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This article presents a hybrid CFD-deep learning framework that combines a Convolutional Autoencoder (CAE) and a Deep Neural Network (DNN) to rapidly predict urban wind fields for UAV path planning. The model achieves a computational speed-up of approximately 4000 times compared to traditional CFD methods, enabling real-time applications on embedded hardware. The approach effectively captures velocity and turbulence structures, facilitating turbulence-aware trajectory planning for UAVs in complex urban envi

Author

wangtengfei2020

Language

EN