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
- URL: http://arxiv.org/abs/2210.16248v1
- Date: Fri, 28 Oct 2022 16:27:14 GMT
- Title: 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
- Authors: Mathis Bode
- Abstract summary: This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for LES subfilter modeling.
It shows a successful application to a non-premixed temporal jet case.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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