Hybrid LES/RANS for flows including separation: a new wall function using machine learning based on binary search trees

Artikel i vetenskaplig tidskrift, 2025

Machine Learning (ML) is used for developing wall functions for Improved Delayed Detached Eddy Simulations (IDDES). The ML model is based on KDTree which essentially is a fast look-up table. It searches the nearest target datapoint(s) for which y(+) and U+ are closest to the CFD y(+) and U+ cells. The target y(+) value gives the friction velocity. Two target databases - diffuser flow with opening angle alpha=15(degrees) and hump flow - are created from time-averaged data of wall-resolved IDDES (WR-IDDES, i.e. wall-adjacent cells at y(+)<1). The new ML wall function is used to predict five test cases: diffuser flow with opening angles alpha=15(degrees) and alpha=10(degrees), the hump flow, channel flow at Re-tau=16,000 and flat-plate boundary layer. A novel grid strategy is used. The wall-adjacent cells are large. But further away from the wall, the wall-normal cell distribution is identical to that of a WR-IDDES grid. This new grid is found to improve the predictions compared to a standard wall-function grid. It is found that the number of cells for a wall-resolved IDDES grid (grid stretching 15%) is a factor of 0.2ln(Re-tau) larger than that of a standard wall-functions mesh (constant wall-normal grid cells). The new ML wall function is found to perform well compared to the WR-IDDES and better than the Reichardt's wall function.