Natural gas compartment accommodated in utility tunnels is beneficial in meeting the pressing demand of energy supply and sustainable urban environment. However, the leaking gas characterized by flammable and explosive can pose a huge threat to the safe operation of the utility tunnel. When an unexpected gas leakage accident happens in the actual situation, the prior information associated with the leakage source is commonly unclear or unknown. Therefore, the absence of an available tool for reasonable leakage and dispersion prediction in the above scenario precludes the timely and appropriate emergency response treatment. In this study, a three-dimensional source term estimation (3D-STE) model with the combination of the computational fluid dynamics (CFD) and ensemble Kalman filter (EnKF) algorithm is proposed to achieve spatiotemporal gas concentration prediction and gas emission source estimation. In the proposed approach, the observation data can be incorporated into the gas dispersion simulations continuously, thus the simulation results can be revised by the observation data and the source term estimation of gas leakage can be achieved by employing the EnKF algorithm. A twin experiment is employed to validate the effectiveness and practicability of the proposed model. The results show that the proposed model can revise the prior errors in the gas leakage rate significantly and obtain an accurate prediction of gas concentration distribution as well as gas leakage rate. A feasible framework is also proposed serving as a good paradigm for the 3D-STE model application. This study helps for consequence assessment and emergency response of gas leakage accidents in utility tunnels.@en

Gas drainage system is a critical technique to prevent gas outbursts in the underground coal mine. The leakage of gas drainage pipelines can pose serious threats to the safety production of underground mining. In this paper, a multi-factors gas drainage pipeline leakage and diffusion (GDPLD) model is proposed based on the OpenFOAM platform, which can analyze the leakage and diffusion characteristics inside the pipelines. With field measurement data in a coal mine, the GDPLD model is verified with good practicability. Furthermore, scenario analysis in the context of different leak sizes, locations, and pipeline diameters is presented to evaluate the specific characteristics of gas leakage and diffusion inside the pipeline with negative pressure. The results showed that the leakage accident close to the pump station with a large leak size and small pipeline diameter usually represents the worst case, and when gas sensors are installed downstream of the leakage location, it is helpful to realize effective detection of the leakage accident. This study can help to improve the understanding of the leakage and diffusion characteristics of gas drainage pipelines and provide technical supports for the monitoring system design of the gas drainage pipelines in underground coal mines.

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