Improving Turbulence Models and Wall Functions by Using Machine Learning

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.I use Support Vector Regression and Nearest Neighbor(s), both available in Python.The input (i.e. the influence parameters) is y +.The ML method is trained to predict U +.The trained ML model (SVR) is saved to disk and it is subsequently uploaded into the Python CFD code pyCALC-LES 1 .SVR finds a time-averaged regression line.As an alternative I also investigate Nearest neighbor (uploading the database to pyCALC-LES) using Python's scipy.spatial.KDTree.This method capture the unsteadiness of U +, see Fig. 22 in this report2 .IDDES is then carried out on coarseand semi-coursenear-wall meshes and the wall-shear stress (using the local y + and u¯) is predicted using the developed ML models.The test cases are channel flow at Reτ = 16 000 and flat-plate boundary layer.I'm currently extending the method to adverse-pressure gradient flows.I have created a number of databases using well-resolved LES in diffuser flows with opening angles 6 o ≤ α ≤ 18o .The influence parameters are local u¯ and y + as well as the non-dimensionalized pressure gradient, p +.I use Neural Network (pytorch in Python).Finally, I will present some preliminary work on how to use Neural Network (pytorch) in Python to improve the prediction of the Reynolds stresses in non-linear eddy-viscosity and algebraic Reynolds stress turbulence models.