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CAREER: Predictive Estimation for Managing Water Distribution Systems and Applications of Distributed Sensing Technologies

职业：管理供水系统的预测估计和分布式传感技术的应用

基本信息

批准号：
1943428
负责人：
Polina Sela
金额：
$ 52.3万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943428&HistoricalAwards=false
关键词：
CAREER Predictive Estimation Managing Water

项目摘要

This Faculty Early Career Development (CAREER) grant will support decision-making processes by water system managers to advance and promote intelligent and resilient water systems. Water distribution systems are critical components of our urban environments providing safe drinking water to our homes and businesses. This research will develop computational methods and tools for using data collected by new sensing devices placed throughout a water system’s distribution network. These tools will better estimate current system’s operation given current demands and necessary modifications given changing demands and uncertain hazards. The primary impact of this research is to bridge knowledge gaps toward intelligent water systems through dissemination of new scientific tools, training future engineers, and integrating research into education. This scientific research contributes to NSF’s mission to promote the progress of science, to advance national welfare, and to secure public water provisions.This project has a research and education plan that focuses on creating new models for predictive state estimation under limited observability through parameter identification, uncertainty quantification, and model reduction for large-scale intelligent water systems. Research activities include: (1) creating new state estimation models that seamlessly integrate heterogeneous measurements from distributed sensing devices; (2) generating flexible alternatives for reducing the dimensionality of the state estimation and uncertainty propagation problems by reducing the hydraulic models through structured elimination of nodes, demand aggregation, and identification of influential parameters; (3) creating new sensor placement strategies for best selection and location of heterogeneous metering instruments providing maximal information gain; and (4) estimating prediction bounds and quantifying prediction sensitivity to measurement, demand, and topology uncertainties. Outreach activities include stakeholders’ workshop that will engage water utilities and local agencies, tech and consulting companies to identify and present practical solutions for technology adoption; open-source toolkit and repository for reproducibility and dissemination of research outcomes and technical workshop for training future engineers in advanced modeling tools and increasing minority participation; battle of algorithms for sensor networks for state estimation as a mechanism to challenge and train researchers, create and share new scientific tools; water competition to get young people excited about engineering through fun, hands-on activity; and revamping engineering curriculum to adapt to the advances in sensing and computational technologies and integrate research into education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该学院早期职业发展（CAREER）补助金将支持水系统管理人员的决策过程，以推进和促进智能和弹性水系统。配水系统是我们城市环境的关键组成部分，为我们的家庭和企业提供安全的饮用水。这项研究将开发计算方法和工具，用于使用放置在整个供水系统的分配网络的新传感设备收集的数据。这些工具将更好地估计当前系统的操作，给出当前的需求和必要的修改，给出不断变化的需求和不确定的危险。这项研究的主要影响是通过传播新的科学工具，培训未来的工程师以及将研究融入教育，弥合智能水系统的知识差距。该项目的研究和教育计划是通过对大规模智能水系统的参数识别、不确定性量化和模型简化，在有限的可观测性条件下建立预测状态估计的新模型。研究活动包括：（2）生成灵活的备选方案，用于通过节点的结构化消除、需求聚集和有影响的参数的识别来减少水力模型，从而减少状态估计和不确定性传播问题的维度;（3）创建新的传感器布置策略，用于提供最大信息增益的异构计量仪器的最佳选择和定位;以及（4）估计预测界限并量化预测对测量、需求和拓扑不确定性的敏感性。外联活动包括：利益攸关方讲习班，该讲习班将使供水公司和地方机构、技术和咨询公司参与，以确定和提出采用技术的实际解决方案;开放源码工具包和研究成果复制和传播资料库，以及培训未来工程师掌握先进建模工具和增加少数群体参与的技术讲习班;传感器网络状态估计算法之战，作为挑战和培训研究人员、创造和分享新科学工具的机制;水竞赛，通过有趣的动手活动让年轻人对工程感到兴奋;该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。