Data-driven Symbolic Closure for Turbulence Modeling in the Lattice Boltzmann Framework

TL;DR

This paper introduces a novel data-driven symbolic closure model for turbulence in the Lattice Boltzmann Method, outperforming traditional models and generalizing well to different flow scenarios.

Contribution

It develops a physics-informed symbolic regression approach that discovers explicit turbulence closures from high-fidelity data, integrating physical scaling laws directly into the model.

Findings

Outperforms Smagorinsky model in energy dissipation prediction

Successfully captures secondary vortices in complex flows

Generalizes to wall-bounded turbulence without additional tuning

Abstract

Turbulence modeling within the Lattice Boltzmann Method (LBM) framework has long relied on traditional algebraic sub-grid scale (SGS) models, which often suffer from over-dissipation and lack of spatial selectivity near solid boundaries. In this work, we utilize Physical Symbolic Optimization (Phi-SO) to discover explicit analytical closures from high-fidelity DNS datasets of Taylor-Green Vortex (TGV) and Lid-Driven Cavity (LDC) flows. Central to our methodology is the integration of virtual dimensional analysis and non-linear tensor invariants, a strategy that enforces physical scaling laws directly within the symbolic search process. The resulting model exhibits a highly non-linear dependency on both strain-rate and rotation-rate invariants. Numerical validations confirm that this symbolic closure outperforms the standard Smagorinsky approach in capturing kinetic energy dissipation rate peaks and resolving delicate secondary corner vortices. Furthermore, the model exhibits robust zero-shot generalization to wall-bounded turbulent channel flow (Re_tau = 180) without the aid of any supplemental wall-damping corrections. This work highlights the potential of symbolic regression to uncover robust, interpretable physical laws for the next generation of intelligent computational fluid dynamics solvers.