Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms

TL;DR

This paper assesses the uncertainties in RANS simulations of wind farm wakes by comparing different models to LES data and perturbing the Reynolds stress tensor to estimate the bounds of model predictions.

Contribution

It introduces a method to quantify model-form uncertainties in RANS simulations of wind farms through tensor perturbations, validated against LES results.

Findings

Realizable k-epsilon model effectively predicts key wind farm metrics.

Tensor perturbation bounds encompass LES results for quantities of interest.

Perturbation magnitude influences the estimated uncertainty bounds.

Abstract

Reynolds-averaged Navier-Stokes (RANS) is one of the most cost-efficient approaches to simulate wind-farm-atmosphere interactions. However, the applicability of RANS-based methods is always limited by the accuracy of turbulence closure models, which introduce various uncertainties into the models. In this study, we estimate model-form uncertainties in RANS simulations of wind farms. For this purpose, we compare different RANS models to a large-eddy simulation (LES). We find that the realizable k-epsilon model is a representative RANS model for predicting the mean velocity, the turbulence intensity, and the power losses within the wind farm. We then investigate the model-form uncertainty associated with this turbulence model by perturbing the Reynolds stress tensor. The focus is placed on perturbing the shape of the tensor represented by its eigenvalues. The results show that the perturbed RANS model successfully estimates the region bounding the LES results for quantities of interest (QoIs). We also discuss the effect of perturbation magnitude on various QoIs.