Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate complex aerodynamics limit cycle oscillations. NSBL provides semi-analytical framework for determining data-optimal model nested within (potentially) over-parameterized model. is particularly relevant dynamical systems where modelling approaches involve use physics-based data-driven components. In such cases, components, analytical descriptions physical processes are not readily available, often prone overfitting, meaning that empirical aspects these models will calibration an unnecessarily large number parameters. While overparameterized may fit observed data well, be inadequate making predictions in regimes different from those wherein were recorded. view this, it desirable only calibrate parameters, but also optimal compromise between complexity. this article, we exhibit discovery aeroelastic system structural dynamics well-known described by differential equation model, coupled with semi-empirical aerodynamic laminar separation flutter, resulting low-amplitude oscillations (LCO). To illustrate performance algorithm, synthetic demonstrate ability correctly rediscover given known data-generating The generated forward simulation parameters selected so as mimic wind-tunnel experiments. Subsequently, selection using noisy LCO wind tunnel As there no ground truth available experimental case, provide comparison validate results, efficient alternative traditional methods.