Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.
Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Venkatesh Duraisamy Jothiprakasam (1) F Eric Dupont (2) Bertrand Carissimo (2)
(1) Siemens Wind Power A/S, Brande, Denmark (2) EDF R&D, Chatou, France
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
Mr. Venkatesh has been working in wind resource assessment for the past 5 years and has many years of experience in CFD modeling. He is currently working at Siemens Wind Power A/S as Siting Engineer in CFD. He successfully defended his PhD in 2014 (May) at École des Ponts ParisTech in France. As part of his PhD he worked towards the development of wind flow modeling using coupled mesoscale-CFD models with data assimilation carried out at EDF R&D. His areas of specialization are CFD modeling, Wind resource assessment and Wind Turbines.
PosterDownload poster (12.05 MB)
Downscaling wind energy resource from mesoscale to CFD models by nesting and data assimilation to reduce uncertainty in complex terrain.
The development of wind energy generation requires precise and well established methods for wind resource assessment, which is the initial step in every wind farm project. During the last two decades linear flow models were widely used in the wind industry for wind resource assessment and micro-siting. But the linear models inaccuracies in predicting the wind speeds in very complex terrain are well known and led to use of CFD, capable of modeling the complex flow in details around specific geographic features. Mesoscale models (NWP) are able to predict the wind regime at resolutions of several kilometers, but are not well suited to resolve the wind speed and turbulence induced by the topography features on the scale of a few hundred meters. CFD has proven successful in capturing flow details at smaller scales, but needs an accurate specification of the inlet conditions. Thus coupling NWP and CFD models is a better modeling approach for wind energy applications with data assimilation of field measurement to correct the mesoscale errors.
A one-year field measurement campaign carried out in a complex terrain in southern France during 2007-2008 provides a well-documented data set both for input and validation data. The proposed new methodology aims to address two problems: the high spatial variation of the topography on the domain lateral boundaries, and the prediction errors of the mesoscale model. It is applied in this work using the open source CFD code Code_Saturne, coupled with the mesoscale forecast model of Météo-France (ALADIN). The improvement is obtained by combining the mesoscale data as inlet condition and field measurement data assimilation into the CFD model. Newtonian relaxation (nudging) data assimilation technique is used to incorporate the measurement data into the CFD simulations. The methodology to reconstruct long term averages uses a clustering process to group the similar meteorological conditions and to reduce the number of CFD simulations needed to reproduce 1 year of atmospheric flow over the site. The assimilation procedure is carried out with either sonic or cup anemometers measurements.
Main body of abstract
First a detailed analysis of the results obtained with the mesoscale-CFD coupling and with or without data assimilation will be shown for two main wind directions, including a sensitivity study to the parameters involved in the coupling and in the nudging. The last part of the work is devoted to the estimate of the wind potential using clustering. A comparison of the annual mean wind speed with measurements that do not enter the assimilation process and with the WAsP model is presented. The improvement provided by the data assimilation on the distribution of differences with measurements is shown on the wind speed and direction for different configurations. Comparison of yearly averaged wind speed of different wind flow models shows the coupled mesoscale and CFD models with data assimilation performed better. The highest error is obtained at the assimilation location (M80) is due to the positive bias in majority of clusters introduced by the assimilation.
Annual average wind speed from coupling mesoscale and CFD models with data assimilation methodology predicted better compared to field measurements than WAsP and coupling mesoscale and CFD models without data assimilation. The methodology also has shown the recent trend and merits of R&D in wind flow modeling towards coupling and data assimilation.
This presentation will educate the delegates about the development, improvement and advances in the wind flow modeling in complex terrain. It will also show that advanced wind flow models are able to reduce the uncertainty (assessment and mapping of wind resources towards the reduction of uncertainties to less than 3% for flat homogenous terrains, and to less than 10% for any terrain) compared to traditional WAsP and industrial CFD approach.