Reynolds-number-adaptive symbolic regression for explainable wall-bounded turbulence modelling
Year: 2026
Type: article
Abstract: In this paper, we investigate the application of Symbolic Regression (SR) to develop wall models for fully developed, pressure-driven turbulent channel flows. Using high quality Direct Numerical Simulation (DNS) data for training, we compare SR-generated models against classical composite wall law models. Two distinct SR strategies are evaluated: a global model ( S R W ) trained across all Reynolds numbers, and Reynolds-specific models ( S R W R e ) trained individually for each case. In parallel, a tree-based machine learning model, which is accurate but not interpretable, is employed as a high-accuracy benchmark, providing a baseline against which all other models are compared. Our results demonstrate that SR-derived models adhere to physical constraints and match the accuracy of high-resolution CFD while remaining computationally efficient, outperforming classical composite wall law models in the complex buffer and transition regions. Furthermore, we analyze the ability of these expressions to extrapolate beyond their training conditions, specifically for higher Reynolds number flows. This is a critical step toward developing robust wall functions for industrial-scale CFD, as it evaluates whether the SR-derived models have captured universal scaling laws of wall turbulence rather than merely overfitting to the specific, lower Reynolds number regimes where DNS data is currently accessible. The resulting expressions highlight the potential of SR to bridge the gap between data-driven approaches and classical theory, providing a transparent and explainable framework for future turbulence closure modeling and near-wall physics exploration.
Source: Computers & Fluids
Institution: University of Thessaly
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,700
OpenAlex ID: W7212045984