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Reducing parametric uncertainty in limit-cycle oscillation computational models

Published online by Cambridge University Press:  23 May 2017

R. Hayes ,
R. Dwight  and
S. Marques
R. Hayes
Affiliation:
Queen’s University Belfast, School of Mechanical & Aerospace Engineering, Belfast, Northern Ireland
R. Dwight
Affiliation:
Delft University of Technology, Aerospace Faculty, Delft, Netherlands
S. Marques*
Affiliation:
Queen’s University Belfast, School of Mechanical & Aerospace Engineering, Belfast, Northern Ireland
*
s.marques@qub.ac.uk
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Abstract

The assimilation of discrete data points with model predictions can be used to achieve a reduction in the uncertainty of the model input parameters, which generate accurate predictions. The problem investigated here involves the prediction of limit-cycle oscillations using a High-Dimensional Harmonic Balance (HDHB) method. The efficiency of the HDHB method is exploited to enable calibration of structural input parameters using a Bayesian inference technique. Markov-chain Monte Carlo is employed to sample the posterior distributions. Parameter estimation is carried out on a pitch/plunge aerofoil and two Goland wing configurations. In all cases, significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their true deterministic values. Additionally, a comparison of two approaches to extract the true values from the posterior distributions is presented.

Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1241 , July 2017 , pp. 940 - 969
Copyright
Copyright © Royal Aeronautical Society 2017 

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