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CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscale predictions. / Stergiannis, Nikolaos; Van Beeck, Jeroen; Runacres, Mark.

2017. Poster session presented at Resource Assessment 2017, Edinburgh , United Kingdom.

Research output: Unpublished contribution to conferencePoster

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Stergiannis, Nikolaos ; Van Beeck, Jeroen ; Runacres, Mark. / CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscale predictions. Poster session presented at Resource Assessment 2017, Edinburgh , United Kingdom.

BibTeX

@conference{c5da9b12a9ee401483ebf54325e9cc01,
title = "CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscale predictions",
abstract = "Due to the complexity and uncertainty involved in the process of making power from wind, more and more advanced tools are being developed to maintain the sustainability and the growing trend of the wind industry. Prior to the development of a wind farm project, measured data are provided by limited installed wind masts at the site under investigation and by other nearby weather stations. Therefore, the wind resource assessment depends on the uncertainties and limitations of those measurements. To improve the reliability and limit the risks, weather prediction forecasting models can be employed in parallel with measurements, to investigate the local wind map and the potential wind power. Nevertheless, the physics involved at the inter-turbine or smaller scales cannot be captured by mesoscale modelling. To obtain predictions of such scales, high resolution mesoscale models are coupled with micro-scale computational fluid dynamics (CFD) simulations. Results of neutral atmospheric stability over a defined wind sector have been averaged in time and extracted from mesoscale simulations using the Weather Research and Forecasting model (WRF) with a very fine resolution (150 m). Those results were used to provide the inlet conditions of the micro-scale CFD simulations which were performed using the open-source CFD software OpenFOAM. The predicted time-averaged atmospheric flow within the Egmond aan Zee wind farm is compared for both numerical approaches. The wind farm’s total power estimations are compared to operational SCADA data.",
keywords = "CFD, Mesoscale modelling, WRF, Wind Energy, Wind Farm, OpenFOAM, Offshore wind energy, Wake effects, Wind measurement, SCADA, RANS",
author = "Nikolaos Stergiannis and {Van Beeck}, Jeroen and Mark Runacres",
year = "2017",
month = "3",
doi = "10.13140/RG.2.2.29343.97441",
language = "English",
note = "Resource Assessment 2017 ; Conference date: 16-03-2017 Through 17-03-2017",
url = "https://windeurope.org/workshops/resource-assessment-2017/",

}

RIS

TY - CONF

T1 - CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscale predictions

AU - Stergiannis, Nikolaos

AU - Van Beeck, Jeroen

AU - Runacres, Mark

PY - 2017/3

Y1 - 2017/3

N2 - Due to the complexity and uncertainty involved in the process of making power from wind, more and more advanced tools are being developed to maintain the sustainability and the growing trend of the wind industry. Prior to the development of a wind farm project, measured data are provided by limited installed wind masts at the site under investigation and by other nearby weather stations. Therefore, the wind resource assessment depends on the uncertainties and limitations of those measurements. To improve the reliability and limit the risks, weather prediction forecasting models can be employed in parallel with measurements, to investigate the local wind map and the potential wind power. Nevertheless, the physics involved at the inter-turbine or smaller scales cannot be captured by mesoscale modelling. To obtain predictions of such scales, high resolution mesoscale models are coupled with micro-scale computational fluid dynamics (CFD) simulations. Results of neutral atmospheric stability over a defined wind sector have been averaged in time and extracted from mesoscale simulations using the Weather Research and Forecasting model (WRF) with a very fine resolution (150 m). Those results were used to provide the inlet conditions of the micro-scale CFD simulations which were performed using the open-source CFD software OpenFOAM. The predicted time-averaged atmospheric flow within the Egmond aan Zee wind farm is compared for both numerical approaches. The wind farm’s total power estimations are compared to operational SCADA data.

AB - Due to the complexity and uncertainty involved in the process of making power from wind, more and more advanced tools are being developed to maintain the sustainability and the growing trend of the wind industry. Prior to the development of a wind farm project, measured data are provided by limited installed wind masts at the site under investigation and by other nearby weather stations. Therefore, the wind resource assessment depends on the uncertainties and limitations of those measurements. To improve the reliability and limit the risks, weather prediction forecasting models can be employed in parallel with measurements, to investigate the local wind map and the potential wind power. Nevertheless, the physics involved at the inter-turbine or smaller scales cannot be captured by mesoscale modelling. To obtain predictions of such scales, high resolution mesoscale models are coupled with micro-scale computational fluid dynamics (CFD) simulations. Results of neutral atmospheric stability over a defined wind sector have been averaged in time and extracted from mesoscale simulations using the Weather Research and Forecasting model (WRF) with a very fine resolution (150 m). Those results were used to provide the inlet conditions of the micro-scale CFD simulations which were performed using the open-source CFD software OpenFOAM. The predicted time-averaged atmospheric flow within the Egmond aan Zee wind farm is compared for both numerical approaches. The wind farm’s total power estimations are compared to operational SCADA data.

KW - CFD

KW - Mesoscale modelling

KW - WRF

KW - Wind Energy

KW - Wind Farm

KW - OpenFOAM

KW - Offshore wind energy

KW - Wake effects

KW - Wind measurement

KW - SCADA

KW - RANS

U2 - 10.13140/RG.2.2.29343.97441

DO - 10.13140/RG.2.2.29343.97441

M3 - Poster

ER -

ID: 30552054