Physics-Assisted Reduced-Order Modeling for Identifying Dominant Features of Transonic Buffet

TL;DR

This paper introduces a physics-assisted variational autoencoder that effectively identifies dominant features of transonic buffet, enabling accurate buffet prediction and providing insights into flow physics for aerodynamic design.

Contribution

The study develops a novel physics-assisted reduced-order model that combines unsupervised learning with physical constraints to improve buffet prediction and interpretability.

Findings

Buffet state can be accurately determined with a single latent space.

The dominant latent features correlate with key boundary layer flow structures.

The proposed metric achieves 98.5% accuracy in buffet classification.

Abstract

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.