[HTML][HTML] Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models

Y Bin, X Hu, J Li, SJ Grauer, XIA Yang - Theoretical and Applied Mechanics …, 2024 - Elsevier
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 …

Turbulence closure modeling with machine learning: A foundational physics perspective

SS Girimaji - New Journal of Physics, 2024 - iopscience.iop.org
Turbulence closure modeling using (ML) is at an early crossroads. The extraordinary
success of ML in a variety of challenging fields had given rise to an expectation of similar …

Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling

S Taghizadeh, FD Witherden, SS Girimaji - Physics of Fluids, 2024 - pubs.aip.org
Complex turbulent flows with large-scale instabilities and coherent structures pose
challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods …

[HTML][HTML] Field Inversion and Machine Learning Based on the Rubber-Band Spalart-Allmaras Model

W Chenyu, Z Yufei - Theoretical and Applied Mechanics Letters, 2024 - Elsevier
Abstract Machine learning (ML) techniques have emerged as powerful tools for improving
the predictive capabilities of Reynolds-averaged Navier-Stokes (RANS) turbulence models …