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

毛伊岛的野火夺走了97条生命，摧毁了历史悠久的拉海纳镇，数千英亩土地被烧毁，2200多座建筑遭到破坏或摧毁。然而，火灾路径上的一些建筑物仍然完好无损。目前设计用于野火在荒地-城市界面（WUI）社区传播的模型主要在社区或更大的尺度上运行。它们未能捕捉到从拉海纳野火中观察到的情况，例如在被广泛破坏的建筑物中仍未受损的特定建筑物。值得注意的是，有一组工具，原则上有能力用高保真的基于计算流体动力学（cfd）的火灾模型来模拟单个结构上和结构之间的火灾蔓延，例如，NIST开发的火灾动力学模拟器（FDS）和FM Global开发的FireFoam。这些工具可以潜在地用于分析和预测WUI社区内的火灾蔓延，并为特定结构的弹性提供深入的见解。然而，到目前为止，由于缺乏正确建立和验证这些高保真模型所需的数据，它们在野火中对建筑物上和建筑物之间的火灾蔓延进行模拟的应用受到限制。该项目旨在克服这些挑战，使未来在WUI设置中更准确地模拟结构燃烧和火灾蔓延。该项目还将有助于培训野火和WUI火灾恢复能力方面的新一代研究人员。该项目的目标是通过编制一个全面的数据集，准确记录野火如何蔓延和影响拉海纳社区，从而加强WUI火灾评估。它还将评估使用高保真cfd模型模拟WUI火灾场景中单个建筑物燃烧的可行性。综合数据集，从不同的数据源和格式提取，将被系统地组织，以易于理解的方式提供丰富的详细信息。该数据集对于从社区范围的火灾蔓延到个体结构响应的所有尺度上的WUI火灾蔓延模型的改进至关重要。数据集编制完成后，将建立两个Lahaina结构的高保真CFD模型，一个被火灾损坏，一个在火灾路径上未被损坏。目的是确定这些模型是否能准确地模拟所观察到的损伤。通过这样做，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。数据集和模型可行性研究最终将增强WUI火灾风险评估，推动更明智的野火减灾战略决策。此外，数据集和模型可行性研究对更广泛的WUI火灾研究和实践都有价值，包括使用高保真cfd模型进行基于性能的野火结构设计。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。

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.