Effect of different parameters on hydrogen affected fatigue failure in pipeline steels

Mohtadi-Bonab, M.A.

doi:10.1016/j.engfailanal.2022.106262

Cited by 15 publications

(2 citation statements)

References 72 publications

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“…Safety is the premise of large-scale application of hydrogen pipeline. It is necessary to establish standards for pipeline protection, operation process control, system maintenance, and emergency treatment when safety risks exist in the operation of hydrogen pipeline [9] . Generally safety is not specified as an independent standard, but as an important component of each link of the pipeline hydrogen industry chain, the safety sub-system occupies an important position in the standard system of hydrogen pipeline.…”

Section: Safetymentioning

confidence: 99%

Research on standards and standard system of hydrogen pipeline in China

Xiaolu,

Yanmei,

Wei

et al. 2024

E3S Web Conf.

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Pipeline hydrogen transport is one of the main technical paths to achieve large-scale storage and transportation of hydrogen energy, and standards are important technical support for pipeline hydrogen transport applications. China’s hydrogen pipeline construction started late, and a relatively complete hydrogen pipeline standard system has been established in the world, but China’s hydrogen pipeline standard system has not been established, the lack of key technical standards and other problems. This paper investigates the current situation of hydrogen transport pipeline standards and the applicability to China. A standard system is preliminarily constructed for hydrogen transport pipelines, including 6 sub-systems and 23 technical standard fields. Some suggestions are put forward for the development of China’s hydrogen pipeline standardization.

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Section: Safetymentioning

confidence: 99%

Research on standards and standard system of hydrogen pipeline in China

Xiaolu,

Yanmei,

Wei

et al. 2024

E3S Web Conf.

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“…As the development of natural gas pipelines is mature, many countries have tried to blend a certain proportion of hydrogen in natural gas pipelines to transport the hydrogen and reduce household carbon emissions, and the mixture of hydrogen and natural gas is called hydrogen-enriched natural gas (HENG). Therefore, the large-scale use of HENG can not only effectively reduce the carbon emissions of end users, but also avoid the high cost of newly constructed hydrogen pipelines for pure hydrogen transportation [1][2][3]. Many studies have shown that when the hydrogen blending ratio (HBR) is less than 20% (volume fraction), it will not cause hydrogen failure in the pipelines or affect the use of the end devices obviously; thus, the HBR of common natural gas pipelines is usually between 5-20% [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Guo

et al. 2022

Energies

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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.

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