This paper addresses the issue of predicting separated flows with Reynolds-averaged
Navier–Stokes (RANS) turbulence models, which are essential for many engineering tasks …

Abstract Machine-learned augmentations to turbulence models can be advantageous for
flows within the training dataset but can often cause harm outside. This lack of …

Abstract The baseline Launder–Spalding k− ε model cannot be integrated to the wall. This
paper seeks to incorporate the entire law of the wall into the model while preserving the …

Field inversion machine learning (FIML) has the advantages of model consistency and low
data dependency and has been used to augment imperfect turbulence models. However …

A data-driven turbulence modeling method based on symbolic regression (SR) is proposed
in this paper to enhance the prediction accuracy of the Spalart–Allmaras (SA) model for …

Abstract Machine learning (ML) techniques have emerged as powerful tools for improving
the predictive capabilities of Reynolds-averaged Navier-Stokes (RANS) turbulence models …

The conventional k-ε model accurately predicts the slope of the logarithmic law but falls short
in estimating its intercept as well as the buffer layer. This limitation can be addressed either …

The object of this study was the motion of an ultralight class variable-length launch vehicle
made of a polymer body along the active phase of the trajectory. The work considers the …

With the growing emphasis on energy efficiency and emission reductions, the
turbomachinery industry faces the dual objective of improving performance and integrating …