EAGER: Establishing a Numerical Framework for Multi-scale Simulations of Wildfire Spread and Smoke Dispersion

EAGER：建立野火蔓延和烟雾扩散多尺度模拟的数值框架

• 批准号: 1836505
• 负责人: Stefano Leonardi
• 金额: $ 9.01万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836505&HistoricalAwards=false
• 关键词: EAGER; Establishing; Numerical; Framework; Multi

Many communities across the country live under the threat of wildland fires that can cause devastating safety, health and financial risks. Computational models of wildfire spread have become a critical tool for the development of prevention plans and containment strategies. Current models provide general forecast of the evolution of fire perimeters. However, these models often have limitations in predicting extreme fire events due to the interaction of weather, topography and turbulence. The proposed work aims at overcoming these limitations by developing a simulation framework with an unprecedented level of resolution and accuracy. The goal is to provide a comprehensive and detailed description of wildfire evolution. Use of improved computer models will help mitigate the risk and harm of wildland fires, thus enhancing the national welfare and security.Wind and terrain slope are major factors that affect wildfire and smoke spread. These interactions occur over a wide range of spatial and temporal scales. To account for the complexity of the phenomena, in this project a numerical weather prediction (NWP) model will be coupled with a computational code based on large-eddy simulation (LES). The code scales efficiently for parallel computation. The topography will be included in the numerical simulations with the efficient immersed boundary method. The evolution of the fire perimeter will be computed by a fire-spread simulator and tracked with the level-set method. The proposed approach will permit the simulation of wildfire evolution under realistic wind patterns, which account for the interaction with the burning fuel, large-scale atmospheric circulation, and local terrain features. This project will be focused on the influence of topography and small-scale flow features on the evolution of wildland fires. It will also address how to downscale the atmospheric circulation from NWP to the high resolution LES domain. This is still an open problem in atmospheric science, which has great relevance in several other fields. The goals of the project include evaluating the optimal setup for simulation and assess the feasibility of the numerical framework for real time tracking of fire spread. Additionally, the numerical results will provide a valuable dataset to refine and develop reduced-order models for wildfire spread and smoke dispersion.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.

全国许多社区生活在野火的威胁下，野火可能会造成毁灭性的安全、健康和金融风险。野火蔓延的计算模型已经成为制定预防计划和遏制战略的关键工具。目前的模型提供了对火场周边演变的总体预测。然而，由于天气、地形和湍流的相互作用，这些模型在预测极端火灾事件时往往存在局限性。拟议的工作旨在通过开发具有前所未有的分辨率和准确性的模拟框架来克服这些限制。其目标是提供对野火演变的全面和详细的描述。使用改进的计算机模型将有助于降低野火的风险和危害，从而提高国家的福利和安全。风和地形坡度是影响野火和烟雾传播的主要因素。这些相互作用发生在广泛的空间和时间尺度上。为了解释这种现象的复杂性，在这个项目中，一个数值天气预报(NWP)模式将与一个基于大涡模拟(LES)的计算程序相结合。该代码可以高效地扩展以进行并行计算。地形将被包含在数值模拟中，采用高效的浸没边界方法。火场边界的演变将由火灾蔓延模拟器计算，并用水平集方法进行跟踪。所提出的方法将允许模拟真实风型下的野火演变，该风型考虑了与燃烧的燃料、大尺度大气环流和当地地形特征的相互作用。本项目将重点研究地形和小尺度流动特征对野地火灾演变的影响。它还将讨论如何将大气环流从NWP缩小到高分辨率LES区域。这在大气科学中仍然是一个悬而未决的问题，在其他几个领域也有很大的相关性。该项目的目标包括评估模拟的最佳设置和评估实时跟踪火势蔓延的数值框架的可行性。此外，数值结果将为改进和开发野火蔓延和烟雾扩散的降阶模型提供有价值的数据集。这一奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

用于模拟德克萨斯州北部风电场的单向中尺度-微尺度耦合：根据 SCADA 和 LiDAR 数据进行评估

• DOI: 10.1002/we.2452
• 发表时间: 2020
• 期刊: Wind Energy
• 影响因子: 4.1
• 作者: Santoni, Christian;García‐Cartagena, Edgardo J.;Ciri, Umberto;Zhan, Lu;Valerio Iungo, Giacomo;Leonardi, Stefano
• 通讯作者: Leonardi, Stefano