空气动力学学报2019，Vol.37Issue(3)：462-469,8.DOI:10.7638/kqdlxxb-2019.0003
基于深度学习的非定常周期性流动预测方法
A method of unsteady periodic flow field prediction based on the deep learning
摘要
Abstract
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.关键词
深度学习/卷积神经网络/生成对抗网络/回归/非定常流场/预测Key words
deep learning/convolutional neural network/generative adversarial networks/regression/unsteady flow/prediction分类
信息技术与安全科学引用本文复制引用
HUI Xinyu,YUAN Zelong,BAI Junqiang,ZHANG Yang,CHEN Gang..基于深度学习的非定常周期性流动预测方法[J].空气动力学学报,2019,37(3):462-469,8.基金项目
国家自然科学基金(11602199) （11602199）
