This survey investigates wall modeling in large-eddy simulations (LES) using data-driven
machine-learning (ML) techniques. To this end, we implement three ML wall models in an …

Understanding turbulence rests delicately on the conflict between Kolmogorov's 1941 theory
of nonintermittent, space-filling energy dissipation characterized by a unique scaling …

Zooplankton in a quiescent environment can detect predators by hydrodynamic sensing,
triggering powerful escape responses. Since turbulent strain tends to mask the …

Research in the last decades has shown a strong interaction between near-wall turbulence
and outer-layer large-scale motions in turbulent wall-bounded flows. In this paper, we …

Existing methods to identify the interfaces separating different regions in turbulent flows,
such as turbulent/nonturbulent interfaces, typically rely on subjectively chosen thresholds …

Accurate subfilter stress modeling aids in increasing the accuracy of large-eddy simulations.
A two neural network architecture for subfilter stress modeling is proposed for its magnitude …

The understanding of the dynamics of the velocity gradients in turbulent flows is critical to
understanding various nonlinear turbulent processes. Several simplified dynamical …

The kinetic energy spectrum of high-Reynolds turbulent swirling flows is experimentally
studied. This spectrum, obtained from direct measurements in space, exhibits nearly two …

We present a local approach to the study of scale-to-scale energy transfers in
magnetohydrodynamic (MHD) turbulence. This approach is based on performing local …

Proper orthogonal decomposition (POD) is applied to distinct data sets in order to
characterize the propagation of error arising from basis truncation in the description of …