Autonomous underwater vehicles play an essential role in geophysical data collection, deep
water mining, seafloor mapping, ocean exploration, and in many other related activities …

Most investigations of turbulent flows in academic studies and industrial applications use
turbulence models. Out of the different turbulence modeling approaches Reynolds stress …

The application of machine learning (ML) algorithms to turbulence modeling has shown
promise over the last few years, but their application has been restricted to eddy viscosity …

A recalcitrant problem in the physics of turbulence is the representation of the tendency of
large-scale anisotropic eddies to redistribute their energy content with decreasing scales, a …

In this paper, we consider the evolution of decaying homogeneous anisotropic turbulence
without mean velocity gradients, where only the slow pressure rate of strain is nonzero. A …

In the presence of mean strain or rotation, the anisotropy of turbulence increases due to the
rapid pressure strain term. In this paper, we consider the modeling of the rapid pressure …

This research study delves into the realm of enhancing the Reynolds stress model through
the remarkable capabilities of machine learning. Focusing on two key aspects, we …

Homogeneous anisotropic turbulence has been widely studied in the past decades, both
numerically and experimentally. Shear flows have received a particular attention because of …

One of the main features of near-neutral atmospheric boundary layer (ABL) turbulence is the
positive vertical velocity skewness S kw above the roughness sublayer or the buffer region …

In this article, the mechanics of drag reduction on an axisymmetric body of revolution by
shallow dimples is presented by using the high-fidelity Reynolds Stress Modeling based …