耦合流动机理与运行数据的成品油管道瞬态仿真

1.中国石油大学(北京)机械与储运工程学院·油气管道输送安全国家工程研究中心·石油工程教育部重点实验室·城市油气输配技术北京市重点实验室;2.中国石油规划总院;3.国家管网集团油气调控中心

成品油管道;瞬态仿真;水击控制方程;物理信息神经网络;机理数据耦合驱动

Transient simulation of multi-product pipeline driven by flow mechanisms and operational data
DU Jian1,LI Haochong1,LIAO Qi1,LU Kaikai1,ZHENG Jianqin2,YU Xiao3

1.College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing)//National Engineering Research Center for Pipeline Safety//MOE Key Laboratory of Petroleum Engineering//Beijing Key Laboratory of Urban Oil and Gas Distribution Technology; 2.PetroChina Planning & Engineering Institute; 3.PipeChina Oil & Gas Control Center

multi-product pipeline, transient simulation, water hammer control equation, physics-informed neural network, mechanism and data coupling driven

DOI: 10.6047/j.issn.1000-8241.2024.10.009

备注

【目的】成品油管道运行工况切换频繁,因此准确监测该过程流动参数,并获取管道高、低点水力状态变化规律尤为重要。现有瞬态估计方法大多依赖准确可靠的物理模型,多工况多参数组合下需高昂的计算成本,而基于机器学习的方法又忽视了管道瞬变物理规律,可靠性、准确性不足。【方法】建立一种耦合流动机理与运行数据的成品油管道瞬态仿真PINN(Physics-Informed Neural Network)模型:首先搭建深度神经网络(Deep Neural Network,DNN)模型,构建流量、压力与管道运行时空坐标映射关系,有效提取瞬变过程中流动参数与时空坐标的非线性关联;然后分析瞬变过程中各流动参数演化的内在联系,挖掘其所遵循的瞬变控制方程与对应的初始、边界条件;最后通过深度学习自动微分构造瞬变控制方程以及初始、边界条件对应惩罚项,约束模型解至瞬变机理解空间内,提高瞬态仿真的准确性。【结果】以某仿真管道系统启输、分输、增降输工况为例验证发现,相较DNN模型,所建PINN模型对于G1管道压力预测结果的MAPE分别降低了77.4%、88.7%、87.8%,流量预测结果的MAPE分别降低了86.7%、94.4%、95.7%;以中国华南地区某成品油管道降输、增输工况为例验证发现,相较DNN模型,所建PINN模型管道压力预测结果的MAPE分别降低了94.2%、92.8%。【结论】所建PINN模型可实现不同工况、参数组合下瞬态流动参数的高效、准确求解,有助于保障成品油管道运行过程的稳定性与安全性。(图 17表 10,参[27]
[Objective] Multi-product pipelines often operate under frequently switched conditions, making it crucial to accurately monitor flow parameters during transitions and understand the changes in hydraulic states at high and low points along these pipelines. Most existing transient estimation methods rely on precise and reliable physical models, which involve high computational costs to address multi-condition and multi-parameter combinations. In contrast, approaches based on machine learning tend to lack reliability and accuracy, as they often overlook the physical patterns associated with pipeline transients. [Methods] This paper presents the development of a Physics-Informed Neural Network (PINN) model for the transient simulation of multi-product pipelines driven by flow mechanisms and operational data. First, a Deep Neural Network (DNN) model was constructed to establish mapping relationships among flows, pressures, and time-space coordinates of pipeline operation. This enables the effective extraction of nonlinear correlations between flow parameters and time-space coordinates during transients. Next, the evolution of flow parameters in transients was analyzed to explore the inherent relationships, as well as the transient control equations governing these evolutions, along with the corresponding initial and boundary conditions. Finally, the transient control equations and penalty terms associated with the initial and boundary conditions were formulated using deep learning automatic differentiation, constraining model solutions within the solution space that corresponds to the transient mechanisms, thus enhancing the accuracy of the transient simulation. [Results] The proposed model was verified based on a simulated pipeline system under start-up, offtake, and capacity increase/decrease conditions. Compared with the DNN model, the PINN model produced predictions for G1 pipeline pressures, with the mean absolute percentage error (MAPE) reduced by 77.4%, 88.7%, and 87.8%, respectively. For flow prediction results, the MAPE decreased by 86.7%, 94.4%, and 95.7%, respectively. Subsequent verification was performed using a multi-product pipeline in Southern China under capacity increase/decrease conditions. The PINN model also outperformed the DNN model, yielding prediction results for pipeline pressures with the MAPE decreasing by 94.2% and 92.8%, respectively. [Conclusion] The established PINN model facilitates efficient and accurate solutions for determining flow parameters during transients under various combinations of conditions and parameters, providing support to ensure the stability and safety of pipeline operation. (17 Figures, 10 Tables, 27 References)
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