  The state-of-the-art in wind-farm flow-physics modeling is Large Eddy
Simulation (LES) which makes accurate predictions of most relevant physics, but
requires extensive computational resources. The next-fidelity model types are
Reynolds-Averaged Navier-Stokes (RANS) which are two orders of magnitude
cheaper, but resolve only mean quantities and model the effect of turbulence.
They often fail to accurately predict key effects, such as the wake recovery
rate. Custom RANS closures designed for wind-farm wakes exist, but so far do
not generalize well: there is substantial room for improvement. In this article
we present the first steps towards a systematic data-driven approach to
deriving new RANS models in the wind-energy setting. Time-averaged LES data is
used as ground-truth, and we first derive optimal corrective fields for the
turbulence anisotropy tensor and turbulence kinetic energy (t.k.e.) production.
These fields, when injected into the RANS equations (with a baseline
$k-\epsilon$ model) reproduce the LES mean-quantities. Next we build a custom
RANS closure from these corrective fields, using a deterministic symbolic
regression method to infer algebraic correction as a function of the (resolved)
mean-flow. The result is a new RANS closure, customized to the training data.
The potential of the approach is demonstrated under neutral atmospheric
conditions for multi-turbine constellations at wind-tunnel scale. The results
show significantly improved predictions compared to the baseline closure, for
both mean velocity and the t.k.e. fields.
