Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …

In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

Data-driven turbulence modeling has been considered an effective method for improving the
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …

The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …

Computational fluid dynamics using the Reynolds-averaged Navier–Stokes (RANS) remains
the most cost-effective approach to study wake flows and power losses in wind farms. The …

This study establishes a continuous adjoint data assimilation model (CADA) for the
reproduction of global turbulent mean flow from a limited number of wall pressure …

With the advent of improved computational resources, aerospace design has shifted from a
testing-based process to a simulation-driven procedure, wherein uncertainties in design and …

Data-driven turbulence modeling is receiving considerable attention specially when Direct
Numerical Simulations (DNS) are the physics-informed learning environment and Reynolds …

We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS)
simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of …