activity
20152022
most citedUsing Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

10 citations · 36 across the 10 of their papers we have counts for

collaborators
Showing physics.flu-dynShow all

8 papers · 1 filter

physics.flu-dyn2022

Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data

Mathis Bode, Michael Gauding, Dominik Goeb +2

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 chall…

physics.flu-dyn20229 cited

Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning

Mathis Bode, Michael Gauding, Jens Henrik Göbbert +3

In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitab…

physics.flu-dyn2020

Turbulent flame speed and reaction layer thickening in premixed jet flames at constant Karlovitz and increasing Reynolds numbers

Antonio Attili, Stefano Luca, Dominik Denker +2

A series of Direct Numerical Simulations (DNS) of lean methane/air flames was conducted in order to investigate the enhancement of the turbulent flame speed and modifications to th…

physics.flu-dyn2019

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part II: Effect of Flame Structure and Geometry

Tobias Falkenstein, Hongchao Chu, Mathis Bode +2

From experimental spark ignition (SI) engine studies, it is known that the slow-down of early flame kernel development caused by the ()-property of common transporta…

physics.flu-dyn2019

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part I: Analysis of the Heat-Release-Rate Response

Tobias Falkenstein, Aleksandra Rezchikova, Raymond Langer +3

Although experimental evidence for the correlation between early flame kernel development and cycle-to-cycle variations (CCV) in spark ignition (SI) engines was provided long ago,…

physics.flu-dyn2019

Analysis of Premixed Flame Kernel/Turbulence Interactions under Engine Conditions based on DNS Data

Tobias Falkenstein, Seongwon Kang, Heinz Pitsch

Although the evolution of premixed flames in turbulence has been frequently studied, it is not well understood how small flames interact with large-scale turbulent flow motion. Sin…