Transonic Buffet

Transonic buffet, a flow instability causing shock-boundary layer interaction and potentially damaging aircraft structures, is a significant challenge in aerospace engineering. Current research focuses on developing accurate and efficient computational models, employing techniques like Bayesian neural networks, autoregressive diffusion models, and physics-assisted variational autoencoders to predict and understand this complex phenomenon. These advanced machine learning approaches aim to improve the accuracy and speed of simulations, enabling better design optimization and uncertainty quantification. Ultimately, this research strives to mitigate the risks associated with transonic buffet, leading to safer and more efficient aircraft designs.