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

TY - JOUR

T1 - A progressive data-augmented RANS model for enhanced wind-farm simulations

AU - Amarloo, Ali

AU - Zehtabiyan-Rezaie, Navid

AU - Abkar, Mahdi

PY - 2024/11/17

Y1 - 2024/11/17

N2 - 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; 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−ωSST 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 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.

AB - 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; 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−ωSST 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 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.

KW - Power losses

KW - Reynolds-averaged simulation

KW - Turbine wakes

KW - Turbulence modeling

KW - Wind-farm modeling

UR - http://www.scopus.com/inward/record.url?scp=85210138663&partnerID=8YFLogxK

U2 - 10.1016/j.energy.2024.133762

DO - 10.1016/j.energy.2024.133762

M3 - Journal article

SN - 0360-5442

VL - 313

JO - Energy

JF - Energy

M1 - 133762

ER -