  In this study, a method for predicting unsteady aerodynamic forces under
different initial conditions using a limited number of samples based on
transfer learning is proposed, aiming to avoid the need for large-scale
high-fidelity aerodynamic simulations. First, a large number of training
samples are acquired through high-fidelity simulation under the initial
condition for the baseline, followed by the establishment of a pre-trained
network as the source model using a long short-term memory (LSTM) network. When
unsteady aerodynamic forces are predicted under the new initial conditions, a
limited number of training samples are collected by high-fidelity simulations.
Then, the parameters of the source model are transferred to the new prediction
model, which is further fine-tuned and trained with limited samples. The new
prediction model can be used to predict the unsteady aerodynamic forces of the
entire process under the new initial conditions. The proposed method is
validated by predicting the aerodynamic forces of free flight of a
high-spinning projectile with a large extension of initial angular velocity and
pitch angle. The results indicatethat the proposed method can predict unsteady
aerodynamic forces under different initial conditions using 1/3 of the sample
size of the source model. Compared with direct modeling using the LSTM
networks, the proposed method shows improved accuracy and efficiency.
