Neurofuzzy-Model-Based Unsteady Aerodynamic Computations Across Varying Freestream Conditions

摘要

This paper presents a reduced-order modeling approach based on recurrent local linear neurofuzzy models for predicting generalized aerodynamic forces in the time domain. Regarding aeroelastic applications, the unsteady aerodynamic loads are modeled as a nonlinear function of structural eigenmode-based disturbances. In contrast to established aerodynamic input/output model approaches trained by high-fidelity flow simulations, the Mach number is considered as an additional model input to account for varying freestream conditions. To train the relationship between the input parameters and the corresponding flow-induced forces, the local linear model tree algorithm is adopted in this work. The proposed method is tested exemplarily with respect to the AGARD 445.6 configuration in the subsonic, transonic, and supersonic flight regimes. It is shown that good conformity is obtained between the reduced-order model results and the respective full-order computational-fluid-dynamics solution. A further comparative analysis in the frequency domain in conjunction with a classical flutter analysis confirms the validity of the approach. Finally, the method's potential for reducing the computational effort of aeroelastic analyses is demonstrated.