CSR-CSI: DDDAS-The Pervasive Dynamical Ecosystem for Oceanographic Research

这项建议开发、部署和评估了一个原型的普适动态海洋生态系统，该生态系统集成了传感器、网络、观测站和计算算法，以实现海洋学中的动态数据驱动应用系统研究(DDDAS)，特别是在新泽西海岸的缺氧和缺氧研究中。研究成果还被纳入跨学科研究课程(跨越SOE、FAS和MCS)，以向学生提供正在建设的迅速扩大的研究和应用天文台网络所需的技能。采用分布式资源分配、分布式仿真环境、群体非线性动态系统理论和计算机视觉学习等新的研究方法来实现所提出的目标。这一DDDAS框架的成果和影响是整合包括计算机、网络、数据档案、仪器、天文台、实验、嵌入式传感器和执行器在内的计算基础设施，以应对下列重大的国家和全球挑战：(1)安全、高效的导航和海洋作业，(2)高效的油类和危险物质泄漏轨迹预测和清理，(3)监测、预测和减轻沿海灾害，(4)军事行动，(5)搜索和救援，以及(6)有害藻类大量繁殖、缺氧条件和其他生态系统或水质现象的预测。

This proposal develops, deploys and evaluates a prototype pervasive dynamic oceanographic ecosystem that integrates sensors, networks, observatories, and computational algorithms to enable dynamic data driven application systems research (DDDAS) in oceanography and in particular the study of anoxia and hypoxia off the coast of New Jersey. The research findings are also incorporated in cross-disciplinary research curricula (across SOE, FAS and MCS) to provide students with the skills needed by the rapidly expanding network of research and applied observatories being constructed. New research methods in distributed resource allocation, distributed simulation environments, nonlinear dynamic system theory for swarming and computer vision learning methods are used to achieve the proposed goals. The result and impact of this DDDAS framework, is to integrate computational infrastructure that includes computers, networks, data archives, instruments, observatories, experiments, and embedded sensors and actuators, to address important national and global challenges such as(1) safe and efficient navigation and marine operations, (2) efficient oil and hazardous material spill trajectory prediction and clean up, (3) monitoring, predicting and mitigating coastal hazards, (4) military operations, (5) search and rescue, and (6) prediction of harmful algal blooms, hypoxic conditions, and other ecosystem or water quality phenomena.