Machine-learning-augmented domain decomposition method for near-wall turbulence modeling

In this work, we developed a novel framework for incorporating the near-wall non-overlapping domain decomposition (NDD) method with the machine learning technique. It allows the solution to be calculated with a Robin-type (slip) wall boundary condition on a relatively coarse mesh and then be corrected in the near-wall region by solving the thin boundary-layer equations on a fine subgrid. Through an estimated turbulent viscosity profile provided by a neural network, the proposed method can be easily extended to different turbulence models and achieve commendable accuracy for the test cases of turbulent wall-bounded flows at various Reynolds numbers.