论文标题
应用物理信息增强的超分辨率生成对抗网络上的非均匀网格上的湍流非燃烧，并演示加速的仿真工作流程
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow
论文作者
论文摘要
本文扩展了使用物理知识增强的增强的超分辨率生成对抗网络（Piesrgans）进行具有有限速率化学性质的湍流中的LES子滤光器建模的方法，并显示了对非临时颞喷气案例的成功应用。考虑到需要更高效，更独立的能量设备来应对气候变化的必要性，这是一个重要的话题。提出和讨论了多个先验和后验结果。为此，强调了基础网格对预测质量的影响，并开发了多个网格方法。可以证明，如何使用基于Piesrgan的LE来预测未用于培训的雷诺数的情况。最后，详细阐述了成功预测所需的数据量。
This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for LES subfilter modeling in turbulent flows with finite-rate chemistry and shows a successful application to a non-premixed temporal jet case. This is an important topic considering the need for more efficient and carbon-neutral energy devices to fight the climate change. Multiple a priori and a posteriori results are presented and discussed. As part of this, the impact of the underlying mesh on the prediction quality is emphasized, and a multi-mesh approach is developed. It is demonstrated how LES based on PIESRGAN can be employed to predict cases at Reynolds numbers which were not used for training. Finally, the amount of data needed for a successful prediction is elaborated.
