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arXiv 2210.16248physics.flu-dyncs.LG
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将物理信息增强的超分辨率生成对抗网络应用于非均匀网格上的湍流非预混燃烧和加速模拟工作流程的演示

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

Forschungszentrum Jülich GmbH(于利希研究中心);RWTH Aachen University(亚琛工业大学)

Mathis Bode

摘要:

本文扩展的方法,使用物理信息增强的超分辨率生成对抗网络(PIESRGANs)的LES子滤波器建模在湍流与有限速率化学,并显示了一个成功的应用到非预混时间射流的情况下。考虑到需要更高效和碳中性的能源设备来应对气候变化,这是一个重要的话题。多个先验和后验的结果,并进行了讨论。作为其中的一部分,强调了底层网格对预测质量的影响,并开发了一种多网格方法。它演示了如何LES的基础上PIESRGAN可以用来预测的情况下,在雷诺数没有用于培训。最后,阐述了成功预测所需的数据量。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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