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

Journal article, 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.