This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2024-00885 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.proci.2022.07.254 in citations.
Applying physics-informed enhanced super-resolution generative adversarial networks to turbulent premixed combustion and engine-like flame kernel direct numerical simulation data
Applying physics-informed enhanced super-resolution generative adversarial networks to turbulent premixed combustion and engine-like flame kernel direct numerical simulation data
Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more challenging if turbulence is involved. This work advances the recently developed PIESRGAN modeling approach to turbulent prem...
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Personal Name(s): | Bode, Mathis (Corresponding author) |
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Gauding, Michael / Goeb, Dominik / Falkenstein, Tobias / Pitsch, Heinz | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: | Proceedings of the Combustion Institute, 39 (2023) 4, S. 5289-5298 |
Imprint: |
New York, NY [u.a.]
Elsevier ScienceDirect
2023
|
DOI: |
10.34734/FZJ-2024-00885 |
DOI: |
10.1016/j.proci.2022.07.254 |
Document Type: |
Journal Article |
Research Program: |
Center of Excellence in Combustion Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.proci.2022.07.254 in citations.
Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more challenging if turbulence is involved. This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. For that, the physical information processed by the network and considered in the loss function are adjusted, the training process is smoothed, and especially effects from density changes are considered. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel. The limits of the modeling approach are discussed. Finally, the model is employed to compute further realizations of the premixed flame kernel, which are analyzed with a scale-sensitive framework regarding their cycle-to-cycle variations. The work shows that the data-driven PIESRGAN subfilter model can very accurately reproduce direct numerical simulation data on much coarser meshes, which is hardly possible with classical subfilter models, and enables studying statistical processes more efficiently due to the smaller computing cost. |