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CPS: Medium: Data-Driven Adaptive Real-Time (DART) Flow-Field Estimation Using Deployable UAVs

CPS：中：使用可部署无人机进行数据驱动的自适应实时 (DART) 流场估计

基本信息

批准号：
1932105
负责人：
Jesse Hoagg
金额：
$ 119.92万
依托单位：
University of Kentucky Research Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932105&HistoricalAwards=false
关键词：
CPS Medium Data Driven Adaptive

项目摘要

The Fukushima Daiichi nuclear disaster and the Aliso Canyon natural gas leak are recent high-profile examples of emergency situations that resulted from the unplanned release of an airborne contaminant. In such emergency scenarios, accurate real-time prediction of contaminant movement is invaluable for planning emergency response, protecting emergency workers, and assessing environmental impact. However, accurate prediction of contaminant dispersion is challenging because of atmospheric turbulence, ground terrain topology, and changing wind conditions. This project addresses the problem of predicting atmospheric contaminant dispersion in real time by using a fleet of autonomous unmanned air vehicles (UAVs) to obtain sparse physical measurements of the atmospheric flow and contaminant concentrations. Then, these sparse physical measurements are used in real time to continually improve a computational fluid dynamic model in order to produce an accurate real-time prediction of the contaminant dispersion. This represents a tight integration of real-time sensing of airborne contamination with multi-vehicle swarm control and cloud dispersion prediction to generate optimal vehicle paths. The primary aim of this project is to develop and demonstrate a new data-driven adaptive real-time (DART) system that produces accurate real-time micro-meteorological estimates and forecasts contaminant dispersion near its source. The DART system will consist of a computational-fluid-dynamic cyber system and a physical system of autonomous UAVs instrumented with flow sensors and contaminant-concentration sensors. Together, this DART system will produce accurate flow-field estimates, which can be used to predict contaminant dispersion. Developing the DART system requires new techniques for real-time data-driven model adaption, advances in computational turbulence modeling, improvements in UAV-based sensing and data processing, and new UAV formation flying methods that use cyber-feedback from the computational-fluid-dynamic cyber system. The project includes multiple levels of experimentation including simulation, wind tunnel, and live flight demonstration to provide proof of concept. This project is jointly funded by the Cyber Physical System Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

福岛第一核电站核灾难和Aliso峡谷天然气泄漏是最近因空气污染物的意外释放而导致的紧急情况的引人注目的例子。在这种紧急情况下，准确的实时预测污染物的移动是非常宝贵的规划应急响应，保护应急工作人员，并评估环境影响。然而，由于大气湍流、地面地形拓扑和不断变化的风况，污染物扩散的准确预测具有挑战性。该项目解决的问题，预测大气污染物的扩散在真实的时间，通过使用一队的自主无人驾驶飞行器（UAV），以获得稀疏的物理测量的大气流量和污染物浓度。然后，这些稀疏的物理测量值被用于真实的时间中，以连续地改进计算流体动力学模型，以便产生污染物扩散的准确的实时预测。这代表了一个紧密的集成，实时传感的空气污染与多车辆群控制和云分散预测，以产生最佳的车辆路径。该项目的主要目的是开发和演示一个新的数据驱动的自适应实时（DART）系统，该系统可以产生准确的实时微气象估计并预测污染物在其源附近的扩散。DART系统将由一个计算流体动力学网络系统和一个装有流量传感器和污染物浓度传感器的自主无人机物理系统组成。总之，该DART系统将产生准确的流场估计，可用于预测污染物扩散。发展DART系统需要新的实时数据驱动模型适应技术、计算湍流建模技术的进步、基于无人机的传感和数据处理技术的改进，以及使用来自计算流体动力学赛博系统的赛博反馈的新的无人机编队飞行方法。该项目包括多个级别的实验，包括模拟，风洞和现场飞行演示，以提供概念验证。该项目由网络物理系统计划和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF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。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Data-Driven Approach For Real-Time Estimation of Material Uncertainty

实时估计材料不确定性的数据驱动方法

DOI：
10.2514/6.2022-3728
发表时间：
2022
期刊：
Proceedings AIAA AVIATION Forum
影响因子：
0
作者：
Fu, Rui;Sinha, Sujit;Barrow, Christopher;Maddox, John F.;Hoagg, Jesse B.;Martin, Alexandre
通讯作者：
Martin, Alexandre

Utilizing a retrospective cost adaptation control (RCAC) algorithm to achieve data-driven, adaptive, real-time (DART) precision meteorological forecasts

利用回顾性成本适应控制（RCAC）算法实现数据驱动、自适应、实时（DART）精准气象预报

DOI：
10.2514/6.2024-1082
发表时间：
2024
期刊：
American Institute of Aeronautics and Astronautics
影响因子：
0
作者：
Sinha, Sujit;Fu, Rui;Bailey, Sean;Hoagg, Jesse B.;Martin, Alexandre
通讯作者：
Martin, Alexandre

Shallow Katabatic Flow in a Complex Valley: An Observational Case Study Leveraging Uncrewed Aircraft Systems

复杂山谷中的浅层下降流：利用无人驾驶飞机系统的观测案例研究

DOI：
10.1007/s10546-022-00783-w
发表时间：
2023
期刊：
Boundary-Layer Meteorology
影响因子：
4.3
作者：
Bailey, Sean C.;Smith, Suzanne Weaver;Sama, Michael P.;Al-Ghussain, Loiy;Boer, Gijs de
通讯作者：
Boer, Gijs de

Formation Control in a Leader-Fixed Frame for Agents with Extended Unicycle Dynamics that Include Orientation Kinematics on SO(m)