| 项目编号: | 1511959
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| 项目名称: | UNS: Data Assimilation and Forecasting for Real-Time Drinking Water Distribution System Modeling |
| 作者: | Dominic Boccelli
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| 承担单位: | University of Cincinnati Main Campus
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| 批准年: | 2014
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| 开始日期: | 2015-09-01
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| 结束日期: | 2018-08-31
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| 资助金额: | USD336321
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | real-time
; drinking water distribution system
; real-time analysis
; real-time modeling framework
; utility computer system
; real-time decision
; real-time demand estimation
; 1511959boccellireal-time modeling
; real-time analytic
; water utility
; real-time demand estimate
; spread forecasting
; water quality maintenance
|
| 英文摘要: | 1511959
Boccelli
Real-time modeling of drinking water distribution systems has the potential to provide several benefits for water utilities by improving energy management, water quality maintenance, and response activities. While most utilities have a network model for planning purposes, these models are "static" and not amenable for making real-time decisions. Thus, there is a critical need to develop a real-time modeling framework that can estimate and forecast the unobserved demands that drive network dynamics.
The objective of this project is to develop a composite demand-hydraulic model - one that couples a demand model with a network hydraulic solver - capable of being updated in real-time using observed hydraulic information. The central hypothesis is that the observed hydraulic data commonly collected via utility computer systems can be used to estimate the expected values and uncertainty of a structured demand model that characterizes the temporal and spatial patterns of consumptive demands. The rationale for developing the real-time composite demand-hydraulic model is to provide a framework to inform real-time analytics and decision support associated with our drinking water distribution systems. Collectively, these outcomes will produce a holistic approach for real-time demand estimation and forecasting through the development of the composite demand-hydraulic model and monitoring placement algorithm. The long-term positive impacts of this project will result from the ability provide real-time demand estimates and forecasts that will lead to real-time analysis and decision support tools such as pump scheduling to minimize energy usage, rapid main break detection, and spread forecasting of contamination events to better identify response and mitigation activities. The PIs will strengthen the link between academic research and industrial entrepreneurship to provide opportunities to the students through: 1) providing industrial research opportunities to both graduate and undergraduate students to extend our research and development activities; 2) developing and distributing our software through open-source activities to further enhance the opportunities within our field; and, 3) introducing a broader range of students to technology transfer and the opportunities and challenges associated with industrial research. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93570
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Dominic Boccelli. UNS: Data Assimilation and Forecasting for Real-Time Drinking Water Distribution System Modeling. 2014-01-01.
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