Abstract
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–ɛ 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.
Highlights
Offshore wind farms have the potential to become the sustainable future power plants of North-Western Europe
In order to study the errors of the optimal corrections from the kfrozen approach, the errors introduced by the sparse regression, and the errors in the final coupled models separately, we consider three kinds of corrections
The proposed frozen k-corrective frozen-Reynolds-Averaged Navier–Stokes (RANS) has demonstrated the potential for improving the predictions of the mean velocity and turbulent kinetic energy
Summary
Offshore wind farms have the potential to become the sustainable future power plants of North-Western Europe. The state of the art is Large Eddy Simulations (LES) which is a high-fidelity Computational Fluid Dynamics (CFD) method where most of the scales of turbulence are resolved whilst the effect of the unresolved on the resolved scales is modeled This type of simulation requires extensive computational resources: one wind speed and direction simulation of the Lillgrund wind farm can take between 160k and 3000k processor hours depending on how the turbines are modeled [4,5]. RANS models provide useful information for time-averaged quantities over short intervals For both RANS and LES the range of scales present, ranging from the boundary-layer on the turbine blades to the height of the atmospheric boundary layer, is too large to be fully resolved. Actuator models are used to model the presence of wind turbines [2]
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