawaii
• 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=2345255&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.

对野火蔓延的准确短期预测对于告知人们、通过有效的扑灭活动最大限度地减少生命损失以及减轻野火造成的损失和成本至关重要。拉海纳大火造成的破坏后，改进这些流程至关重要。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至关重要。该项目将培养博士。计算科学和建模专业的学生。该项目将涉及毛伊岛“艾娜数据管理员”计划的高中生和社区学院学生。该项目将开发野火模型，该模型有可能在未来的野火中拯救人类生命和基础设施，使用水平集方法和汉密尔顿-雅可比方程来模拟住宅区野火活动期间和之后与人类活动相关的野火蔓延。虽然水平集方法在野火建模中相对众所周知，并与实时分析的数据同化方法相结合，但仍需要了解它们与人类活动的相互作用，特别是因为它与野火事件发生后立即疏散和财产保护有关。这项研究的产品将是一个新模型，旨在帮助人们了解野火响应的复杂算法和数学基础，从而有助于在拉海纳火灾等几乎实时的灾难情况下进行资源分配。该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 NSF 的法定使命，并通过评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。