12 citations · 23 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2023★ 11 cited
Assessment of flamelet manifolds for turbulent flame-wall interactions in Large-Eddy Simulations
Yujuan Luo, Matthias Steinhausen, Driss Kaddar +2
A turbulent side-wall quenching (SWQ) flame in a fully developed channel flow is studied using Large-Eddy Simulation (LES) with a tabulated chemistry approach. Three different flam…
physics.flu-dyn2023★ 12 cited
Application of dense neural networks for manifold-based modeling of flame-wall interactions
Julian Bissantz, Jeremy Karpowski, Matthias Steinhausen +5
Artifical neural networks (ANNs) are universal approximators capable of learning any correlation between arbitrary input data with corresponding outputs, which can also be exploite…