Reducing Parametric Uncertainty in Limit Cycle Oscillations

R. Hayes, R. Dwight, S. Marques

Research output: Contribution to conferencePaper

113 Downloads (Pure)


The assimilation of discrete higher fidelity 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 method (HDHB). 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 both a pitch/plunge aerofoil and Goland wing configuration. In both cases significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their
true deterministic values.
Original languageEnglish
Number of pages15
Publication statusPublished - Jul 2015
Event3rd ECCOMAS Young Investigators Conference (YIC) - Aachen, Germany
Duration: 22 Jul 201524 Jul 2015


Conference3rd ECCOMAS Young Investigators Conference (YIC)


  • Harmonic Balance
  • nonlinear
  • inverse problem
  • aeroelasticity
  • Bayesian Updating


Dive into the research topics of 'Reducing Parametric Uncertainty in Limit Cycle Oscillations'. Together they form a unique fingerprint.

Cite this