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: 2345256
• 负责人: Andrea Bertozzi
• 金额: $ 10万
• 依托单位: University of California-Los Angeles
• 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=2345256&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.

准确的短期预测野火差异对于告知人们，通过有效的抑制活动来最大程度地减少生命损失，减轻野火的损失和成本至关重要。在Lahaina火灾破坏后，改善这些过程至关重要。在忘记细节之前，必须确定影响野火差异的所有因素，并确定如何将它们纳入快速预测过程。该项目将培训博士学位。计算科学和建模的学生。该项目将涉及来自毛伊岛的AINA数据管家计划的高中和社区大学生。该项目将开发野火模型，这些模型有可能使用级别的方法和汉密尔顿 - 雅各比方程来挽救未来野火中的人类生命和基础设施，以模拟住宅区中野火活动期间和之后的人类活动的野火扩散。虽然级别的方法对于与数据同化方法进行实时分析相对众所周知，但需要了解它们与人类活动的相互作用，尤其是在野火事件后立即与疏散和保护财产有关的相互作用。这项研究的产物将是一种新模型，以了解野火响应的复杂算法和数学基础的理解，可以在几乎实时的实时灾难情况下（例如Lahaina Firestorm）进行资源分配。该项目将展示如何立即部署该模型以避免瓶颈导致悲剧以及在消防管理技术中实施范式移动解决方案所需的必要技术进步。该奖项反映了NSF的法定任务，并认为通过使用该基金会的知识分子和更广泛的影响来评估Criteria的评估，并被认为是值得通过评估的支持。