Collaborative Research/DDDAS-TMRP: An Adaptive Cyberinfrastructure for Threat Management in Urban Water Distribution Systems

这个合作/多学科研究项目的目标是开发一个网络基础设施系统，该系统将适应和控制不断变化的数据，模型，计算机资源和管理选择的需求，并通过动态的工作流程设计提供便利。利用虚拟模拟和实地研究，将在配水系统污染事件适应性管理的说明性情景中对这一网络基础设施进行测试。 饮用水分配系统中的污染威胁管理涉及污染源和羽流的实时表征、控制策略的识别以及增量数据采样时间表的设计。这需要动态集成的时变测量流量，压力和污染物浓度与分析模块，包括模型来模拟系统的状态，自适应采样的统计方法，和优化方法，以寻找有效的控制策略。对于现实的配电系统，分析模块是高度计算密集型的，需要通过计算机集群进行多级并行处理。虽然数据通常驱动分析模块，但当污染事件发生时，用于提高这些模块生成的解决方案的准确性和确定性的数据需求会动态变化。由于这种对时间敏感的威胁事件需要实时响应，因此计算需求也必须与可用资源自适应地匹配。因此，需要一种软件系统来经由高性能计算架构（例如，TeraGrid），使得测量系统、分析模块和计算资源可以相互适应和相互引导。城市供水系统容易受到意外和故意污染事件的影响，这些事件可能导致不利的人类健康和安全影响。 典型的市政配水系统中的管网包括冗余的流动路径，以确保在网络的部分不可用时提供服务，并且设计有大量的存储以在每日高峰需求期间输送水。 因此，一个典型的网络是高度互连的，并且在流量和运输路径上经历显著和频繁的波动。 这些设计特征无意中使系统中单个点处的污染物通过网络中的不同路径快速传播，而消费者和操作员对此一无所知。当经由第一道防线检测到污染事件时，例如，根据水质监测传感器网络的数据和消费者的报告，随着污染事件的展开，市政当局面临着几个关键问题：污染源在哪里？这种污染是什么时候发生的，持续了多久？应在何处进行额外的水力或水质测量，以更准确地查明污染源？目前和不久的将来污染程度如何？应采取何种响应措施（如关闭部分管网、实施液压控制策略或引入去污剂）来最大限度地减少污染事件的影响？这些行动对消费者有何影响？对这些复杂问题的实时回答将带来重大的计算挑战。 该项目将通过开发自适应网络基础设施来应对这些挑战，该基础设施将通过计算组件和实时传感器数据的动态集成来实现实时处理和控制。该系统将使用基于大都市地区实地规模数据的污染情景进行评估。

The goal of this collaborative/multi-disciplinary research project is to develop a cyberinfrastructure system that will both adapt to and control changing needs in data, models, computer resources and management choices facilitated by a dynamic workflow design. Using virtual simulation and a field study, this cyberinfrastructure will be tested on illustrative scenarios for adaptive management of contamination events in water distribution systems. Contamination threat management in drinking water distribution systems involves real-time characterization of the contaminant source and plume, identification of control strategies, and design of incremental data sampling schedules. This requires dynamic integration of time-varying measurements of flow, pressure and contaminant concentration with analytical modules including models to simulate the state of the system, statistical methods for adaptive sampling, and optimization methods to search for efficient control strategies. For realistic distribution systems, the analytical modules are highly compute-intensive, requiring multi-level parallel processing via computer clusters. While data often drive the analytical modules, data needs for improving the accuracy and certainty of the solutions generated by these modules dynamically change when a contamination event unfolds. Since such time-sensitive threat events require real-time responses, the computational needs must also be adaptively matched with available resources. Thus, a software system is needed to facilitate this integration via a high-performance computing architecture (e.g., the TeraGrid) such that the measurement system, the analytical modules and the computing resources can mutually adapt and steer each other.Urban water distribution systems are vulnerable to accidental and intentional contamination incidents that could result in adverse human health and safety impacts. The pipe network in a typical municipal distribution system includes redundant flow paths to ensure service when parts of the network are unavailable, and is designed with significant storage to deliver water during daily peak demand periods. Thus, a typical network is highly interconnected and experiences significant and frequent fluctuations in flows and transport paths. These design features unintentionally enable contamination at a single point in the system to spread rapidly via different pathways through the network, unbeknown to consumers and operators. When a contamination event is detected via the first line of defense, e.g., data from a water quality surveillance sensor network and reports from consumers, the municipal authorities are faced with several critical questions as the contamination event unfolds: Where is the source of contamination? When and for how long did this contamination occur? Where additional hydraulic or water quality measurements should be taken to pinpoint the source more accurately? What is the current and near future extent of contamination? What response action, such as shutting down portions of the network, implementing hydraulic control strategies, or introducing decontaminants, should be taken to minimize the impact of the contamination event? What would be the impact on consumers by these actions? Real-time answers to such complex questions will present significant computational challenges. This project will address these challenges by developing an adaptive cyberinfrastucture that will enable real-time processing and control through dynamic integration of computational components and real-time sensor data. This system will be evaluated using contamination scenarios based on field-scale data from a large metropolitan area.