The invention discloses a
water turbine local turbulence field prediction method, and relates to the technical field of water turbines. The method comprises the following steps: S1,
data acquisition and preprocessing; s2, sparse
measurement point feature extraction; s3, low-dimensional representation of the turbulent flow field; s4, carrying out double-model joint training; and S5, performing real-time prediction. According to the method, the global turbulent flow field prediction can be realized only by using the pressure / speed
time sequence data of a small number of sparse measuring points in the
water turbine runner, and an expensive full flow
field experiment table does not need to be built or a dense
sensor array does not need to be deployed. Compared with the traditional experimental measurement (the equipment cost is more than ten million yuan), the
total investment of the sensor and the computing equipment is only ten thousand yuan, and the cost is reduced by more than 99%; meanwhile, the installation difficulty of the sensor in a high-flow-speed and high-pressure area is avoided, the existing monitoring point
layout of a
hydropower station can be directly adapted, the technology landing feasibility is remarkably improved, and the
model architecture does not depend on a specific
water turbine type and can be adapted to different types such as a mixed-flow type and an axial-flow type by adjusting grid parameters and training data.