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Method for identifying risky pipeline and node by adopting parameter uncertainty analysis model

A technology of parameter uncertainty and uncertainty, which is applied in data processing applications, electrical digital data processing, special data processing applications, etc., and can solve problems such as difficulty in meeting density requirements

Pending Publication Date: 2020-12-15
BEIJING UNIV OF TECH
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

However, this method needs to provide relatively dense data of monitoring points. At present, the density of drainage monitoring points in most cities in China is difficult to meet the requirements, and even some cities or regions have no monitoring data. Therefore, it is necessary to establish a model that does not have Calibrate the method to ensure that the simulation accuracy of the drainage model meets the requirements under the conditions of the required monitoring data, so as to realize the use of the model to analyze the risky pipelines and nodes in the pipeline network, and optimize the pipeline renewal and reconstruction plan

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  • Method for identifying risky pipeline and node by adopting parameter uncertainty analysis model
  • Method for identifying risky pipeline and node by adopting parameter uncertainty analysis model
  • Method for identifying risky pipeline and node by adopting parameter uncertainty analysis model

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Embodiment 1

[0040] Taking the drainage pipe network of a city research area ( figure 2 ) as an example, the total area of ​​the study area is 3.1km 2 , the rainwater pipe network design standard return period is 3 to 5 years, 764 inspection wells, 766 pipes (total length 22.36km), and 7 water outlets. The return period of the design standard for river flood control is 50 to 100 years, and the water outlet is set as free flow. Using the parameters shown in Attached Table 1 as the uncertainty parameters of this case model, this method is applied to the identification of risk pipelines and nodes in this area.

[0041] The number of Monte Carlo simulations N is set to 5000. In order to ensure the convergence of the Monte Carlo simulation calculation results, all and Coefficient of variation δ k,node and δ k,pipe The maximum values ​​are {0.057, 0.047}, indicating that the Monte Carlo simulation results have good convergence. image 3 For 5000 simulations of selecting the values ​​of ...

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Abstract

The invention discloses a method for identifying risk pipelines and nodes by adopting a parameter uncertainty analysis model, and belongs to the technical field of drainage pipe network analysis. An SWMM model solver is adopted, the failure probabilities of nodes and pipelines are solved according to the Monte Carlo random simulation principle, the risks of the pipelines and the nodes are expressed with the failure probabilities, and theoretical guidance is provided for pipe network risk assessment work under the conditions that parameters are uncertain and monitoring data do not exist.

Description

technical field [0001] The invention relates to a method for identifying high-risk pipelines and nodes by establishing an analysis model considering the uncertainty of model parameters for the requirement of urban drainage network function risk assessment. The invention belongs to the technical field of drainage pipe network analysis. Background technique [0002] The drainage pipe network model is a reasonable generalization of the actual drainage pipe network system based on the basic laws of urban surface runoff, runoff and pipe network convergence. By simulating the state of the pipe network under various working conditions, mastering the drainage and operation rules of the pipe network, combined with the analysis of the effectiveness of each pipe section and node in the pipe network, it can provide planning, design, operation management and renewal of the drainage pipe network Program formulation provides a scientific basis for decision-making. [0003] Due to the unc...

Claims

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Application Information

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IPC IPC(8): G06F30/18G06Q10/06G06Q50/06G06F111/02G06F111/08G06F113/14G06F119/02
CPCG06F30/18G06Q10/0635G06Q50/06G06F2111/02G06F2111/08G06F2113/14G06F2119/02Y02A10/40
Inventor 吴珊马晴晴侯本伟程玉林
Owner BEIJING UNIV OF TECH