Abstract: The rapid and accurate prediction of compressor performance and key flow field characteristics is critical for digital design, digital twin modeling, and virtual-real interaction of compressors. This study focuses on the first-stage stator of a transonic three-stage compressor. A flow field database was established using high-precision numerical simulation results for 205 different operating conditions at various speeds. We employed a symmetric convolutional neural network with attention mechanisms to reconstruct flow field parameters such as static temperature, static pressure, and Mach number at different radii of the stator blade. This network comprises a visual self-attention model and symmetric convolutional neural network. The former extracts geometric features of blade passages at different radii under various operating conditions, while the latter con-ducts deeper feature extraction of these features and other inputs (including inlet and outlet boundary conditions, flow field coordinates, and distance fields) through transposed convolution and convolution operations. Consequently, it predicts the flow field at different radial positions. Our findings demonstrate that the symmetric convolutional neural network based on deep attention can rapidly and effectively predict the internal flow field of compressors. The model accurately captures changes in physical parameters near the blade wall, blade wakes, and flow separation. Compared with high-precision numerical simulation, the model achieves rapid and accu-rate reconstruction of the flow field of compressor stators with an average relative error not exceeding 1%.

Key words: compressor stator, deep learning, visual self-attention mechanism, symmetric convolution, flow field reconstruction