Multi-fidelity graph neural network for flow field data fusion of turbomachinery

Efficient and accurate prediction of the flow field in turbomachinery is vital for tasks such as optimization and off-design modeling. Deep learning methods offer inspiring tools for flow field prediction when there is sufficient high-fidelity data for training. However, high-fidelity flow fields may be insufficient in practice due to the high computational/experimental cost. In this work, the capabilities of deep learning methods for fusing multi-fidelity flow field data are further explored. A multi-fidelity graph neural network (MFGNN) is proposed. The proposed framework contains two networks for approximating the low-fidelity flow fields and the correlations between the low-fidelity and high-fidelity flow fields, respectively. The data fusion method is validated by the off-design flow field prediction of a turbine. With limited high-fidelity data, MFGNN can accurately predict flow fields and is superior to the graph neural network that only uses high-fidelity data. The effects of low-fidelity dataset size and the extrapolation performance are also explored. With appropriate prior guidance by low-fidelity data, MFGNN can predict unknown flow fields within and beyond the range of high-fidelity training datasets. The proposed deep learning method shows the advantages of high precision and generalizability in addressing the physical field prediction problem.