Collaborative Research: ITR/AP: Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulations (DDDAS) Techniques

合作研究：ITR/AP：使用动态数据驱动应用模拟 (DDDAS) 技术进行预测污染物跟踪

• 批准号: 0218721
• 负责人: Christopher Johnson
• 金额: --
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218721&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; AP; Predictive

EIA-0219627 Craig Douglas University of Kentucky Collaborative Research: ITR/AP-Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation \(DDDAS\) TechniquesThis project will lead to a leap-ahead technology in simulation capabilities. Research in the development of new methods and algorithms for the specific application areas is needed. The dynamic application requirements will dictate computing systems' support that includes systems' software technologies, such as active middleware services for real time, dynamic reconfiguration capabilities, resource discovery, load balancing, security, fault tolerance, quality of service, and dynamic interfaces with field measurement systems. An encoded web stream set of contaminations from actual situations (both above ground and underground) will allow researchers besides us to tap into our virtual reality DDDAS environment. Visualization systems will allow us to work with a variety of real networks, sensors, and environments.

Craig Douglas University of Kentucky合作研究：ITR/ ap-预测污染物跟踪使用动态数据驱动的应用模拟\(DDDAS\)技术该项目将导致模拟能力的跨越式技术。需要针对具体应用领域研究开发新的方法和算法。动态应用需求将决定计算系统的支持，包括系统的软件技术，如实时活动中间件服务、动态重新配置能力、资源发现、负载平衡、安全性、容错、服务质量以及与现场测量系统的动态接口。一组来自实际情况（包括地面和地下）的编码网络污染流将允许我们以外的研究人员进入我们的虚拟现实DDDAS环境。可视化系统将使我们能够处理各种真实的网络、传感器和环境。