Collaborative Research: Planning: FIRE-PLAN:High-Spatiotemporal-Resolution Sensing and Digital Twin to Advance Wildland Fire Science

合作研究：规划：FIRE-PLAN：高时空分辨率传感和数字孪生，以推进荒地火灾科学

• 批准号: 2335568
• 负责人: Haiyang Chao
• 金额: $ 9.6万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335568&HistoricalAwards=false
• 关键词: Collaborative; Research; Planning; FIRE; PLAN

The number of catastrophic wildfires in the United States has been steadily increasing in recent decades, which generate casualties, large loss of properties, and dramatic environmental changes. However, it is difficult to make accurate predictions of wildland fire spread in real time for firefighters and emergency response teams. Although many fire spread models have been developed, one of the biggest challenges in their operational use is the lack of ground truth fire data at high spatiotemporal resolutions, which are indispensable for model evaluation and improvements. The objective of this planning project is to bring together wildland fire science researchers, fire sensing and data science experts, and diverse stakeholders to develop standards and requirements for high-spatiotemporal-resolution wildland fire sensing and digital twin construction. An organizing committee will be formed from wildland fire science, engineering, and stake holder communities including fire ecology and behavior modeling, pollution monitoring, robotics, cyber physical systems (CPS), wildfire fighting, indigenous cultural burns, and prescribed fires. A series of physical and remote workshops will be held focusing on themes such as open fire data for wildland fire modeling validation, digital twins for prescribed fires, and safe and efficient wildland fire data collection. Research tasks of this planning project include: 1) identification of key high-spatiotemporal-resolution fire metrics and data representations to support fire model validation and fire operations, 2) proposition of sensing strategies and algorithms for fire sensing and suppression robots and cyber physical systems that can support safe and efficient collection of desired high-resolution fire data, 3) development and evaluation of data assimilation and digital twin construction using high-resolution data to advance fire behavior modeling, coupled fire-atmosphere modeling, and smoke modeling, and 4) prototype and initial fire data ecosystem demonstration including collection of cultural burn data and establishment of GeoFireData, a benchmark fire data sharing and digital twin website, which can support different fire operation types such as fire spread model validation and controlled burn planning. The special attention will be devoted to interdisciplinary training of the next generation of scientists working with wildfire risks at the interface of computational sciences, engineering, ecology, and data sciences.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.

近几十年来，美国的灾难性野火数量一直在稳步增加，这会导致人员伤亡，巨大的财产丧失和急剧的环境变化。但是，对于消防员和应急响应小组，很难实时对野外火的准确预测。尽管已经开发了许多火灾传播模型，但其操作用途中最大的挑战之一是缺乏高时空分辨率的地面真相消防数据，这对于模型评估和改进是必不可少的。该计划项目的目的是将荒野消防科学研究人员，消防传感和数据科学专家以及各种利益相关者汇集在一起​​，以制定高级分辨率 - 分辨率野生世界消防和数字双胞胎构建的标准和要求。一个组织委员会将由野国消防科学，工程和利益持有人社区组成，包括消防生态和行为建模，污染监测，机器人技术，网络物理系统（CPS），野火战斗，土著文化燃烧和规定的大火。将举办一系列的物理和远程研讨会，重点关注主题，例如用于野外火灾建模验证的开火数据，用于处方大火的数字双胞胎以及安全有效的野生世界火灾数据收集。 该计划项目的研究任务包括：1）确定关键的高型高分辨率火灾指标和数据表示，以支持火灾模型验证和火灾操作，2）感应策略和火灾感应机器人以及抑制机器人的命令以及可以使用所需的高分辨率数据进行数据构造，3）构建数据，3）建模，耦合大气建模和烟雾建模，以及4）原型和初始火灾数据生态系统演示，包括收集文化燃烧数据和Geofiredata的收集，Geofiredata，基准的火灾数据共享和数字双胞胎网站，这些网站可以支持不同的火灾操作类型，例如消防差异范围验证模型验证和受控烧伤计划。特别注意的是，在计算科学，工程，生态学和数据科学的界面上，下一代科学家的下一代科学家的跨学科培训。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。