Unet-based flow field prediction model training method and device, computer device and storage medium

Abstract

The application relates to a UNet-based flow field prediction model training method and device, computer equipment and a storage medium. The method comprises the following steps: constructing grid data of a target cascade, and generating simulation flow field data corresponding to the target cascade according to the grid data and working condition conditions; inputting the grid data and the working condition conditions into a flow field prediction model constructed based on UNet to obtain predicted flow field data; determining a first loss value according to the gradient difference between the predicted flow field data and the simulation flow field data, determining a second loss value according to the difference between the predicted flow field data and the simulation flow field data of the surface area of each blade in the target cascade, and determining a target loss value according to the first loss value and the second loss value; and adjusting the flow field prediction model according to the target loss value to obtain a trained flow field prediction model. The method can improve the prediction accuracy of the model.

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