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

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

• 批准号: 0218229
• 负责人: Richard Ewing
• 金额: $ 16万
• 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: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218229&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.

EIA-0219627肯塔基州大学克雷格道格拉斯合作研究：ITR/AP-使用动态数据驱动应用模拟技术的预测污染物跟踪（DDDAS）该项目将导致模拟能力的飞跃式技术。 需要研究开发用于特定应用领域的新方法和算法。 动态应用需求将规定计算系统的支持，其包括系统的软件技术，诸如用于真实的时间的主动中间件服务、动态重新配置能力、资源发现、负载平衡、安全性、容错性、服务质量以及与现场测量系统的动态接口。 来自实际情况（地上和地下）的污染物的编码网络流集将允许除了我们之外的研究人员利用我们的虚拟现实DDDAS环境。 可视化系统将使我们能够与各种真实的网络、传感器和环境一起工作。