Data-driven turbulence closure using a tensor basis neural network without reliance on baseline models

We propose a novel machine-learning-based turbulence closure framework in which a tensor basis neural network (TBNN) is directly embedded into a Reynolds-averaged Navier–Stokes (RANS) formulation, eliminating reliance on traditional baseline turbulence models. The TBNN is trained to predict the Reynolds stress anisotropic tensor from local invariant inputs and geometry-informed features, including stream function and velocity potential. Its output is processed by a regression model that generates an optimised eddy viscosity field, which is then integrated into the RANS equations as a zero-equation turbulence closure. The framework is evaluated on three turbulent flows over complex geometries: a wavy-bottom channel, a smoothed step and a backward-facing step. Incorporating geometry-informed features significantly enhances model robustness, yielding numerically stable and convergent solutions across all cases. The predicted velocity fields and turbulence distributions closely match large eddy simulation (LES) data, confirming the accuracy of the proposed approach and demonstrating its ability to operate independently of conventional turbulence closures.