受到规划设计、施工建设与运行管理等多方面因素的制约，我国城市排水系统在内涝防治和溢流污染控制方面普遍存在一定的问题。面对排水系统空间复杂性和水力过程动态性特征，以及降雨不确定性的影响，如何诊断识别系统中的薄弱环节，如何为建设相应的工程措施以改进系统性能提供科学合理的决策依据，是当前亟需解决的技术难题。本研究旨在建立一套具有一定普适性的城市排水系统内涝与溢流控制性能评价与优化方法，为削减城市排水系统内涝和溢流，提高系统雨季运行绩效提供支撑。本研究构建了基于指标体系的城市排水系统内涝与溢流控制性能评价方法。从管段、片区、全系统不同层次出发，综合考虑降雨过程中的平均性能和高危时段，形成由4个溢流指标和8个内涝指标构成的评价指标体系。通过不确定降雨条件下排水系统动态模拟对指标加以量化，计算结果可用于表征内涝、溢流、调蓄潜力的大小、空间分布和时间特征。在此基础上评估系统性能，进而识别重点控制区域和关键控制时段，确定系统内部挖潜、管段更换、增设调蓄设施等典型控制措施的合理位置及其布局优先序。针对排水系统内涝和溢流控制性能改善需求，在考虑降雨随机性对系统性能影响的前提下，建立了排水系统调蓄设施优化设计方法，并开发了调蓄设施多目标优化设计模型用于辅助设计。该模型以多种降雨情景下溢流量加权均值最小化、内涝量加权均值最小化、系统成本最小化为目标，采用分步优化与并行计算相结合的优化策略和NSGA_II算法予以求解，实现设施数量、规模与布局的同步优化。利用所建立的评价方法和优化模型，在昆明市城北排水片区开展案例研究，评估了案例区域排水系统的内涝和溢流控制性能，识别出急需改善的重点区域，提出不同过载控制水平下的管段更换建议，并给出新增调蓄设施的优化设计方案集合。案例研究验证了城市排水系统内涝与溢流控制性能评价方法的可行性与实用性，优化方法的有效性和模型工具的可靠性。
Constricted by multiple factors in phase of planning, designing, construction and management of operation, urban drainage system in China shows obvious drawbacks relating to waterlog prevention and combined sewer overflow control. How to identify potential threat of system, and to provide decision support for corresponding engineering measures in the target of system performance improving, is still a difficult problem has yet to be solved. To solve these problems, an integrated method for evaluating and optimizing flood and overflow control capacity of urban drainage system is developed to mitigate urban waterlogging and CSO damage, and to support the system operation performance improving in rainy weather.This research sets up a method for efficiently evaluating the flood and overflow control capacity of urban drainage system based on index system. From pipe section, integrated district to the whole system, an index system concludes 4 overflow indexes and 8 flood indexes was formed considering the average performance and high risk period in the system dynamic process. Also, the index system is quantified based on dynamic modelling under uncertainty conditions of rainfall. Corresponding results elaborates the numerical scale, spatial distribution and time characteristics of flood, overflow and potential control capacity. On the basis of which, the system performance could be evaluated. More over, key regions and periods demands control could be identified, thus determining the proper location and placement priority of typical measures such as internal capacity usage, pipe section replacement and storage tanks.To respond the improvement requirements of flood and overflow capacity of urban drainage system, optimal storage facilities designing method is established considering the influence of rainfall uncertainty. And a multi-objective optimal design model is developed to assist the design. The three objective functions include minimizing weighted average spilled volume from CSOs, total volume of urban flooding, and system retrofit cost. The solving strategy combines stepwise optimization and distributed calculation strategy, and based on non-dominated sorting genetic algorithm II (NSGA_II), which can generate Pareto optimal solutions with reconcile of number, location and scale. With the multiformity of variable types, constrictions and nonlinear system behaviors, the algorithm is improved to cut down calculation time to fit the design requirements. The evaluation and optimal method and the corresponding tools could support for more scientific and rational decisions.The established method and developed tools are implemented to a representative complicated drainage system in North District of central Kunming City, China. The characteristic of the drainage system’s flood and overflow control capacity is recognized. Area critical for flood and overflow control were highlighted. The pipeline replacement control suggestion for different drainage network is proposed, together with the collection of facility storage design proposals. The case study verifies the feasibility and practicability of the proposed flood and overflow control capacity evaluation method of urban drainage system, the effectiveness of the optimal design method of storage facilities, and the reliability of modeling tools.
