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

EIA-0219627克雷格·道格拉斯肯塔基大学合作研究：ITR/AP-使用动态数据驱动应用程序模拟(DDDAS)技术的预测污染物跟踪该项目将导致模拟能力方面的跨越式技术。需要研究针对特定应用领域开发新的方法和算法。动态应用需求将规定计算系统的支持，包括系统的软件技术，例如用于实时的主动中间件服务、动态重新配置能力、资源发现、负载平衡、安全性、容错、服务质量以及与现场测量系统的动态接口。来自实际情况(地面和地下)的污染物编码网络流集合将允许研究人员除了我们之外还可以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。

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.