Nonlinear Unsteady Reduced-Order Modeling for Gust-Load Predictions

A tremendous number of gust-load cases need to be computed during the aircraft design and certification process. From an aerodynamic point of view, gust-load 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 modeling techniques have been proposed to decrease the computational cost. 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. Although 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 and the NASA Common Research Model at transonic flow conditions exhibiting shock-induced separation during the airframe–gust interaction. Essential quantities for the gust-load analysis, such as global coefficients, sectional forces, and distributed quantities, are evaluated and compared to highlight the strengths and weaknesses of both model reduction techniques.