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OA

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.