期刊
AEROSPACE SCIENCE AND TECHNOLOGY
卷 84, 期 -, 页码 880-894出版社
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ast.2018.11.014
关键词
Reduced-order model; Hybrid modeling; Aeroelasticity; Neural networks; Unsteady aerodynamics
资金
- National Natural Science Foundation of China [11572252]
- National Science Fund for Excellent Young Scholars [11622220]
- 111 project of China [B17037]
- ATCFD project [2015-F-016]
This paper develops a hybrid and parallel-structured reduced-order framework for modeling unsteady aerodynamics, which incorporates both linear and nonlinear system identification methods. To reflect unsteady flow physics, the hybrid model introduces time-delayed output feedback to both linear and nonlinear subsystems. The linear output and nonlinear residual are identified by the autoregressive with exogenous input model and the multi-kernel neural network, respectively. The proposed approach is illustrated here with the reduction of computational-fluid-dynamics-based aeroelastic analysis of a NACA0012 airfoil oscillating in transonic and viscous flows. In particular, we exploit the potential of this model in analyzing complex aeroelastic phenomena including limit-cycle oscillations, the beat phenomenon at high reduced velocities, and nodal-shaped oscillations induced by the interaction between buffet and flutter. Results demonstrate that the proposed approach approximates the dynamically linear and nonlinear aerodynamic characteristics obtained from high-fidelity time-marching methods with a high level of accuracy. This framework can be used as a general reduced-order modeling strategy to represent dynamic systems exhibiting both linear and nonlinear characteristics. (C) 2018 Elsevier Masson SAS. All rights reserved.
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