Collaborative Research:CPS:Medium:SMAC-FIRE: Closed-Loop Sensing, Modeling and Communications for WildFIRE

合作研究：CPS：中：SMAC-FIRE：野火的闭环传感、建模和通信

基本信息

批准号：
2209695
负责人：
Arnold Swindlehurst
金额：
$ 104.91万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209695&HistoricalAwards=false
关键词：
Collaborative Research CPS Medium SMAC

项目摘要

Increases in temperatures and drought duration and intensity due to climate change, together with the expansion of wildlife-urban interfaces, has dramatically increased the frequency and intensity of forest fires, and has had devastating effects on lives, property, and the environment. To address this challenge, this project’s goal is to design a network of airborne drones and wireless sensors that can aid in initial wildfire localization and mapping, near-term prediction of fire progression, and providing communications support for firefighting personnel on the ground. Two key aspects differentiate the system from prior work: (1) It leverages and subsequently updates detailed three-dimensional models of the environment, including the effects of fuel type and moisture state, terrain, and atmospheric/wind conditions, in order to provide the most timely and accurate predictions of fire behavior possible, and (2) It adapts to hazardous and rapidly changing conditions, optimally balancing the need for wide-area coverage and maintaining communication links with personnel in remote locations. The science and engineering developed under this project can be adapted to many applications beyond wildfires including structural fires in urban and suburban settings, natural or man-made emergencies involving radiation or airborne chemical leaks, "dirty bombs" that release chemical or biological agents, or tracking highly localized atmospheric conditions surrounding imminent or on-going extreme weather events.The system developed under this project will enable more rapid localization and situational awareness of wildfires at their earliest stages, better predictions of both local, near-term and event-scale behavior, better situational awareness and coordination of personnel and resources, and increased safety for fire fighters on the ground. Models ranging from simple algebraic relationships based on wind velocity to more complex time-dependent coupled fluid dynamics-fire physics models will be used to anticipate fire behavior. These models are hampered by stochastic processes such as the lofting of burning embers to ignite new fires, that cause errors to grow rapidly with time. This project is focused on closing the loop using sensor data provided by airborne drones and ground-based sensors (GBS). The models inform the sensing by anticipating rapid growth of problematic phenomena, and the subsequent sensing updates the models, providing local wind and spot fire locations. Closing this loop as quickly as possible is critical to mitigating the fire’s impact. The system we propose integrates advanced fire modeling tools with mobile drones, wireless GBS, and high-level human interaction for both the initial attack of a wildfire event and subsequent on-going support.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.

气候变化导致气温升高、干旱持续时间和强度增加，加上野生动物与城市接触面的扩大，大大增加了森林火灾的频率和强度，并对生命、财产和环境造成了破坏性影响。为了应对这一挑战，该项目的目标是设计一个由机载无人机和无线传感器组成的网络，帮助进行野火的初步定位和测绘、火灾进展的近期预测，并为地面消防人员提供通信支持，这两个关键方面有所不同。之前工作的系统： (1) 它利用并随后更新详细的环境三维模型，包括燃料类型和湿度状态、地形以及大气/风力条件的影响，以便尽可能提供最及时、最准确的火灾行为预测， (2) 它能够适应危险和快速变化的条件，最佳地平衡广域覆盖的需求和与偏远地区人员保持通信联系。该项目开发的科学和工程可以适应野火以外的许多应用，包括结构性火灾。城市和郊区的火灾，自然或涉及辐射或空气中的化学化学品、释放化学或生物剂的“脏弹”或围绕即将发生或正在发生的极端天气事件的高度局部化的大气条件的人为紧急情况。根据该项目开发的系统将能够更快速地定位和泄漏对野火最早阶段的态势感知，更好地预测当地、近期和事件规模的行为，更好的态势感知以及人员和资源的协调，以及提高地面消防员的安全性。基于风速的更复杂的与时间相关的耦合流体动力学火灾物理模型将用于预测火灾行为，这些模型受到随机过程的阻碍，例如燃烧余烬的升起以点燃新的火灾，从而导致误差迅速增长。随着时间的推移，该项目的重点是使用机载无人机和地面传感器 (GBS) 提供的传感器数据来闭合循环，这些模型通过预测问题现象的快速增长来通知传感，随后的传感更新模型，提供本地信息。风和点尽快关闭这个循环对于减轻火灾的影响至关重要，我们提出的系统将先进的火灾建模工具与移动无人机、无线 GBS 和高级人机交互相结合，用于野火事件的初始攻击和控制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Cell-Free UAV Networks with Wireless Fronthaul: Analysis and Optimization

具有无线前传的无蜂窝无人机网络：分析和优化

DOI：
发表时间：
2023-07
期刊：
IEEE transactions on wireless communications
影响因子：
10.4
作者：
Diaz;Lozano, A.;Jafarkhani H.
通讯作者：
Jafarkhani H.

Historical spatiotemporal changes in fire danger potential across biomes

生物群落潜在火灾危险的历史时空变化

DOI：
10.1016/j.scitotenv.2023.161954
发表时间：
2023-04
期刊：
Science of The Total Environment
影响因子：
9.8
作者：
Baijnath;Le, Phong V.V.;Foufoula;Banerjee, Tirtha
通讯作者：
Banerjee, Tirtha

Quantifying the effectiveness of shaded fuel breaks from ground-based, aerial, and spaceborne observations

根据地面、空中和星载观测量化阴影燃料中断的有效性

DOI：
10.1016/j.foreco.2023.121142
发表时间：
2023-09
期刊：
Forest Ecology and Management
影响因子：
3.7
作者：
Baijnath;Martinez, Alexandre;York, Robert A.;Foufoula;AghaKouchak, Amir;Banerjee, Tirtha
通讯作者：
Banerjee, Tirtha

Features of turbulence during wildland fires in forested and grassland environments

森林和草原环境野火期间的湍流特征

DOI：
10.1016/j.agrformet.2023.109501
发表时间：
2023-07
期刊：
Agricultural and Forest Meteorology
影响因子：
6.2
作者：
Desai, Ajinkya;Heilman, Warren E.;Skowronski, Nicholas S.;Clark, Kenneth L.;Gallagher, Michael R.;Clements, Craig B.;Banerjee, Tirtha
通讯作者：
Banerjee, Tirtha

Revisiting “bursts” in wall-bounded turbulent flows

重温壁面湍流中的“爆发”