3D Super-Resolution Model for Vehicle Flow Field Enrichment

Thanh Luan Trinh, Fangge Chen, Takuya Nanri, Kei Akasaka

Abstract: In vehicle shape design from an 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 learning effectiveness and avoid instability during training for our enrichment task. Experimental results demonstrate that the proposed method out-performs the previous study in vehicle flow field enrichment tasks by a significant margin.