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

该项目旨在开发一个可变光波传感器阵列，我们将把它纳入海洋观测系统。该系统将上级大多数近海海洋模型，这些模型通常是风力驱动的，但不是污染物传输驱动的，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，统计学和软件应用集成的研究课题，具有灵活的，基于网格的数据库和解决问题的环境。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，并为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）模拟然后将新数据合并，更新其预测，并在闭环中根据需要重新编程传感器。我们将减少监测泄漏和其他污染事件所需的人工干预量，使DDDAS适用于难以与传感器实时通信的位置（例如，不可靠的卫星链路或未来的另一个行星体）。这项工作将建立在以前由NSF资助的研究的成功成果之上，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，从而最佳地管理可证明可扩展的网格计算资源。 该项目的最终目标是指导硬件和软件的开发，以便进行实验室和海洋测试，但这些任务将被转移到本项目的后续工作中。学术界和工业界的合作伙伴都将参与目前的努力。本课题的研究成果具有一定的推广价值。

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.