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Uncertainty analysis of water supply networks using the fuzzy set theory and NSGA-Ⅱ

机译:基于模糊集理论和NSGA-Ⅱ的供水管网不确定度分析

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This work introduces an approach for taking into account the uncertainty of pipe friction coefficients and nodal demands in the hydraulic analysis of water supply networks. For this purpose, uncertainties are represented by fuzzy numbers and incorporated into the network's governing equations. Input uncertainties are spread out on the network and influence its hydraulic responses, including pipe velocities and nodal pressures. To estimate the responses' uncertainty, input fuzzy numbers are discretized in some levels of membership function. Then, a multiobjective optimization problem is developed for each level to find the extreme values of the node pressures and pipe velocities. The raised problem is solved using the method of Non Dominated Sorting Genetic Algorithm (NSGA-Ⅱ) coupled to the network hydraulic simulation model. The proposed approach is applied to an example and a real pipe network. It is found that small uncertainties in input variables can significantly influence the network's responses as well as its performance reliability. It is also concluded that NSGA-Ⅱ has a great role in solving the problem systematically, and improves the computational efficiency of the whole process of network fuzzy analysis.
机译:这项工作介绍了一种在供水网络的水力分析中考虑管道摩擦系数和节点需求不确定性的方法。为此,不确定性用模糊数表示,并结合到网络的控制方程中。输入不确定性分散在网络上并影响其水力响应,包括管道速度和节点压力。为了估计响应的不确定性,在某些级别的隶属函数中将输入的模糊数离散化。然后,针对每个级别开发一个多目标优化问题,以找到节点压力和管道速度的极值。提出的问题采用非支配排序遗传算法(NSGA-Ⅱ)结合网络水力仿真模型解决。所提出的方法被应用于示例和真实的管网。发现输入变量的小的不确定性会显着影响网络的响应及其性能可靠性。得出的结论是,NSGA-Ⅱ在系统地解决问题方面具有重要作用,并提高了网络模糊分析全过程的计算效率。

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