作者：何太碧1, 何风成1, 杜文1, 袁伟杰1, 贾瑞2, 韩锐2
作者单位：1. 西华大学汽车与交通学院，四川 成都 610039;
2. 西华大学材料科学与工程学院，四川 成都 610039
关键词：数值模拟;输氢管道;遗传算法;神经网络
摘要：
为探究埋地输氢管道的氢气泄漏扩散规律，建立以埋地输氢管道为对象的流体域模型，讨论工作压力、泄漏孔径、土壤类型对氢气泄漏扩散的影响。结果表明，当管道发生泄漏后，气体扩散速度和泄漏量与管道工作压力、泄漏孔径呈正比关系，同时在不同的监测位置，氢气浓度具有不同的变化规律；氢气在低黏度的土壤中泄漏时有着较大的扩散速度，管道在砂土中的泄漏量是黏土中的40倍；因此当管道处于低黏度土壤、高压力的工况时具有较高的危险系数。为对不同工况下地面氢气浓度达到氢气爆炸下限的时间进行预测，以数值模拟结果为训练样本建立ga-bp神经网络模型，结果显示该模型平均误差仅为3.3%，性能远优于传统bp模型，能合理地对该问题进行预测。
research on the leakage and diffusion law of buried hydrogen pipeline
he taibi1, he fengcheng1, du wen1, yuan weijie1, jia rui2, han rui2
1. school of automobile and transportation, xihua university, chengdu 610039, china;
2. school of materials science and engineering, xihua university, chengdu 610039, china
abstract: to investigate the hydrogen leakage and diffusion law of buried hydrogen pipelines, a fluid domain model was established with buried hydrogen pipelines as the object, and the effects of working pressure, leakage aperture, and soil type on hydrogen leakage and diffusion were discussed. the results show that when a pipeline leaks, the gas diffusion rate and leakage amount are directly proportional to the pipeline working pressure and leakage aperture. at different monitoring positions, the hydrogen concentration has different variation patterns. hydrogen gas has a greater diffusion rate when leaking in low viscosity soil, and the leakage rate of pipelines in sandy soil is 40 times that in clay. therefore, when the pipeline is in low viscosity soil and high pressure conditions, it has a higher risk factor. in order to predict the time when the ground hydrogen concentration reaches the lower limit of hydrogen explosion under different working conditions, a ga-bp neural network model was established with numerical simulation results as training samples. the results showed that the average error of the model was only 3.3%, which was much better than traditional bp models and could reasonably predict this problem.
keywords: numerical simulation;hydrogen transmission pipeline;genetic algorithm;neural network
2024, 50(8):171-179 收稿日期: 2024-06-12;收到修改稿日期: 2024-07-19
基金项目: “大学生创新创业训练计划”项目（d202405312052544057）
作者简介: 何太碧（1970-），男，四川南充市人，教授，博士，主要从事低碳能源与交通载具的研究工作。
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