Abstract: Aiming at the problem of low data generation efficiency due to the high complexity of solving the N-S equation of smoke flow field, a deep learning model which can generate high-resolution smoke flow data based on low-resolution smoke flow data solved by N-S equation is explored and designed. Based on the Generative Adversarial Network, the smoke data reconstruction network based on the sub voxel convolution layer is constructed. Considering the fluidity of smoke, time loss based on advection step is introduced into the loss function to realize high-precision smoke simulation. By extending the image super-resolution quality evaluation index, the peak signal-to-noise ratio of smoke density data is constructed to evaluate the data quality of the reconstructed high-resolution three-dimensional smoke flow field. The experimental results show that the smoke data reconstructed by the deep learning model designed in this paper based on the low resolution data generated by the N-S equation of smoke flow field have good performance in numerical distribution, accuracy and visual effects.

Key words: deep learning, smoke simulation, super-resolution, sub voxel convolution, GAN