Abstract
The aim of this work is to develop an algorithm that is able to provide predictions of wind speed statistics (WSS) in renewable energy environments. The subject is clearly interesting, as predictions of storms and extreme winds are important for decision makers and emergency response teams in renewable energy environments, e.g., in places where wind turbines could be located, including cities. The goal of the work is achieved through two phases: (a) During the preparation phase, the construction of a big WSS database based on computational fluid dynamics (CFD) is carried out, which includes flow fields of different wind directions in all grid numerical points; (b) In the second phase, the algorithm is used to find the records in the WSS database with the closest meteorological conditions to the meteorological conditions of interest. The evaluation of the CFD model (including both RANS and LES turbulence methodologies) is performed using the experimental data of the MUST (Mock Urban Setting Test) wind tunnel experiment.
Highlights
Fluids 2021, 6, 461. https://doi.org/The prediction of wind speed in renewable energy environments is a very interesting research field
When turbulence is modeled with direct numerical simulation (DNS) or large eddy simulation (LES), the prediction of wind speed can be performed through the predicted time series
The algorithm is based on computational fluid dynamics (CFD) model results while at the same time it is fast enough to be applied in emergency situations
Summary
The prediction of wind speed in renewable energy environments is a very interesting research field. When turbulence is modeled with direct numerical simulation (DNS) or large eddy simulation (LES), the prediction of wind speed can be performed through the predicted time series. This reason, the present situation requiresrequires answersanswers in someinminutes For this in theinpresent work,work, an an algorithm is developed to extract the information from a RANS database and to pro‐. Algorithm is developed to extract the information from a RANS database and to provide a reliable andwind fast wind prediction. In the the present study,was the algorithm be appliedenergy in renewable ments,environments, and based on the knowledge this knowledge is the first effort energy andauthors’.
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