  This work presents a converged framework of Machine-Learning Assisted
Turbulence Modeling (MLATM). Our objective is to develop a turbulence model
directly learning from high fidelity data (DNS/LES) with eddy-viscosity
hypothesis induced. First, the target machine-learning quantity is discussed in
order to avoid the ill-conditioning problem of RANS equations. Then, the novel
framework to build the turbulence model using the prior estimation of
traditional models is demonstrated. A close-loop computational chain is
designed to ensure the convergence of result. Besides, reasonable
non-dimensional variables are selected to predict the target learning variables
and make the solver re-converge to DNS mean flow field. The MLATM is tested in
incompressible turbulent channel flows, and it proved that the result converges
well to DNS training data for both mean velocity and turbulence viscosity
profiles.
