Hauptseite > Publikationsdatenbank > Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C > print

001     917411
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024 7 _ |a 10.4271/2022-01-0503
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037 _ _ |a FZJ-2023-00625
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245 _ _ |a Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C
260 _ _ |a Warrendale, Pa.
|c 2022
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520 _ _ |a Large-eddy simulation (LES) is an important tool to understand and analyze sprays, such as those found in engines. Subfilter models are crucial for the accuracy of spray-LES, thereby signifying the importance of their development for predictive spray-LES. Recently, new subfilter models based on physics-informed generative adversarial networks (GANs) were developed, known as physics-informed enhanced super-resolution GANs (PIESRGANs). These models were successfully applied to the Spray A case defined by the Engine Combustion Network (ECN). This work presents technical details of this novel method, which are relevant for the modeling of spray combustion, and applies PIESRGANs to the ECN Spray C case. The results are validated against experimental data, and computational challenges and advantages are particularly emphasized compared to classical simulation approaches.
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536 _ _ |a CoEC - Center of Excellence in Combustion (952181)
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536 _ _ |a Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20190501)
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|f Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
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773 _ _ |a 10.4271/2022-01-0503
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|t SAE international journal of advances and current practices in mobility
|v 4
|y 2022
|x 2641-9637
856 4 _ |u https://juser.fz-juelich.de/record/917411/files/Invoice_20216526-1.pdf
856 4 _ |u https://juser.fz-juelich.de/record/917411/files/2022-01-0503.pdf
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|v Enabling Computational- & Data-Intensive Science and Engineering
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