[HTML][HTML] Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models
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 …
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 …
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
Complex turbulent flows with large-scale instabilities and coherent structures pose
challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods …
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 …
the predictive capabilities of Reynolds-averaged Navier-Stokes (RANS) turbulence models …