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 …

Accurate simulation of transition from the laminar to the turbulent flow is of great importance
in industrial applications. In the present work, the framework of field inversion and machine …

Abstract Reynolds-averaged Navier–Stokes (RANS) simulations are the most widespread
approach to predict turbulent flows typical of industrial problems. Despite its success, the …

Field inversion and machine learning are implemented in this study to describe three-
dimensional (3D) separation flow around an axisymmetric hill and augment the Spalart …

In pursuit of deriving a limited expense store trajectory prediction model, this work
investigates the application of two data-driven surrogate modeling approaches for the …

Accurate simulation of turbulent flow with separation is an important but challenging
problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach …

Axisymmetric base flow involves massive flow separation which is challenging for a typical
Reynolds-averaged Navier-Stokes (RANS) turbulence model. Furthermore, a supersonic …

A machine-learning approach for data-driven Reynolds-Averaged Navier–Stokes (RANS)
predictions of turbulent flows including estimates of turbulence modelling uncertainties is …

The constants and functions in Reynolds-averaged Navier–Stokes (RANS) turbulence
models are coupled. Consequently, modifications of a RANS model often negatively impact …

This work presents a formalism to improve the predictive accuracy of physical models by
learning generalizable model augmentations from sparse data. Building on recent advances …