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This work presents a converged framework of Machine-Learning AssistedTurbulence Modeling (MLATM). Our objective is to develop a turbulence modeldirectly learning from high fidelity data (DNS/LES) with eddy-viscosityhypothesis induced. First, the target machine-learning quantity is discussed inorder to avoid the ill-conditioning problem of RANS equations. Then, the novelframework to build the turbulence model using the prior estimation oftraditional models is demonstrated. A close-loop computational chain isdesigned to ensure the convergence of result. Besides, reasonablenon-dimensional variables are selected to predict the target learning variablesand make the solver re-converge to DNS mean flow field. The MLATM is tested inincompressible turbulent channel flows, and it proved that the result convergeswell to DNS training data for both mean velocity and turbulence viscosityprofiles.

An Initial Attempt of Converged Machine-Learning Assisted Turbulence Modeling in RANS Simulations with Eddy-Viscosity Hypothesis