摘要：Liquid accumulation in gas pipelines will reduce transportation efficiency, increase corrosion rates, and induce severe slug flow. Calculation for critical gas velocity vcg of liquid accumulation in inclined pipelines is important for the prevention of liquid accumulation. Due to the complexity of multiphase flow, the mechanism of liquid accumulation is still controversial. Many models have been proposed based on different liquid accumulation theories, but most of these models are complex and inaccurate. It is difficult to compare the calculation results of different theories in a unified standard. To simplify the calculation and improve the accuracy, a new physics-informed neural network (PINN) for calculating vcg is proposed. PINN is trained only by the physical constraints of gas-liquid two-phase flow (GLF) and does not require any training data. In the same computational framework, PINN can calculate the vcg corresponding to minimum pressure gradient (MPG), minimum gas-liquid interface shear stress (MIS), and zero liquid-wall shear stress (ZLS), respectively. In addition, the same two empirical equations are introduced for each calculation procedure, which ensures objectivity in the evaluation of different liquid accumulation theories. With 89 collected public experimental data, PINN is compared with 3 models based on different theories, and the changing law of vcg are analyzed. The results show that PINN is applicable to a range of operating conditions with liquid superficial velocity from 0.001 to 0.100 m/s, pipe inclination from 2° to 20°, and pipe diameters from 50 to 200 mm. PINN are better than other models, and different theories have different sensitivities to each factor. This study provides a new computational method for the research of GLF and provides guidance for the prevention of liquid accumulation in gas pipelines. ? 2025

ISSN号：2667-1433

卷、期、页：卷5期3

发表日期：2025-09-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Journal of Pipeline Science and Engineering

参与作者：王鑫

通讯作者：刘珈铨,朱祚良

第一作者：张鑫儒,侯磊

论文类型：期刊论文

论文概要：张鑫儒,侯磊,王鑫,刘珈铨,朱祚良，Calculation for critical gas velocity of liquid accumulation in inclined pipelines: A method based on physics-informed neural network，Journal of Pipeline Science and Engineering，2025，卷5期3

论文题目：Calculation for critical gas velocity of liquid accumulation in inclined pipelines: A method based on physics-informed neural network