4 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Incorporating basic calibrations in existing machine-learned turbulence modeling
Jiaqi J. L. Li, Yuanwei Bin, George P. Huang +1
This work aims to incorporate basic calibrations of Reynolds-averaged Navier-Stokes (RANS) models as part of machine learning (ML) frameworks. The ML frameworks considered are tens…
Constrained re-calibration of Reynolds-averaged Navier-Stokes models
Yuanwei Bin, George Huang, Robert Kunz +1
The constants and functions in Reynolds-averaged Navier Stokes (RANS) turbulence models are coupled. Consequently, modifications of a RANS model often negatively impact its basic c…
Large-eddy simulation of separated flows on unconventionally coarse grids
Yuanwei Bin, George I. Park, Yu Lv +1
We examine and benchmark the emerging idea of applying the large-eddy simulation (LES) formalism to unconventionally coarse grids where RANS would be considered more appropriate at…
A priori screening of data-enabled turbulence models
Peng E S Chen, Yuanwei Bin, Xiang I A Yang +3
Assessing the compliance of a white-box turbulence model with known turbulent knowledge is straightforward. It enables users to screen conventional turbulence models and identify a…