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.

毛伊岛的野火夺走了97人的生命，并摧毁了历史悠久的拉海纳镇，数千英亩的土地被烧毁，并损坏或摧毁了2200多个建筑物。但是，火路上的某些结构毫发无损。目前为野火设计的当前模型在野生世界 - 城市界面（WUI）社区中，主要在社区或更大的尺度上发挥作用。他们在捕获拉海纳野火的观察结果方面缺乏，例如在广泛破坏的结构中仍未受到损害的特定建筑物。值得注意的是，存在一组工具，这些工具原则上有能力模拟基于高富达计算 - 基于基于基于CFD的消防模型在具有高富裕计算 - 基于基于FIL DYNAGITS模拟器（FDS）由NIST和FIREFOAM开发的Fire Dynamilics Simulator（FDS）的单个结构之间的散布。这些工具可以可能用于分析和预测wui社区内部的火灾，并深入了解特定结构的弹性。但是，迄今为止，它们用于建模野火在结构上和之间之间的火灾建模的应用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，从而使未来在WUI环境中更准确地建模结构燃烧和火灾。该项目还将有助于培训新一代的野火研究人员和WUI火灾弹性。该项目的目的是通过编译全面的数据集来增强WUI火灾评估，该数据集准确地记录了野火如何传播和影响Lahaina社区。它还将评估使用高保真性CFD模型在WUI火灾方案中模拟单个结构的燃烧的可行性。从各种数据源和格式中提取的综合数据集将系统地组织起来，以易于理解的方式提供大量详细信息。该数据集对于从社区范围的火灾到个体结构响应来完善WUI火灾范围的模型至关重要。编译数据集后，将为两个Lahaina结构设置高保真CFD模型，其中一个被火灾损坏，尽管处于火灾之路，但一个未损坏。目的是确定这些模型是否可以准确模拟观察到的损害。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开发。数据集和模型可行性研究最终将增强WUI火灾风险评估，以减轻野火缓解策略的更明智的决策。此外，数据集和模型可行性研究对于更广泛的WUI消防研究和实践都是有价值的，包括使用基于CFD的高保真性模型对野火进行基于绩效的结构设计。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来评估CRETERIA的评估。