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3D Super-Resolution Model for Vehicle Flow Field Enrichment
In vehicle shape design from aerodynamic performance perspective, deep learning methods enable us to estimate the flow field in a short period. However, the estimated flow fields are generally coarse and of low resolution. Therefore, a super-resolution model is required to enrich them. In this study, we propose a novel super-resolution model to enrich the flow fields around the vehicle to a higher resolution. To deal with the complex flow fields of vehicles, we apply the residual-in-residual dense block (RRDB) as the basic network-building unit in the generator without batch normalization. We then apply the relativistic discriminator to provide better feedback regarding the lack of high-frequency components. In addition, we propose a distance-weighted loss to obtain better estimation in wake regions and regions near the vehicle surface. Physics-informed loss is used to help the model generate data that satisfies the physical governing equations. We also propose a new training strategy to improve the leaning effectiveness and avoid instability during training. Experimental results demonstrate that the proposed method outperforms the previous study in vehicle flow field enrichment tasks by a significant margin.