A nonlinear POD reduced order model for limit cycle oscillation prediction

As the amplitude of the unsteady flow oscillation is large or large changes occur in the mean background flow such as limit cycle oscillation, the traditional proper orthogonal decomposition reduced order model based on linearized time or frequency domain small disturbance solvers can not capture th...

Full description

Saved in:
Bibliographic Details
Published in:Science China. Physics, mechanics & astronomy mechanics & astronomy, 2010-07, Vol.53 (7), p.1325-1332
Main Authors: Chen, Gang, Li, YueMing, Yan, GuiRong
Format: Article
Language:English
Subjects:
Aeroelasticity
Astronomy
Classical and Continuum Physics
Decomposition
Flow equations
Limit cycle oscillations
Observations and Techniques
Physics
Physics and Astronomy
Proper Orthogonal Decomposition
Reduced order models
Research Paper
Series expansion
Solvers
Taylor series
Unsteady flow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As the amplitude of the unsteady flow oscillation is large or large changes occur in the mean background flow such as limit cycle oscillation, the traditional proper orthogonal decomposition reduced order model based on linearized time or frequency domain small disturbance solvers can not capture the main nonlinear features. A new nonlinear reduced order model based on the dynamically nonlinear flow equation was investigated. The nonlinear second order snapshot equation in the time domain for proper orthogonal decomposition basis construction was obtained from the Taylor series expansion of the flow solver. The NLR 7301 airfoil configuration and Goland+ wing/store aeroelastic model were used to validate the capability and efficiency of the new nonlinear reduced order model. The simulation results indicate that the proposed new reduced order model can capture the limit cycle oscillation of aeroelastic system very well, while the traditional proper orthogonal decomposition reduced order model will lose effectiveness.
ISSN:1674-7348
1869-1927
DOI:10.1007/s11433-010-4013-2