Buried gas pipeline multi-leakage parameter identification and concentration distribution rapid prediction method

By combining CFD and deep learning neural networks with ensemble Kalman filtering, the problem of incomplete concentration field information acquisition in buried gas pipeline leak detection is solved, achieving fast and accurate multi-parameter inversion and concentration distribution reconstruction, which is suitable for early warning of leak risks in buried gas pipelines.

CN121859786APending Publication Date: 2026-04-14NANJING TECH UNIV
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Patent Information

Application Number
CN202610019741.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-08
Publication Date
2026-04-14

AI Technical Summary

Technical Problem

Existing methods for detecting leaks in buried gas pipelines cannot achieve high-precision and rapid acquisition of concentration field information, cannot reflect the gas concentration distribution at the leak location in real time, and cannot simultaneously retrieve multiple leak parameters, resulting in an inability to accurately assess the scope of the accident's impact.

Method used

By combining computational fluid dynamics (CFD) and deep learning neural networks, a multi-condition training set is constructed. The neural network is used to quickly calculate the underground gas concentration distribution, and the ensemble Kalman filter (EnKF) is combined to achieve the collaborative inversion of multiple leakage parameters and reconstruct the concentration distribution of the entire space.

Benefits of technology

It enables real-time prediction and concentration field reconstruction of buried gas leak source parameters under limited monitoring point conditions, and can quickly identify parameters such as soil type, leak direction and leak flow rate to meet the needs of emergency situations.

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Abstract

The invention discloses a buried gas pipeline multi-leakage parameter identification and concentration distribution rapid prediction method, and belongs to the technical field of buried gas pipeline safety detection. The method comprises the following steps: firstly, based on a porous medium flow control equation and a component transport equation, establishing a leakage diffusion physical model by utilizing computational fluid mechanics and generating a multi-working-condition sample data set; training a neural network model by using the sample data, and constructing a rapid concentration prediction agent model; in combination with measured data of a monitoring area, an ensemble Kalman filtering algorithm is adopted, the agent model serves as a forward prediction operator, and collaborative inversion of parameters such as the soil type, the leakage direction and the flow is achieved through iterative updating; and finally, outputting complete underground gas concentration distribution in the monitoring area based on the leakage parameters obtained through identification. According to the method, rapid identification of multiple leakage parameters and accurate prediction of concentration distribution are realized under the condition of limited measuring points, and the method has remarkable disaster early warning and engineering application values.
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