Natural gas leakage estimation in underground utility tunnels using Bayesian inference based on flow fields with gas jet disturbance

Abstract

In damp environment of underground utility tunnels, natural gas pipelines may corrode, resulting in leakage from small holes. Considering the flammable and explosive properties of natural gas, accurate and prompt identification of leakage location and leakage rate after gas leaks is crucial for emergency repair. Prior research assumed that source leakage causes no disturbance on its surrounding flow field. However, when natural gas leaks, the leaking gas may reach a very high speed in certain cases, which will produce a non-negligible disturbance on the surrounding flow field, causing a large error in source parameters estimation. In this paper, Bayesian inference for source parameters estimation is further developed based on the disturbed flow field. The results demonstrate that when the leakage rate is high, using undisturbed flow fields for estimation will lead to large errors (with the maximum error of about 46 %), whereas estimated results are closer to true values when using disturbed flow fields with the final error within 5 %. Finally, the dimensionless leakage rate is used as a metric to quantify the disturbance effect. It is considered that disturbed flow fields are needed for source parameters estimation when the dimensionless leakage rate is above 1.