The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Turbulence is still one of the main challenges in accurate prediction of reactive flows.
Therefore, the development of new turbulence closures that can be applied to combustion …

The combined effect of internal and external intermittency on the statistical properties of
small-scale turbulence is investigated in temporally evolving, planar turbulent jet flows at …

Turbulence is still one of the main challenges for accurately predicting reactive flows.
Therefore, the development of new turbulence closures which can be applied to combustion …

Modeling of turbulent flows is still challenging. One way to deal with the large scale
separation due to turbulence is to simulate only the large scales and model the unresolved …

This article summarizes and discusses recent developments with respect to artificial
intelligence (AI) super-resolution as a subfilter model for large-eddy simulations. The focus …

An analytical framework is proposed to explore the structure and kinematics of iso-scalar
fields. It is based on a two-point statistical analysis of the phase indicator field which is used …

Many complex simulations are extremely expensive and hardly if at all doable, even on
current supercomputers. A typical reason for this are coupled length and time scales in the …

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 …

This work presents the full reconstruction of coarse-grained turbulence fields in a planar
turbulent jet flow by a deep learning framework for large-eddy simulations (LES). Turbulent …