Using a Proper Orthogonal Decomposition representation of the aerodynamic forces for stochastic buffeting prediction

Abstract

High-performance aircraft often suffer from the consequences of tail buffeting at moderate subsonic Mach numbers and medium to high angles of attack. The impact of the aircraft’s highly unsteady flow field on the tails can result in significant structural fatigue and degraded handling qualities. Various methods have been developed to predict tail buffeting. Stochastic response methods are among frequently used approaches. For such methods the size of the excitation data set can become an issue, especially when the auto- and cross-spectra of all available excitation signals on the configuration are considered. The present paper demonstrates how to modify stochastic tail buffeting prediction methods using Proper Orthogonal Decomposition (POD). The approach is based on the modal decomposition of the aerodynamic buffet excitation data set. It notably reduces the computational effort for structural response and loads prediction with limited losses in accuracy while using all power- and cross-spectra of the reduced dataset. The method was applied to the computational buffeting prediction for a generic configuration with double-delta wing and horizontal tail plane (HTP) over a wide range of angles of attack. It was shown that the POD-modes of the aerodynamic buffet excitation resembled the characteristics of configuration’s complex vortical flow field. The predicted structural response and loads converged well with increasing number of POD-modes. With the presented approach, the computational effort of stochastic tail buffeting prediction has been reduced by orders of magnitude compared to the case with the full aerodynamic buffet excitation data set.