Blending a fraction of hydrogen into the natural gas pipeline or urban pipe network is an efficient approach for hydrogen delivery. In this paper, the mathematical model of Hydrogen-Blended Natural Gas (HBNG) transportation is established, and the influences of hydrogen blending on hydraulic and thermal characteristics of natural gas pipeline and pipe network are numerically investigated. The impact of hydrogen blending ratio on the performance of centrifugal compressor and the operating point for joint operation of pipeline and compressor is discussed. Results illustrate that compared with natural gas without hydrogen, the hydrogen blending can reduce the pipeline friction resistance and increase the volume flow rate. However, due to the lower volumetric calorific value of HBNG, the energy flow rate actually decreases under the same transportation condition. Meanwhile, the temperature drop along the pipeline slows down due to the blended hydrogen. The performance degradation of centrifugal compressor occurs with the increasing hydrogen blending ratio, and the operating point for joint operation of pipeline and centrifugal compressor moves to the direction of higher volume flow rate and lower pressure. This study is expected to shed a light on the hydrogen delivery by natural gas pipelines and pipe networks.

It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study, the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV, a deep neural network (DNN) model was constructed based on CFD simulation data, and the main influencing factors of the COV including flow velocity, hydrogen blending ratio, gas temperature, flow distance, and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process, the effects of various parameters on the prediction accuracy of the DNN model were studied, and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.

The phase-field model (PFM) is gaining increasing attention in the application of multiphase flows due to its advantages, in which the phase interface is treated as a narrow layer and phase parameters change smoothly and continually at this thin layer. Thus, the construction or tracking of the phase interface can be avoided, and the bulk phase and phase interface can be simulated integrally. PFM provides a useful alternative that does not suffer from problems with either the mass conservation or the accurate computation of surface tension. In this paper, the state of the art of PFM in the numerical modeling and simulation of multiphase flows is comprehensively reviewed. Starting with a brief description of historical developments in the PFM, we continue to take a tour into the basic concepts, fundamental theory, and mathematical models. Then, the commonly used numerical schemes and algorithms for solving the governing systems of PFM in the application of multiphase flows are presented. The various applications and representative results, especially in non-match density scenarios of multiphase flows, are reviewed. The primary challenges and research focus of PFM are analyzed and summarized as well. This review is expected to provide a valuable reference for PFM in the application of multiphase flows.