Physics-Assisted Reduced-Order Modeling for Identifying Dominant
  Features of Transonic Buffet
Jing Wang Hairun Xie Miao Zhang Hui Xu
Transonic buffet is a flow instability phenomenon that arises from the
interaction between the shock wave and the separated boundary layer. This flow
phenomenon is considered to be highly detrimental during flight and poses a
significant risk to the structural strength and fatigue life of aircraft. Up to
now, there has been a lack of an accurate, efficient, and intuitive metric to
predict buffet and impose a feasible constraint on aerodynamic design. In this
paper, a Physics-Assisted Variational Autoencoder (PAVAE) is proposed to
identify dominant features of transonic buffet, which combines unsupervised
reduced-order modeling with additional physical information embedded via a
buffet classifier. Specifically, four models with various weights adjusting the
contribution of the classifier are trained, so as to investigate the impact of
buffet information on the latent space. Statistical results reveal that buffet
state can be determined exactly with just one latent space when a proper weight
of classifier is chosen. The dominant latent space further reveals a strong
relevance with the key flow features located in the boundary layers downstream
of shock. Based on this identification, the displacement thickness at 80%
chordwise location is proposed as a metric for buffet prediction. This metric
achieves an accuracy of 98.5% in buffet state classification, which is more
reliable than the existing separation metric used in design. The proposed
method integrates the benefits of feature extraction, flow reconstruction, and
buffet prediction into a unified framework, demonstrating its potential in
low-dimensional representations of high-dimensional flow data and interpreting
the "black box" neural network.