摘要：In addressing the hydraulic simulation of hydrogen-blended pipeline transportation, this paper establishes a physics-informed neural network (PINN) model for solving the physical field of hydraulic transport in hydrogen-blended pipelines. Through data normalization, the model effectively avoids the issue of multi-scale dimensional gradient conflicts during neural network training in hydraulic simulations. Case studies in classic transient gas pipeline simulations demonstrate that the proposed model achieves small errors in predicting flow rate and pressure. ? 2024 IEEE.

ISSN号：9798331529147

卷、期、页：页62-66

发表日期：2024-01-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：2024 2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

通讯作者：马杰,何宇轩

第一作者：苏怀,张劲军

论文类型：会议论文

论文概要：马杰,何宇轩,苏怀,张劲军，A Transient Simulation Framework for Hydrogen Blending Pipeline Transportation with Physics-Informed Neural Networks，2024 2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024，2024，页62-66

论文题目：A Transient Simulation Framework for Hydrogen Blending Pipeline Transportation with Physics-Informed Neural Networks