Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering

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...

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Bibliographic Details
Published in:Complex & intelligent systems 2025-01, Vol.11 (1), p.3-18, Article 3
Main Authors: Wang, Jing, Liu, Wei, Xie, Hairun, Zhang, Miao
Format: Article
Language:English
Subjects:
Aerodynamic design
Aircraft
Aircraft guidance
Aircraft performance
Algorithms
Artificial neural networks
Boundary layers
Complexity
Computational Intelligence
Data Structures and Information Theory
Deep learning
Design engineering
Engineering
Explainable machine learning
Flight envelopes
Machine learning
Original Article
Physical properties
Shock loading
Shock waves
Statistical analysis
Suction
Supercritical airfoils
Transonic buffet
Wings (aircraft)
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Summary: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.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01612-y