A progressive data-augmented RANS model for enhanced wind-farm simulations

TL;DR

This paper introduces a progressive data-augmentation method for RANS models to improve wind-farm flow simulations, enhancing turbulence prediction and secondary flow capture for better wind-energy design.

Contribution

The study presents a novel progressive data-augmentation approach that incorporates turbine forces and uses LES data to improve RANS model accuracy in wind-farm simulations.

Findings

Enhanced prediction of wake recovery and secondary flows.

Superior performance of the augmented model over standard RANS.

Accurate replication of wake characteristics compared to wind-tunnel data.

Abstract

The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier-Stokes (RANS) models are pivotal in enhancing our comprehension of the complex flow within and around wind farms and, hence, improving their capacity to accurately model turbulence within this context is a vital research goal. The enhancement is essential for a precise prediction of wake recovery and for capturing intricate flow phenomena such as secondary flows of Prandtl's second kind behind the turbines. To reach these objectives, here, we propose a progressive data-augmentation approach. We first incorporate the turbine-induced forces in the turbulent kinetic energy equation of the widely used $k-\omega\text{SST}$ model. Afterward, we utilize data from large-eddy simulations to progressively enhance the Reynolds-stress prediction of this baseline model, accurately capturing the evolution of eddy viscosity in the wake, as well as the emergence of secondary flows. We then apply the optimized model to two unseen cases with distinct layouts and conduct a comparative analysis focusing on the obtained quantities such as normalized streamwise velocity deficit, turbulence intensity, and power output. We also examine the success rate of the augmented model in predicting the secondary flows in the wake region. We also evaluate the performance of the augmented model in predicting wake characteristics by comparing it with wind-tunnel measurement data. Our comparisons and validations demonstrate the superior performance of the progressive data-augmented model over the standard version in all cases considered in this study.