Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

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 …

This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …

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

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

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

In this work, we present a novel data-based approach to turbulence modeling for Large
Eddy Simulation (LES) by artificial neural networks. We define the perfect LES formulation …

While data-based approaches were found to be useful for subgrid scale (SGS) modeling in
Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts …

The current generation of axial turbomachines is the culmination of decades of experience,
and detailed understanding of the underlying flow physics has been a key factor for …