期刊
AEROSPACE SCIENCE AND TECHNOLOGY
卷 113, 期 -, 页码 -出版社
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ast.2021.106652
关键词
Nonlinear system identification; Reduced-order model; Long short-term memory neural network; Buffet aerodynamics; Computational fluid dynamics
资金
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [BR1511/11-1]
- Gauss Centre for Supercomputing e.V.
In this study, a reduced-order modeling framework based on long short-term memory neural network is applied to predict transonic buffet aerodynamics, successfully capturing the essential characteristics of the nonlinear buffet phenomenon.
In the present work, a reduced-order modeling (ROM) framework based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. This type of network has a high potential for modeling sequential data, which is favorable for capturing the time-delayed effects associated with unsteady aerodynamics. Therefore, the nonlinear identification procedure as well as the generalization of the resulting ROM are presented. Further, a Monte-Carlo-based training procedure is performed in order to estimate statistical errors. The training data set for the ROM is provided by means of forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulation. Subsequent to the training process, the ROM is applied for the computation of time-varying integral quantities such as aerodynamic force and moment coefficients. The most challenging aspect when considering buffet aerodynamics is given by the reproduction of the self-sustained unsteadiness of the buffeting flow. Even without any external excitation, the flow is characterized by large shock-boundary layer interaction, resulting in shock movement and flow separation. Finally, the performance of the trained network is demonstrated by predicting the aerodynamic loads of the NACA0012 airfoil considered at transonic freestream conditions. Therefore, the airfoil is excited by a forced pitching motion beyond the buffet-critical angle of attack. A comparison with a full-order computational fluid dynamics (CFD) solution shows that the essential characteristics of the nonlinear buffet phenomenon are captured by the ROM method. (C) 2021 Elsevier Masson SAS. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据