基于深度学习的非定常周期性流动预测方法

In order to overcome the shortages of the computationally expensive and time-consuming iterative process in traditional CFD simulation, a framework based on the deep learning to predict periodic unsteady flow field is proposed, which can accurately predict real-time complex vortex flow state at different moments. The conditional generative adversarial network and convolutional neural network are combined to improve the conditional constraint method from conditional generative adversarial network. The improved regression generative adversarial network based on the deep learning is proposed. The two scenarios of conditional generative adversarial network and regression generative adversarial network are tested and compared via giving different periodic moments to predict the corresponding flow field variables. The final results demonstrate that regression generative adversarial network can estimate complex flow fields, and is faster than traditional CFD simulation over one order of magnitudes.