CACFD:Channel Attention for Computational Fluid Dynamics Stimulation

Computational Fluid Dynamics (CFD) simulations are essential for predicting fluid behavior in various engineering applications. However, traditional CFD approaches based on the numerical solution of the Navier-Stokes equations can be computationally expensive and time-consuming. To address these challenges, we propose a novel deep learning model, CACFD, which leverages Channel Attention mechanisms within an autoencoder-like architecture to efficiently simulate fluid dynamics. Our approach focuses on improving the representation of complex flow patterns by selectively emphasizing relevant features in the channel dimension, enhancing the model's ability to approximate the solutions to the Navier-Stokes equations. We demonstrate that CACFD achieves significant speedups over conventional CFD methods while maintaining high accuracy. Through extensive experiments, we show that CACFD not only reduces computational cost but also preserves the fidelity of velocity and pressure fields, making it a promising alternative for real-time fluid dynamics simulation.