Aerodynamic data fusion for low-Reynolds-number compressors based on film-Re physics-guided multi-fidelity network

Strong three-dimensional effects within low Reynolds number compressor cascades invalidate the assumption of turbulence isotropy, significantly reducing the accuracy of solving the Navier–Stokes equations using Reynolds-averaged Navier–Stokes (RANS) methods. Although large eddy simulation (LES) maintains high accuracy at low Reynolds numbers, it comes with substantially higher computational costs. Therefore, this study integrates RANS and LES samples to construct a variable-Re multi-fidelity dataset. Based on this, a film-Re physics-guided multi-fidelity network (FP–MFN) is developed to predict flow fields at Reynolds numbers beyond those used in the training set. The FP–MFN model consists of a low-fidelity network and a high-fidelity network, which are coupled in the loss function via a fidelity fusion coefficient. When the fidelity fusion coefficient is set to 0.5, the prediction demands of the two models reach an optimal balance, yielding the highest prediction accuracy. The FP–MFN model incorporates a Reynolds number modulation network in part of its hidden layers, enhancing its ability to learn the relationships between flow field structures and Reynolds number variations. This design substantially enhances the prediction accuracy of flow fields at Reynolds numbers unseen during training compared with conventional multi-fidelity networks. Furthermore, the FP–MFN incorporates a physics-guided loss term into the loss function, ensuring the physical consistency of predicted flow fields under previously unseen Reynolds conditions. The FP–MFN model accurately predicts flow separation in low-Reynolds-number compressor cascades under different loading conditions, highlighting its strong generalization capability.