Reducing parametric uncertainty in limit-cycle oscillation computational models. (23rd May 2017)

• Record Type: Journal Article
• Authors: Hayes, R.; Dwight, R.; Marques, S.
• Is Part Of: Aeronautical journal. Volume 121:Number 1241(2017)
• Page Start: 940
• Page End: 969
• Publication Date: 2017-05-23
• Subjects: Aeronautics -- Periodicals; 629.1305
• DOI: 10.1017/aer.2017.37
• Languages: English
• ISSNs: 0001-9240

Abstract

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.