Decision Support for Urban Water Management - Enhanced Technology Design, Modelling and Optimization Approaches for Collection Systems

城市水管理决策支持 - 收集系统的增强技术设计、建模和优化方法

• 批准号: RGPIN-2019-05608
• 负责人: Joksimovic, Darko
• 金额: $ 1.89万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738398
• 关键词: Decision; Support; Urban; Water; Management

As demands placed upon urban drainage systems continue to increase due to growth in urban areas, climate change and aging infrastructure, decentralized solutions are increasingly sought. However, the lessons learned over the years of the large stock of existing infrastructure, and opportunities for their design improvements and retrofit options are frequently overlooked. In addition, the modelling tools have not kept pace with technological advancements, including the increasing options for systemwide monitoring and control - the `smart water systems'. The proposed research will address some of these issues by optimizing the design of the Etobicoke Exfiltration System, a technology identified through past monitoring to present a potential for better performance, using a combination of physical and numerical modelling and integration into existing stormwater modelling tool. Techniques for low-cost, widespread monitoring of sewer systems will be developed, along with analytical tools that take advantage of vastly increased volume of measurements of system performance in continuous identification and quantification of detrimental extraneous flows. Necessary modifications to stormwater management models will be developed to allow evaluation of distributed monitoring and control, along with optimization methodologies, leading to the development of design guidance for smart stormwater systems. The outputs of this research are meant to address some of the key gaps perceived to currently exist in the methodologies for urban drainage modelling and optimization.

由于城市地区的发展、气候变化和基础设施老化，对城市排水系统的要求不断增加，人们越来越多地寻求分散的解决方案。然而，多年来从现有基础设施的大量存量中吸取的经验教训及其改进设计和改装选择的机会往往被忽视。此外，建模工具没有跟上技术进步的步伐，包括增加全系统监测和控制的选择--“智能供水系统”。拟议的研究将通过优化Etobike渗出系统的设计来解决其中一些问题，这是一项通过过去的监测确定的技术，利用物理和数值模拟相结合并与现有的雨水模拟工具相结合，显示出改进性能的潜力。将开发低成本、广泛监测下水道系统的技术，以及分析工具，这些工具利用大幅增加的系统性能测量量，不断识别和量化有害的外来流动。将对雨水管理模型进行必要的修改，以便能够评价分布式监测和控制以及优化方法，从而制定智能雨水系统的设计指南。这项研究的成果旨在解决人们认为目前城市排水系统建模和优化方法中存在的一些关键差距。