NTRS - NASA Technical Reports Server

Back to Results
Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural NetworkThe present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis
Document ID
Document Type
Conference Paper
Yao, Weigang
(NASA Glenn Research Center Cleveland, OH, United States)
Liou, Meng-Sing
(NASA Glenn Research Center Cleveland, OH, United States)
Date Acquired
August 27, 2013
Publication Date
September 17, 2012
Subject Category
Aircraft Stability And Control
Report/Patent Number
AIAA Paper 2012-5446
Meeting Information
14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference(Indianapolis, IN)
Funding Number(s)
WBS: WBS 56181.
Distribution Limits

Available Downloads

There are no available downloads for this record.
No Preview Available