DDDAS-TMRP: Collaborative Research: Adaptive Data-Driven Sensor Configuration, Modeling, and Deployment for Oil, Chemical, and Biological Contamination near Coastal Facilities

DDDAS-TMRP：协作研究：沿海设施附近石油、化学和生物污染的自适应数据驱动传感器配置、建模和部署

• 批准号: 0540178
• 负责人: Craig Douglas
• 金额: $ 11.45万
• 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: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540178&HistoricalAwards=false
• 关键词: DDDAS; TMRP; Collaborative; Research; Adaptive

The project is aimed at developing a variable light wave sensor array that we will integrate into an ocean observational system. This system will be superior to most near coastal ocean models, which are typically wind driven but not contamination transport driven, in that our new model will be both. These objectives will be accomplished through the dynamic injection of observed ocean data into multiscale mathematical models and computer simulations. The project will create research topics in multiscale mathematics, statistics, and software application integration with a flexible, Grid-baseddatabase and problem solving environment. The project will follow an integrated approach that addresses technical issues at each step of the process: 1) the dynamic simulation instructs the sensors what to look for and reprograms it for those analytes, 2) the sensors report to the simulation the new observed data, and 3) the simulation then incorporates the new data, updates its predictions, and reprograms the sensors as necessary in a closed loop. We will reduce the amount of human intervention needed to monitor spills and other contamination events, making DDDAS viable for sensors going to locations that are difficult to communicate with the sensors in real-time (e.g., an unreliable satellite link or another planetary body in the future). The work will build on the successful results of research previously funded by the NSF, including the SURA Coastal Ocean Observation and Prediction and two ITR projects to develop algorithms, error controls, andmiddleware to optimally manage provably scalable computing resources for Grid computing. The project has the ultimate objective to guide the development of hardware and software to enable performing both lab and ocean test, but these tasks will be relegated for follow-on efforts to the present project. Both academic and industrial partners will be involved in the present effort. The research in this project will be extendable to other application environments.

该项目旨在开发可变光波传感器阵列，我们将其集成到海洋观测系统中。该系统将优于大多数近海岸海洋模型，这些模型通常由风力驱动，但不是由污染物传输驱动，因为我们的新模型将两者兼而有之。这些目标将通过将观测到的海洋数据动态注入多尺度数学模型和计算机模拟来实现。该项目将创建多尺度数学、统计学和软件应用程序集成与灵活的基于网格的数据库和问题解决环境的研究主题。该项目将遵循一种综合方法，解决过程中每个步骤的技术问题：1）动态模拟指示传感器寻找什么并针对这些分析物重新编程，2）传感器向模拟报告新观察到的数据， 3) 然后，模拟会合并新数据，更新其预测，并根据需要在闭环中对传感器重新编程。我们将减少监测泄漏和其他污染事件所需的人为干预量，使 DDDAS 对于前往难以与传感器实时通信的位置的传感器来说是可行的（例如，不可靠的卫星链路或地球上的另一个行星体）未来）。这项工作将建立在 NSF 先前资助的研究成果的基础上，包括 SURA 沿海海洋观测和预测以及两个 ITR 项目，以开发算法、错误控制和中间件，以最佳方式管理可证明可扩展的网格计算计算资源。 该项目的最终目标是指导硬件和软件的开发，以实现实验室和海洋测试，但这些任务将被转移到当前项目的后续工作中。学术界和工业界合作伙伴都将参与目前的工作。该项目的研究将可扩展到其他应用环境。