Unraveling the effects of atmospheric dynamics on wakes with a controlled synthetic inflow methodology

TL;DR

The study tackles how atmospheric boundary layer variability shapes wind-turbine wakes by introducing a synthetic inflow LES framework that independently prescribes mean profiles and injects turbulence, enabling controlled variation of shear, veer, TI, and Coriolis forcing. A comprehensive validation against concurrent-precursor TNBL and stratified inflows demonstrates that the method reproduces wake recovery and key budget dynamics while offering stationary statistics for robust turbulence analysis. Analysis of 640 LES cases reveals that wake recovery is most sensitive to inflow veer, TI, and turbulence length scales, with veer enhancing turbulent entrainment and shortening the near-wake region; a novel scaling shows wake deflections collapse when expressed through the product of shear and veer, $\alpha_s\alpha_v$, modulated by Ro and TI. The findings provide a scalable framework for exploring ABL-wake interactions across realistic field conditions and inform improved wake modeling for wind-farm design and control, including future integration with conventional coupled ABL simulations.

Abstract

Winds in the atmospheric boundary layer (ABL) display a wide range of velocity profiles and turbulence properties that affect wind turbine wake dynamics. However, standard concurrent-precursor large eddy simulations (LES) often neglect phenomena such as mesoscale patterns, limiting the range and controllability of inflow parameters that can be studied. Here, we propose a synthetic inflow LES method with high inflow controllability to allow parameters such as shear, turbulence, and Coriolis effects to be varied independently, facilitating the efficient exploration of wake dynamics across the full range of conditions observed in the field. The synthetic inflow method faithfully reconstructs wake dynamics when compared with standard concurrent-precursor LES. We then run a suite of over 600 LES cases to investigate the ABL processes that most affect wake dynamics. We find that wake recovery strongly depends on inflow wind veer, especially at low turbulence intensities, due to the elongation of the skewed wake. Furthermore, we identify a novel scaling relation that collapses wake deflections and dynamics onto the combination of shear and veer. The suite of LES cases elucidates ABL regimes and wake dynamics where current and future wind turbines may operate, building toward improved wake modeling for wind farm design and control.

Figures (15)

• Figure 1: LES methodology for the synthetic inflow simulations. Three large eddy simulations are concurrently run (HIT, empty, primary), and the velocity perturbations from the HIT simulation are advected and superimposed on the mean profiles in the primary and empty domains.
• Figure 2: Joint distributions of observed ABL parameters at the Martha's Vineyard Coastal Observatory (MVCO) and profiling lidar. Shear and veer distributions are approximated as linear profiles of wind speed and direction computed across the rotor extent, while $\mathrm{TI}$ is interpolated at hub height. The Rossby number is based on the turbine diameter of the IEA 15 MW reference turbine.
• Figure 3: Comparison of ($a$) the streamtube-averaged wake velocity deficit $\langle \overline{\Delta u} \rangle$ and ($b$) wake centroid between the synthetic inflow simulations (dashes) and concurrent-precursor TNBL simulations (solid lines), plotted as a function of the inverse Rossby number $Ro^{-1}$.
• Figure 4: Streamwise evolution of the streamtube-averaged streamwise RANS budget terms for ($a$) relatively weak Coriolis forcing $(\mathrm{R500})$ and ($b$) relatively strong Coriolis forcing $(\mathrm{R100})$.
• Figure 5: Streamwise evolution of the streamtube-averaged lateral RANS deficit budget terms for ($a$) relatively weak Coriolis forcing $(\mathrm{R500})$ and ($b$) relatively strong Coriolis forcing $(\mathrm{R100})$.