An attention-enhanced Fourier neural operator model for predicting flow fields in turbomachinery Cascades

Flow field information within cascades is crucial for refined turbomachinery design. Currently, this information is primarily obtained through experimental methods or numerical simulations, both of which are complex and time-consuming. Data-driven deep learning approaches offer a potential solution for rapid flow field evaluation. However, existing deep learning-based flow field prediction models exhibit certain limitations in accuracy and generalization, particularly in regions with high gradients, which are often the primary sources of aerodynamic losses. To address these issues, this study develops a high-precision cascade flow field prediction model, A-FNO, based on a Galerkin-type self-attention mechanism and Fourier Neural Operator (FNO). A-FNO is designed based on the newly proposed FNO, which has demonstrated excellent performance in solving partial differential equations. This study extends its application to cascade flow field prediction problems. To mitigate the limitations of FNO in predicting areas with steep gradient changes, we incorporate the self-attention mechanism to capture dependencies between different regions of the flow field, thereby enhancing FNO's ability to express flow field details. Experimental results demonstrate that A-FNO significantly improves prediction accuracy in regions surrounding the boundary layer. The maximum relative error for velocity field predictions is within 5%, for pressure field predictions within 2%, and for temperature field predictions within 1%.