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

气候变化导致的气温升高、干旱持续时间和强度增加，以及野生动物与城市交界面的扩大，大大增加了森林火灾的频率和强度，对生命、财产和环境造成了毁灭性的影响。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，该网络可以帮助进行初步的野火定位和地图绘制，对火灾进展进行近期预测，并为地面消防人员提供通信支持。该系统与之前的工作有两个关键的区别：(1)它利用并随后更新详细的三维环境模型，包括燃料类型和湿度状态、地形和大气/风条件的影响，以便提供最及时和准确的火灾行为预测；(2)它适应危险和快速变化的条件，最佳地平衡广域覆盖的需求，并保持与偏远地区人员的通信联系。在该项目下开发的科学和工程可以适用于野火以外的许多应用，包括城市和郊区的结构火灾，涉及辐射或空气化学泄漏的自然或人为紧急情况，释放化学或生物制剂的“脏弹”，或跟踪即将发生或正在发生的极端天气事件周围的高度本地化的大气条件。根据该项目开发的系统将能够在野火的早期阶段更快速地定位和态势感知，更好地预测当地、近期和事件规模的行为，更好地态势感知和人员和资源的协调，并提高地面消防员的安全性。从基于风速的简单代数关系到更复杂的随时间耦合流体动力学-火灾物理模型，各种模型都将用于预测火灾行为。这些模型受到随机过程的阻碍，比如燃烧的余烬点燃新的火焰，导致误差随着时间的推移而迅速增长。该项目的重点是利用机载无人机和地面传感器（GBS）提供的传感器数据闭合环路。模型通过预测问题现象的快速增长来通知感知，随后的感知更新模型，提供当地的风和火点位置。尽快关闭这个循环对于减轻火灾的影响至关重要。我们提出的系统集成了先进的火灾建模工具、移动无人机、无线GBS和高级人类互动，用于野火事件的初始攻击和随后的持续支持。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。

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.