基于LabVIEW和Python的水泵数字孪生平台设计OA

[Background]Data acquisition and numerical simulation are widely used for monitoring and evaluating pump performance,but they are often disconnected in practical application.This paper presents a digital twin platform for pumps to bridge this gap,in which machine learning,computational fluid dynamics(CFD),and modal decomposition techniques are applied in an integrated manner to represent high-dimensional flow fields using low-dimensional representations,thereby improving computational efficiency.[Method]The digital twin platform was developed based on Lab VIEW and Python,consisting of four modules:data acquisition,numerical simulation,internal flow analysis,and feedback control.It collects boundary-condition data from sensors at regular temporal intervals and automatically feeds them into the simulation software for real-time computation.The platform also provides a graphical user interface to visualize internal flow fields,enabling feedback control and optimization of pump operation and maintenance.Compared with conventional monitoring systems,the platform supports real-time monitoring,efficient operation,and active regulation,while reducing maintenance costs and providing capabilities for prediction,early warning,and preventive control.As a demonstration example,the impeller was selected for transient flow simulation,in which proper orthogonal decomposition(POD)was applied to perform modal decomposition of pressure flow fields,evaluate reconstruction accuracy,and identify dominant frequencies of different modes.[Result]The pump digital twin platform was successfully applied to a pump test rig,enabling automated data acquisition and simulation.Modal decomposition results showed that the first five modes captured more than 70％of the total flow field energy,each exhibiting distinct error characteristics.Compared with high-flow conditions,low-flow conditions exhibited greater instability in the internal flow,due to flow separation in the mid-region of the impeller passage and rotor-stator interaction near the outlet.Under all operating conditions,the dominant frequencies of the first and second modes corresponded to the shaft frequency of 48.33 Hz,while high-order modes were associated with integer multiples of this frequency,likely resulting from asymmetric flow structures within the impeller.[Conclusion]The developed digital twin platform effectively integrates real-time operational data with numerical simulation.It overcomes the disconnection between physical operation and computational analysis,and significantly improves the intelligence and operational efficiency of pump systems.