Reducing Parametric Uncertainty in Limit Cycle Oscillation Computational Models

Richard Hayes, Richard Dwight, Simao Marques

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
218 Downloads (Pure)


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 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 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.
Original languageEnglish
Number of pages30
JournalThe Aeronautical Journal
Early online date23 May 2017
Publication statusEarly online date - 23 May 2017


  • Limit Cycle Oscillation
  • aeroelasticity
  • uncertainty
  • Bayesian Updating

ASJC Scopus subject areas

  • Aerospace Engineering


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

Cite this