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

TL;DR

This paper introduces a synthetic inflow LES method with high controllability to study wind turbine wake dynamics under various atmospheric boundary layer conditions, revealing key influences of veer and shear on wake behavior.

Contribution

The paper presents a novel synthetic inflow LES approach that independently varies key atmospheric parameters, enabling comprehensive exploration of wake dynamics across diverse conditions.

Findings

Wake recovery depends on wind veer, especially at low turbulence.

A new scaling relation links wake deflections to shear and veer.

The study identifies ABL regimes critical for wind turbine operation.

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.