Uncertainty analysis of geometric deviation effects on compressor stage performance using explainable neural networks

Although the geometric manufacturing deviations of compressor blades are typically small in magnitude (generally on the order of millimeters), they can significantly disturb the flow field structure, leading to notable performance degradation. To investigate the uncertainty in compressor stage performance caused by deviations in leading-edge radius, this study develops a high-precision gated recurrent unit-Kolmogorov-Arnold network (GRU-KAN) neural network surrogate model. The model output is interpreted using SHapley additive exPlanations and accumulated local effects methods to provide multidimensional insights. Results indicate that the combined presence of leading-edge radius deviations in both rotor and stator blades has the most pronounced impact on compressor performance, particularly under peak-efficiency conditions where the sensitivity is heightened. Moreover, deviations at the 50% span of the rotor blade exhibit the greatest influence on both stage performance and flow structure, with effects propagating across adjacent spans. In contrast, leading-edge radius deviations at 95% span primarily induce localized disturbances in the flow field. Under near-stall conditions, the impact of stator leading-edge radius deviations across various spans becomes more prominent, especially in terms of their contribution to total pressure ratio prediction. Overall, leading-edge radius deviations impose systematic and cumulative negative effects on compressor performance. As the deviation amplitude increases, the performance deterioration becomes increasingly severe.