mlsanityData to 7 Oct 2026

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

arXiv 2210.16248 ↗First posted 28 Oct 2022physics.flu-dyn cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 28 Oct 2022.

Builds on

17 of 51 references.

References · 17

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5 more
  1. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Super-resolution of turbulent reacting flows on complex meshes using graph neural networkscited by 2