给水管网动态模型维护与校验方法研究
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摘要
城市给水管网微观水力模型(以下均简称为管网模型)是了解给水管网运行状况模拟的基础和供水系统信息化的重要环节，是城市供水部门进行管网规划、运行管理和优化调度的现代技术方法。建立准确的管网模型，是供水行业信息化进程中迫切需要解决的问题。
针对管网模型应用和管网建模技术要求，以给水管网计算机数学模拟为基础，提出管网模型参数校验的自适应遗传模拟退火算法(Adaptive Genetic Algorithm and Simulated Annealing，AGASA)，并结合两个算例管网和一个实例管网进行了单工况与延时模拟的参数校验。
论文中采用管网隐式校验模型进行管网模型的校验工作。通过计算管网模型的模拟量与现场实测量之间差异的最小值，间接的求出管网模型参数的最优值。管网模型参数校验的对象是管网中难以实测的各类参数，本论文选取管道粗糙系数和节点流量两类参数作为管网模型校验的对象。参数校验时的约束条件分别是：管道粗糙系数(海参—威廉系数，C值)的变化范围限定在80～130之间，节点流量的调整量限定在其初始值的±20％以内。
管网隐式校验模型的目标函数具有多种形式，包括实测值和管网模型模拟值的误差平方和形式∑(H_(mi)-H_(ti))~2(标准最小二乘)、实测值和管网模型模拟值误差绝对值之和形式∑∣H_(mi)-H_(ti)∣、实测值和管网模型模拟值相对误差绝对值之和形式∑∣(H_(mi)-H_(ti))／H_(ti)∣×100％等三种目标函数。本文分析了三种形式的目标函数对于校验效果的影响。结果表明，以误差绝对值之和作为目标函数的校验效果最好，以相对误差绝对值为目标函数的校验效果较差，而以误差平方和作为目标函数的校验效果最差。
针对管网模型校验参数间的误差互相补偿作用，考察了单工况和延时模拟时校验参数间误差补偿的情况。基于延时模拟进行管网模型校验是在逐步变化的工况下校验管道粗糙系数和节点流量的准确度，因此，具有更
The microscopic hydraulic model (MHM) of urban water distribution network
(WDN) is the foundation in WDN control, and one of the important part of water
supply system inforaiationization. Also, the MHM is a most useful tool for network
design and optimal operation. Building an accurate MHM of WDN, is the key
problem needs to be solved in process of water supply system informationization.
In view of MHM of urban WDN application and WDN modeling technology requirement, This paper proposes a hybrid algorithm for water distribution model calibration based on water simulation techniques, which combined the Adaptive Genetic Algorithm with Simulated Annealing(AGASA). The algorithm is finally tested and approved with two simple networks and an actual WDN, and both the fixed-time simulation and the extended period simulation were performed.
In this paper, the calibration of WDN parameters is based on an implicit calibration model, which takes mitigation of the differences between the simulated values and the observed values as the objective function. And the best value of parameters in MHM is achieved indirectly. The object of calibration is those parameters in the WDN model which are difficult to measure. In the paper, pipe roughness coefficient and node demand are selected as parameters to be calibrated. The value range of pipe roughness coefficient (the Hazen-Williams coefficient, C) is defined between 80～130, the node demand adjustment range is defined between 80% ～120% of the initial value.
There are many kinds of objective function in the implicit calibration model,
including the least-squares method ∑(H_(mi)-H_(ti))~2 , the sum of differences absolute
value between actual value and WDN model simulation value ∑|H_(mi)-H_(ti)|, the sum of relative differences absolute value between actual value and WDN model
simulation value ∑|H_(mi)-H_(ti)/H_(ti)|×100%. The second one which summarization of
针对管网模型应用和管网建模技术要求，以给水管网计算机数学模拟为基础，提出管网模型参数校验的自适应遗传模拟退火算法(Adaptive Genetic Algorithm and Simulated Annealing，AGASA)，并结合两个算例管网和一个实例管网进行了单工况与延时模拟的参数校验。
论文中采用管网隐式校验模型进行管网模型的校验工作。通过计算管网模型的模拟量与现场实测量之间差异的最小值，间接的求出管网模型参数的最优值。管网模型参数校验的对象是管网中难以实测的各类参数，本论文选取管道粗糙系数和节点流量两类参数作为管网模型校验的对象。参数校验时的约束条件分别是：管道粗糙系数(海参—威廉系数，C值)的变化范围限定在80～130之间，节点流量的调整量限定在其初始值的±20％以内。
管网隐式校验模型的目标函数具有多种形式，包括实测值和管网模型模拟值的误差平方和形式∑(H_(mi)-H_(ti))~2(标准最小二乘)、实测值和管网模型模拟值误差绝对值之和形式∑∣H_(mi)-H_(ti)∣、实测值和管网模型模拟值相对误差绝对值之和形式∑∣(H_(mi)-H_(ti))／H_(ti)∣×100％等三种目标函数。本文分析了三种形式的目标函数对于校验效果的影响。结果表明，以误差绝对值之和作为目标函数的校验效果最好，以相对误差绝对值为目标函数的校验效果较差，而以误差平方和作为目标函数的校验效果最差。
针对管网模型校验参数间的误差互相补偿作用，考察了单工况和延时模拟时校验参数间误差补偿的情况。基于延时模拟进行管网模型校验是在逐步变化的工况下校验管道粗糙系数和节点流量的准确度，因此，具有更
The microscopic hydraulic model (MHM) of urban water distribution network
(WDN) is the foundation in WDN control, and one of the important part of water
supply system inforaiationization. Also, the MHM is a most useful tool for network
design and optimal operation. Building an accurate MHM of WDN, is the key
problem needs to be solved in process of water supply system informationization.
In view of MHM of urban WDN application and WDN modeling technology requirement, This paper proposes a hybrid algorithm for water distribution model calibration based on water simulation techniques, which combined the Adaptive Genetic Algorithm with Simulated Annealing(AGASA). The algorithm is finally tested and approved with two simple networks and an actual WDN, and both the fixed-time simulation and the extended period simulation were performed.
In this paper, the calibration of WDN parameters is based on an implicit calibration model, which takes mitigation of the differences between the simulated values and the observed values as the objective function. And the best value of parameters in MHM is achieved indirectly. The object of calibration is those parameters in the WDN model which are difficult to measure. In the paper, pipe roughness coefficient and node demand are selected as parameters to be calibrated. The value range of pipe roughness coefficient (the Hazen-Williams coefficient, C) is defined between 80～130, the node demand adjustment range is defined between 80% ～120% of the initial value.
There are many kinds of objective function in the implicit calibration model,
including the least-squares method ∑(H_(mi)-H_(ti))~2 , the sum of differences absolute
value between actual value and WDN model simulation value ∑|H_(mi)-H_(ti)|, the sum of relative differences absolute value between actual value and WDN model
simulation value ∑|H_(mi)-H_(ti)/H_(ti)|×100%. The second one which summarization of
引文
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