Convolutional neural network-based optimization model for bird flight aerodynamic simulation

This study analyzes the aerodynamics of fluttering flight of birds through their body structure characteristics.A convolutional neural network is combined with a bird-like flight aerodynamic model.By analyzing the symmetric and asymmetric motion laws of birds in flight, the three-dimensional model and equations of motion of the wing-fluttering motion are established, the aerodynamic simulation study of bird wing-fluttering flight under Computational Fluid Dynamics(CFD) and train it by convolutional neural network.When the model trained to 12 rounds, the loss values on both the training and validation sets converge to about 3.5%, the training effect is good.The predicted values of the lift-to-drag ratio by the model in this paper are close to the CFD calculated values, and the average relative errors of the validation set test set are 0.483% and 0.486%, respectively.In addition, the model predicts the pressure coefficient of the flow field better, and the prediction error of the vast majority of the positions is less than 1.2%.In conclusion, the convolutional neural network can significantly improve the performance of bird flight aerodynamic simulation model.