A leakage detection method for hydrogen-blended natural gas pipelines in utility tunnels based on multi-task LSTM and CFD simulation

Blending hydrogen into natural gas pipelines in utility tunnels is crucial for hydrogen applications, but introduces safety challenges. Therefore, this study proposes a new leakage detection method for hydrogen-blended natural gas (HBNG) pipelines in utility tunnels. This method employs computational fluid dynamics (CFD) simulations to generate a leakage diffusion dataset for HBNG pipelines. It then combines multi-task learning (MTL) with the long short-term memory (LSTM) network to develop an MTL-LSTM model for HBNG pipelines leakage detection in utility tunnels. The impact of hyperparameters on model accuracy is assessed using the dataset and the model is compared with other neural networks. Results indicate that after optimizing the hyperparameter combination, the MTL-LSTM model demonstrates higher accuracy and lower relative error in predicting the leakage location and rate of HBNG pipelines compared with other neural network models. This study provides technical support for the safe operation of HBNG pipelines in utility tunnels.