Modal parameter estimation of turbulence response based on self-attention generative model

Abstract

Modal parameter estimation of turbulence response is an important aspect of flutter test data processing. Using the modal parameter estimation results of turbulence response and the damping extrapolation curve, the flutter boundary of aircrafts can be predicted. However, owing to the randomness of atmospheric turbulence excitation, modal parameter estimation of the turbulence response has a challenge. This study analyses the turbulence response and calculates the corresponding impulse response using the seq-to-seq self-attention generative network. The encoder performs feature compression of the turbulence response, whereas the multi-head self-attention structure in the middle layer extracts the time series features. Up-sampling is performed based on the decoder to obtain the impulse response, and modal parameter estimation of the turbulence response is achieved. The self-attention generative model is validated using simulation data and the turbulence response is obtained by the wind tunnel test and the flutter flight test. The results demonstrate the feasibility and engineering applicability of the seq-to-seq generative model.