The interaction between the shock wave and boundary layer of transonic wings can trigger periodic self-excited oscillations, resulting in transonic buffet. Buffet severely restricts the flight envelope of civil aircraft and is directly related to their aerodynamic performance and safety. Developing efficient and reliable techniques for buffet onset prediction is crucial for the advancement of civil aircraft. In this study, utilizing a comprehensive database of supercritical airfoils generated through numerical simulations, a convolutional neural network (CNN) model is firstly developed to perform buffet classification based on the flow fields. After that, employing explainable machine learning techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), random forest algorithms, and statistical analysis, the research investigates the correlations between supervised CNN features and key physical characteristics related with the separation region, shock wave, leading edge suction peak, and post-shock loading. Finally, physical buffet onset metric is established with good generalization and accuracy, providing valuable guidance for engineering design in civil aircraft.

冲击波与跨音速翼边界层之间的相互作用可以触发周期性的自激振荡，从而导致跨音速冲击。巴菲特严重限制了民用飞机的飞行包线，直接关系到其空气动力学性能和安全性。开发高效可靠的 buffet onset 预测技术对于民用飞机的发展至关重要。在本研究中，利用通过数值模拟生成的超临界翼型的综合数据库，首先开发了一种卷积神经网络 （CNN） 模型，用于执行基于流场的自助餐分类。之后，采用可解释的机器学习技术，包括梯度加权类激活映射 （Grad-CAM）、随机森林算法和统计分析，该研究调查了监督 CNN 特征与与分离区域、冲击波、前缘吸力峰值和冲击后载荷相关的关键物理特性之间的相关性。最后，建立了具有较好泛化性和准确性的物理 buffet 起始度量，为民用飞机的工程设计提供了有价值的指导。