RAPID: Enhancing WUI Fire Assessment through Comprehensive Data and High-Fidelity Simulation

RAPID：通过综合数据和高保真模拟增强 WUI 火灾评估

• 批准号: 2401876
• 负责人: Shuna Ni
• 金额: $ 19.66万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401876&HistoricalAwards=false
• 关键词: RAPID; Enhancing; WUI; Fire; Assessment

The Maui wildfire has claimed 97 lives and decimated the historic Lahaina town, with thousands of acres burned and over 2,200 structures damaged or destroyed. However, some structures on the fire path remained unscathed. Current models designed for wildfire spread in wildland-urban-interface (WUI) communities predominantly function at community or larger scales. They fall short in capturing the observations from the Lahaina wildfires, such as specific buildings remaining undamaged amidst extensively destroyed structures. Notably, there exists a group of tools that, in principle, have the capability to simulate fire spread on and between individual structures with high fidelity Computational-Fluid-Dynamics-based (CFD-based) fire models, e.g., the Fire Dynamics Simulator (FDS) developed by NIST and FireFoam developed by FM Global. These tools can potentially be used to both analyze and predict fire spread inside WUI communities and provide deep insights into the resilience of particular structures. However, to date, their application to model fire spread on and between structures in a wildfire has been limited primarily due to the lack of data necessary to correctly set up and validate these high-fidelity models. This project aims to overcome these challenges, enabling more accurate modeling of structure burning and fire spread in WUI settings in the future. This project will also help in training a new generation of researchers in wildfire and WUI fire resilience. The goal of this project is to enhance the WUI fire assessment through compiling a comprehensive dataset that accurately documents how the wildfire spread and impacted the community of Lahaina. It will also assess the feasibility of using high-fidelity CFD-based models to simulate the burning of individual structures in a WUI fire scenario. The comprehensive dataset, pulling from diverse data sources and formats, will be systematically organized, offering a wealth of detailed information in an easily understandable manner. This dataset is crucial for refining WUI fire spread models across all scales, from community-wide fire spread to individual structure response. After the dataset is compiled, high-fidelity CFD models will be set up for two Lahaina structures, one damaged by fire and one undamaged despite being in the path of fire. The aim is to determine if these models can accurately simulate the damage that was observed. By doing so, the project can identify areas where our current understanding and modeling approaches may be lacking or incomplete, guiding the future development of modeling techniques. The dataset and the model feasibility study ultimately will enhance WUI fire risk assessment, driving more informed decision-making in wildfire mitigation strategies. Additionally, both the dataset and the model feasibility study are valuable for broader WUI fire research and practice, including the performance-based design of structures against wildfires using high-fidelity CFD-based models.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.

毛伊岛野火已造成 97 人死亡，并摧毁了历史悠久的拉海纳小镇，数千英亩土地被烧毁，2,200 多座建筑物受损或毁坏。然而，火灾路径上的一些建筑物仍然毫发无伤。当前针对荒地-城市界面（WUI）社区野火蔓延而设计的模型主要在社区或更大规模上发挥作用。他们未能捕捉到拉海纳野火的观测结果，例如在大面积毁坏的建筑中，特定建筑物仍未受损。值得注意的是，存在一组工具，原则上能够通过高保真基于计算流体动力学（基于 CFD）的火灾模型来模拟各个结构上和各个结构之间的火灾蔓延，例如火灾动力学模拟器（ FDS）由 NIST 开发，FireFoam 由 FM Global 开发。这些工具可用于分析和预测 WUI 社区内的火灾蔓延，并提供对特定结构的恢复能力的深入见解。然而，迄今为止，它们在野火中建筑物上和建筑物之间的火蔓延建模的应用受到限制，主要是由于缺乏正确设置和验证这些高保真模型所需的数据。该项目旨在克服这些挑战，从而在未来对 WUI 设置中的结构燃烧和火灾蔓延进行更准确的建模。该项目还将有助于培训新一代野火和 WUI 火灾抵御能力的研究人员。该项目的目标是通过编制全面的数据集来加强 WUI 火灾评估，该数据集准确记录野火如何蔓延和对拉海纳社区的影响。它还将评估使用基于 CFD 的高保真模型来模拟 WUI 火灾场景中单个结构燃烧的可行性。来自不同数据源和格式的综合数据集将被系统地组织，以易于理解的方式提供大量详细信息。该数据集对于完善从社区范围的火灾蔓延到单个结构响应的所有规模的 WUI 火灾蔓延模型至关重要。数据集编译完成后，将为两座拉海纳结构建立高保真 CFD 模型，其中一处因火灾而受损，另一处尽管位于火灾路径中却未受损。目的是确定这些模型是否能够准确模拟观察到的损坏。通过这样做，该项目可以确定我们当前的理解和建模方法可能缺乏或不完整的领域，从而指导建模技术的未来发展。该数据集和模型可行性研究最终将加强 WUI 火灾风险评估，推动野火缓解策略做出更明智的决策。此外，数据集和模型可行性研究对于更广泛的 WUI 火灾研究和实践都很有价值，包括使用基于 CFD 的高保真模型进行基于性能的野火结构设计。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。