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

气候变化导致的气温和干旱持续时间和强度的增加，加上野生动物与城市交界处的扩大，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，以帮助进行初步的野火定位和测绘，对火灾进展进行短期预测，并为地面消防人员提供通信支持。该系统与以前的工作有两个关键方面的区别：(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、地形和大气/风条件的影响，以便尽可能提供最及时和最准确的火灾行为预测；(2)它适应危险和快速变化的条件，最佳地平衡对大范围覆盖的需求，并保持与偏远地点人员的通信联系。在这个项目下开发的科学和工程可以适应野火以外的许多应用，包括城市和郊区的结构性火灾，涉及辐射或空气传播的化学物质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进行的极端天气事件周围的高度局部化的大气条件。在该项目下开发的系统将能够在野火的早期阶段更快地定位和情景感知，更好地预测当地、近期和事件规模的行为，更好的情景感知和人员和资源的协调，以及增加地面消防员的安全。从基于风速的简单代数关系到更复杂的依赖时间的流体动力学-火灾物理耦合模型，将被用于预测火灾行为。这些模型受到随机过程的阻碍，例如燃烧的灰烬放样点燃新的火灾，这些过程导致误差随着时间的推移迅速增长。该项目的重点是使用机载无人机和地面传感器(GBS)提供的传感器数据关闭环路。模型通过预测有问题的现象的快速增长来通知感知，随后的感知更新模型，提供当地的风和现场火灾位置。尽快关闭这个环路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和高级人类交互集成在一起，用于野火事件的初始攻击和后续的持续支持。该奖项反映了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。

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.