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

• Published in: arXiv.org 2023-05
• Main Authors: Wang, Jing, Xie, Hairun, Zhang, Miao, Xu, Hui
• Format: Article
• Language: English
• Subjects: Aging aircraft; Boundary layers; Classifiers; Fatigue life; Feature extraction; Flight data recorders; Flow stability; Model reduction; Modelling; Neural networks; Reduced order models; Structural strength
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2305.13644

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.