水利学报2026，Vol.57Issue(4)：535-545,11.DOI:10.3724/j.slxb.20250445
基于数字孪生的大型调水泵站水力机组内部流动状态预测
Prediction of the internal flow state of the hydraulic units in large pumping stations based on digital twins
摘要
Abstract
To address the limitations of traditional hydrodynamic analysis methods in large-scale water diversion proj-ects—specifically long computation cycles,high computational costs,and the inability to capture dynamic character-istics in real-time—this paper proposes a digital twin flow field state prediction method driven by a Reduced Order Model(ROM)within the Simulink environment.This study focuses on axial-flow pump units in low-head pump sta-tions.By constructing physical field ROMs and establishing a virtual-real dynamic interaction mechanism,the pro-posed method achieves millisecond-level prediction of flow field states under complex operating conditions.Further-more,a physical model test rig was constructed to conduct systematic verification.The results indicate that,as evalu-ated by the RRMSE and LOOCV methods,the average relative errors of the static pressure,velocity,and total pres-sure models established via Singular Value Decomposition(SVD)are controlled within 5％,while the average rela-tive error for turbulence intensity is 8.5％.These findings demonstrate the model's generalization capability for unknown operating conditions.Concurrently,while maintaining simulation accuracy,the average simulation time per operating point is approximately 0.1 seconds.This significant improvement in computational efficiency provides robust technical support for efficient modeling and real-time decision-making in smart water conservancy systems.关键词
轴流泵站/数字孪生/降阶模型/奇异值分解/流动状态预测Key words
axial-flow pump station/digital twin/Reduced Order Model/singular value decomposition/flow state prediction分类
建筑与水利引用本文复制引用
司乔瑞,倪愈棚,徐虎,刘斌,王超,袁寿其..基于数字孪生的大型调水泵站水力机组内部流动状态预测[J].水利学报,2026,57(4):535-545,11.基金项目
江苏省水利科技项目(2024026) （2024026）
国家重点研发计划课题(2022YFC3204603) （2022YFC3204603）
