Using Machine Learning for formulating new wall functions for Large Eddy Simulation: A Second Attempt

Preprint, 2022

Machine Learning (ML) is used for developing wall functions for Large Eddy Simulations (LES). I use Improved Delayed Detached Eddy Simulations (IDDES) in fully-developed channel flow at a frictional Reynolds number of 5 200 to create the database. This database is used as a training set for the ML method (support vector regression). The input (i.e. the influence
parameters) is y+. The ML method is trained to predict U+.
The support vector regression methods in Python are used. The trained ML model is saved to disk and it is subsequently uploaded into the Python CFD code pyCALC-LES [1]. IDDES is carried out on coarse – and semicourse – near-wall meshes and the wall-shear stress (using the local y+ and u¯) is predicted using the developed ML model. The test cases are channel
flow at Reτ = 16 000 and flat-plate boundary layer.