Nonlinear Unsteady Reduced Order Models based on Computational Fluid Dynamics for Gust Loads Predictions

Abstract

A tremendous number of gust load cases needs to be computed during the aircraft design and certification process. From an aerodynamic point of view, gust loads predictions in industry rely on linear potential flow methods which are inappropriate at transonic flight conditions. Prediction accuracy can be enhanced by accounting for aerodynamic loads computed with computational fluid dynamics, eventually resulting in lighter, more efficient designs. However, full order, unsteady time-marching simulations are still prohibitively expensive in an industrial environment. Therefore, different reduced order modelling techniques have been propose to decrease computational cost in many query scenarios while retaining the underlying physics and a high level of accuracy. This paper focuses on an unsteady nonlinear reduced order model based on least squares residual minimization and a comparison to the linearized frequency domain method. While the latter is in line with current industrial practice of sampling aerodynamic forces in the frequency domain, it neglects dynamic nonlinearities which are included in the former approach. Results are presented for an airfoil at transonic flow conditions exhibiting shock induced Separation during the airfoil-gust interaction and for the NASA common research model at cruise flight conditions. Essential quantities for the gust loads analysis, such as global coefficients and sectional forces, are evaluated and compared to highlight strengths and weaknesses of both model reduction techniques. Moreover, distributed surface loads, which can be used for a direct sizing of the structural model, are analyzed. Computational cost, split in an offline and an online part, is quantified to demonstrate efficiency gains compared to full-order solutions.