The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

TL;DR

The paper introduces the Area Localized Coupled (ALC) model, which improves mean flow and power predictions in arbitrary wind farm layouts by coupling wake and boundary layer models using Voronoi tessellation.

Contribution

The ALC model is a novel approach that couples wake and boundary layer models with Voronoi tessellation for arbitrary wind farm geometries, validated against LES data.

Findings

Accurately predicts power and velocity in complex wind farms.

Demonstrates versatility across different turbine arrangements.

Outperforms existing models in diverse inflow conditions.

Abstract

This work introduces the Area Localized Coupled (ALC) model, which extends earlier approaches to coupling classical wake superposition and atmospheric boundary layer models in order to enable applicability to arbitrary wind-farm layouts. Coupling wake and top-down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with certain turbine-specific regions of the flow. The ALC model uses Voronoi tesselation to define a local area around each turbine. A top-down description of a developing internal boundary layers is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top-down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The ALC model is implemented using a wake model with a profile that transitions from a top-hat to Gaussian function and accounts for wake interactions through linear superposition. Detailed comparisons to large-eddy simulation (LES) data demonstrate the efficacy of the model in accurate predictions of both power and hub height velocity for complex wind farm geometries. Further validation with LES for a hybrid array-random farm that has half of the turbines arranged in an array and the other half randomly distributed indicates the model's versatility with respect to capturing results from different wind farm configurations. In both cases, the ALC model is shown to produce improved power predictions for both the farm and individual turbines over prevailing approaches for a range of wind inflow directions.