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

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 11(1)

Published: Nov. 8, 2024

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

Language: Английский

Toward aerodynamic surrogate modeling based on β-variational autoencoders

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(11)

Published: Nov. 1, 2024

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using β-variational autoencoder (β-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation their latent spaces. We propose a surrogate model based on space predict pressure distributions transonic wing given flight conditions: Mach number angle attack. The β-VAE model, enhanced with principal component analysis (PCA), maps space, showing direct correlation conditions. Regularization through β requires careful tuning improve overall performance, PCA preprocessing helps construct an effective improving training performance. Gaussian process is used variables from conditions, robust behavior independent β, decoder reconstructs field This pipeline provides insight into unexplored Furthermore, fine-tuning further refines reducing dependence enhancing accuracy. Structured significant improvements collectively create highly accurate efficient model. Our methodology demonstrates effectiveness β-VAEs aerodynamic modeling, offering rapid, cost-effective, reliable alternative prediction.

Language: Английский