DOI: 10.48804/lrsenq
title: A reference database for the analysis of wind-farm wake recovery mechanisms in a large-eddy simulation framework

description: &lt;p&gt;Note: we recommend switching the view from 'Table' to 'Tree' when exploring the dataset. Further, we refer to https://www.kuleuven.be/rdm/en/rdr/large-downloads for efficient download options.&lt;/p&gt;

&lt;p&gt;The dataset contains a suite of large-eddy simulation results of a wind farm operating in conventionally neutral boundary layers, in which atmospheric conditions are varied to study the wind-farm wake behaviour and its recovery mechanisms. A 1.6GW offshore wind farm with a fixed layout, composed of 160 IEA 10MW turbines, is considered for 4 different atmospheric stratification conditions. In particular, we initialize the simulations with four capping-inversion heights (i.e. 150, 300, 500 and 1000 m) while keeping the capping-inversion strength and free-atmosphere lapse rate fixed to 5 K and 4 K/km, respectively. All simulations are performed by using a concurrent precursor method. Hence, the inflow conditions in the main domain (the one containing the turbines) are provided by the flow fields generated in the precursor domain. Appropriate spin-ups are used (first in the precursor domain, and subsequently in precursor and main domains) to generate fully developed turbulence in the boundary layer. The dataset is generated with the SP-Wind code, an in-house LES and DNS code developed at KU Leuven.&lt;/p&gt;

&lt;p&gt;The dataset is structured as follows. The results from the 4 simulations are organized into 4 separate folders. Each folder contains results obtained on both the precursor (stat_precursor_**.h5) and main (stat_main_**.h5) domains. There are 17 time-averaged flow fields per domain, which are categorized in first- and second-order statistics, further divided into resolved and sub-grid scale components. The flow fields have dimensions of Nx x Ny x Nz, where Nx, Ny and Nz are the number of grid points in the streamwise, spanwise and vertical directions used in the respective domain. Note that these flow fields are time-averaged over the last 2 hours of the simulation. Finally, the turbine_data.h5 file contains information about the thrust, power and orientation of all turbines in the farm. For more information, we refer to the readme.txt file located in the dataset.&lt;/p&gt;

&lt;p&gt;&lt;u&gt;Acknowledgements&lt;/u&gt;&lt;/p&gt;

&lt;p&gt;The authors acknowledge support from the Research Foundation Flanders (FWO, Grant No. G0B1518N), from the project FREEWIND, funded by the Energy Transition Fund of the Belgian Federal Public Service for Economy, SMEs, and Energy (FOD Economie, K.M.O., Middenstand en Energie) and from the European Union Horizon Europe Framework programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205. The computational resources and services in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government department EWI.&lt;/p&gt;

&lt;p&gt;&lt;u&gt;References&lt;/u&gt;&lt;/p&gt;

&lt;p&gt;Lanzilao, L. & Meyers, J. (2024), Wind-farm wake recovery mechanisms in conventionally neutral boundary layers. Manuscript submitted to the J. Fluid Mech. and currently available on arXiv, https://arxiv.org/abs/2407.17198.&lt;/p&gt;
description: SP-Wind, v2.0