Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations
A Bayesian model selection and parameter estimation algorithm is applied to investigate the influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) of a pitching airfoil in the transitional Reynolds number regime. At small angles of attack, laminar boundary layer trailing edge separation causes negative aerodynamic damping leading to the LCO. The fluid-structure interaction of the rigid, but elastically mounted, airfoil and nonlinear unsteady aerodynamics is represented by two coupled nonlinear stochastic ordinary differential equations containing uncertain parameters and model approximation errors. Several plausible aerodynamic models with increasing complexity are proposed to describe the aeroelastic system leading to LCO. The likelihood in the posterior parameter probability density function (pdf) is available semi-analytically using the extended Kalman filter for the state estimation of the coupled nonlinear structural and unsteady aerodynamic model. The posterior parameter pdf is sampled using a parallel and adaptive Markov Chain Monte Carlo (MCMC) algorithm. The posterior probability of each model is estimated using the Chib-Jeliazkov method that directly uses the posterior MCMC samples for evidence (marginal likelihood) computation. The Bayesian algorithm is validated through a numerical study and then applied to model the nonlinear unsteady aerodynamic loads using wind-tunnel test data at various Reynolds numbers.
|Keywords||Bayesian inference, Bayesian model selection, Kalman filter, Limit cycle oscillation, Markov Chain Monte Carlo simulation, Nonlinear aeroelasticity, Unsteady aerodynamics|
|Journal||Journal of Computational Physics|
Sandhu, R. (Rimple), Poirel, D. (Dominique), Pettit, C. (Chris), Khalil, M. (Mohammad), & Sarkar, A. (2016). Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations. Journal of Computational Physics, 316, 534–557. doi:10.1016/j.jcp.2016.03.006