Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling

TL;DR

This paper introduces a novel wind turbine wake modeling approach that combines symbolic regression with dynamic wake meandering, resulting in fast, accurate, and physically consistent predictions validated against LES data.

Contribution

The paper presents a symbolic regression-enhanced DWM framework that embeds physically interpretable equations into wake modeling, improving accuracy and computational efficiency.

Findings

Achieves over 1000x speedup compared to LES

Accurately reproduces mean wake and turbulence dynamics

Provides physically consistent, interpretable wake models

Abstract

Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behavior, it suffers from near-wake inaccuracies due to empirical closures. We propose a Symbolic Regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physically consistent expressions are discovered from LES data using symbolic regression guided by a hierarchical, domain-informed decomposition strategy. A revised wake-added turbulence formulation is further introduced to enhance turbulence intensity predictions. Extensive validation across varying inflows shows that SRDWM accurately reproduces both mean wake characteristics and turbulent dynamics, achieving full spatiotemporal resolution with over three orders of magnitude speedup compared to LES. The results highlight symbolic regression as a bridge between data and physics, enabling interpretable and generalizable modeling.