Using Machine Learning for formulating new wall functions for Detached Eddy Simulation
Paper in proceeding, 2023
Machine Learning (ML) is used for developing wall functions for Detached Eddy Simulations.I use Improved Delayed Detached Eddy Simulations (IDDES) in fully-developed channel flow at a frictional Reynolds number of 5200 to create the database. This database is used as a training set for the ML method (support vector regression, SVR, in Python is used). The input (i.e. the influence parameters) is y^+. The ML method is trained to predict U^+.
The trained ML model is saved to disk and it is subsequently uploaded into the Python CFD code pyCALC-LES. IDDES is carried out on coarse wall-function meshes. The wall-shear stress (using the local y^+ and u) is predicted using the developed ML model. The test cases are channel flow at Re_tau=16 000 and flat-plate boundary layer at Re_theta=2550.
The trained ML model is saved to disk and it is subsequently uploaded into the Python CFD code pyCALC-LES. IDDES is carried out on coarse wall-function meshes. The wall-shear stress (using the local y^+ and u) is predicted using the developed ML model. The test cases are channel flow at Re_tau=16 000 and flat-plate boundary layer at Re_theta=2550.
Machine Learning
wall functions
IDDES
Large eddy simultiona
Author
Lars Davidson
Chalmers, Mechanics and Maritime Sciences (M2), Fluid Dynamics
ERCOFTAC symposium on Engineering, Turbulence, Modelling and Measurements (ETMM14)
Subject Categories
Theoretical Chemistry
Fluid Mechanics and Acoustics