Sewer Network Maintenance Decision System Using Coupled Hydraulic Models
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Solution Overview
Problem
Existing methods for maintaining urban underground sewer networks fail to accurately account for multiphase flow and multi-field coupling, leading to inefficiencies in identifying the impact of pipeline functional defects on hydrology and hydrodynamics, resulting in low accuracy in urban waterlogging early warning and disaster prevention systems.
Innovation Solution
An intelligent decision-making method and system that utilizes a three-dimensional instantaneous hydraulic model, finite element fitting analysis, and deep small-world neural networks to rebuild a surface-subsurface one-two-dimensional coupled connection model, incorporating fluid dynamics and multi-objective planning algorithms for accurate identification of sewer network functional defects and waterlogging losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hydrological hydrodynamic coupling models are used for waterlogging prediction, then the system can provide early warning analysis, but the accuracy is low because they do not account for multiphase flow and multi-field coupling
Solution Approach 1:
The patent segments the complex multiphase flow model into separate phase-specific equations (gas phase, liquid phase, solid phase) that can be solved independently yet coupled together. This allows accurate prediction by accounting for each phase's characteristics while managing computational complexity through modular solution approaches.
Solution Approach 2:
The patent transitions from traditional 1D/2D models to a 3D instantaneous hydraulic model that incorporates vertical dimension effects in sewer pipelines. This dimensional enhancement captures the complex interaction between multiphase flow and pipeline geometry, significantly improving prediction accuracy for waterlogging conditions.
2Productivity
If control variable method is used to analyze pipeline functional defects one by one, then the analysis can be systematic, but the workload is heavy and efficiency is low
Solution Approach 1:
The patent replaces the manual control variable method with an intelligent decision-making system based on machine learning algorithms and automated computational models. This substitution dramatically increases analysis efficiency by automatically processing multiple pipeline defect scenarios simultaneously without the heavy manual workload of traditional systematic analysis.
3Measurement precision
If simple factors are considered in waterlogging prediction, then the calculation is easier, but the accuracy of results cannot be guaranteed
Solution Approach 1:
The patent changes the parameters considered in waterlogging prediction from simple hydrological variables to comprehensive multiphase flow parameters including gas-liquid-solid interactions, pressure gradients, and instantaneous hydraulic conditions. This parameter expansion captures the true complexity of sewer system behavior, ensuring accurate results while the automated computational framework manages the calculation complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and efficient decision-making for urban sewer network maintenance by integrating fluid dynamics, full-scale testing, and machine learning algorithms, ensuring precise preventive maintenance while considering the coupling effects of pipeline functional defects and waterlogging under multi-objective constraints.
Implementation Method 1
based on fluid dynamics and 'mass-momentum-energy' conservation theory, analyzing with a sewer network functional defect three-dimensional instantaneous hydraulic model
Implementation Method 2
based on fluid dynamics and 'mass-momentum-energy' conservation theory
Implementation Method 3
calibrating parameters by finite element fitting analysis and full-scale test
Implementation Method 4
using an R language, a dynamic library linking technology, and a long-short-term memory neural network method of multi-source data samples
Implementation Method 5
combining a node water level iteration method, a Preissmann slit method, a Godunov finite volume method and an unstructured grid to rebuild a surface-subsurface one-two-dimensional coupled connection model
Implementation Method 6
introducing a deep small-world neural network, a genetic algorithm and a simulated annealing algorithm to establish a multi-objective planning intelligent decision-making model
Implementation Method 7
introducing a deep small-world neural network, a genetic algorithm and a simulated annealing algorithm
Data Source
AI summary
An intelligent decision-making method for maintaining urban underground sewer network includes steps of: analyzing with a sewer network functional defect three-dimensional instantaneous hydraulic model; calibrating parameters by finite element fitting analysis and full-scale test, and verifying accuracy of the sewer network functional defect three-dimensional instantaneous hydraulic model; combining node water level iteration method, Preissmann slit method, Godunov finite volume method and unstructured grid to rebuild a surface-subsurface one-two-dimensional coupled connection model; using R language, dynamic library linking technology, and long-short-term memory neural network method of multi-source data samples for engineering secondary development of the surface-subsurface one-two-dimensional coupled connection model, and obtaining urban sewer network functional defect conditions with waterlogging result labels; and establishing a multi-objective planning intelligent decision-making model for sewer network maintenance and a solving method thereof. The present invention provides intelligent, accurate and scientific management for urban sewer network.