Discovery of a Physically Interpretable Data-Driven Wind-Turbine Wake Model

TL;DR

This paper introduces a simple, data-driven RANS wind turbine wake model that improves wake prediction accuracy by reducing turbulence mixing in high-shear regions, demonstrating good generalization from limited training data.

Contribution

A novel linear eddy viscosity model derived via symbolic regression that enhances standard RANS models with physical interpretability and improved wake prediction accuracy.

Findings

The model reduces eddy viscosity in high-shear wake regions.

It performs comparably to established models in velocity and power predictions.

The model generalizes well to unseen turbine configurations.

Abstract

This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard \(k\)-\(\varepsilon\) formulation. Several candidate models were discovered using the symbolic regression framework Sparse Regression of Turbulent Stress Anisotropy (SpaRTA), trained on a single Large Eddy Simulation (LES) dataset of a standalone wind turbine. The leading model was selected by prioritizing simplicity while maintaining reasonable accuracy, resulting in a novel linear eddy viscosity model. This selected leading model reduces eddy viscosity in high-shear regions, particularly in the wake, to limit turbulence mixing and delay wake recovery. This addresses a common shortcoming of the standard \(k\)-\(\varepsilon\) model, which tends to overpredict mixing, leading to unrealistically fast wake recovery. Moreover, the formulation of the leading model closely resembles that of the established \(k\)-\(\varepsilon\)-\(f_P\) model. Consistent with this resemblance, the leading and \(k\)-\(\varepsilon\)-\(f_P\) models show nearly identical performance in predicting velocity fields and power output, but they differ in their predictions of turbulent kinetic energy. In addition, the generalization capability of the leading model was assessed using three unseen six-turbine configurations with varying spacing and alignment. Despite being trained solely on a standalone turbine case, the model produced results comparable to LES data. These findings demonstrate that data-driven methods can yield interpretable, physically consistent RANS models that are competitive with traditional modeling approaches while maintaining simplicity and achieving generalizability.