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: 0540136
• 负责人: Yalchin Efendiev
• 金额: --
• 依托单位: Texas A&M 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 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540136&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.

该项目旨在开发一个可变的光波传感器阵列，我们将集成到海洋观察系统中。该系统将优于大多数近海海洋模型，这些模型通常是风向驱动的，但不是由污染运输驱动的，因为我们的新型号都是两者兼而有之。这些目标将通过动态注入观察到的海洋数据进行多尺度数学模型和计算机模拟来实现。该项目将通过灵活，基于网格的数据库和解决问题的环境创建多尺度数学，统计和软件应用程序集成的研究主题。该项目将遵循一种集成方法，该方法在过程的每个步骤中解决了技术问题：1）动态仿真指示传感器要查找内容并为这些分析物进行重新编程，2）传感器向模拟新观察到的数据报告，以及3）模拟然后将新数据进行了更新的预测，并将其更新并在封闭的库中重新编程。我们将减少监视溢出物和其他污染事件所需的人类干预量，使DDDA可行，可用于实时与传感器很难与传感器进行通信的传感器（例如，将来是不可靠的卫星链接或另一个行星体）。这项工作将建立在先前由NSF资助的研究结果的基础上，包括Sura沿海海洋观察和预测，以及两个ITR项目，以开发算法，错误控制和Middleware，以最佳地管理可透露式可扩展的计算资源用于电网计算。 该项目的最终目标是指导硬件和软件的开发以使实验室和海洋测试同时执行，但是这些任务将被降级以进行后续工作。学术和工业伙伴都将参与目前的努力。该项目的研究将扩展到其他应用程序环境。