A neural network that corrects RANS turbulence models using velocity-aligned, scale-normalized local stencils transfers from one periodic hill training case to other hill geometries and Reynolds numbers, cutting mean-velocity prediction errors substantially.
DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression.Flow, Turbulence and Combustion, 112(1):217–241, January 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.flu-dyn 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Non-Linear Super-Stencils for Turbulence Model Corrections
A neural network that corrects RANS turbulence models using velocity-aligned, scale-normalized local stencils transfers from one periodic hill training case to other hill geometries and Reynolds numbers, cutting mean-velocity prediction errors substantially.