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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
Claude Abiven Natural Power, France
Co-authors:
Claude Abiven (1) F
(1) Natural Power, Strasbourg, France

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Claude owns masters in Fluid Mechanics and Climate Sciences from Virginia Tech and MIT. He has been working for Natural Power, a wind energy consultancy, for eight years. His work is mainly focused on advanced wind flow modelling for energy yield assessment. His research interests include CFD modelling of complex or forested terrain including wake modelling, as well as mesoscale and CFD coupling.


Poster

Poster Download poster (3.92 MB)

Abstract

Automated CFD for energy yield assessment

Introduction

Computational Fluid Dynamics (CFD) codes have been used in the industry for over 10 years. This period of learning and adjustment has enabled these tools to display lower uncertainties than linear models (e.g. [1]). This timeframe has also enabled CFD to become more accessible through increased computational performance. However, CFD outputs are often recognized as sensitive to a multiplicity of parameters requiring codes to be run by experts. This implies costs and timeframes that limit CFD use within the industry.

Approach

Here we propose to present the characteristics of a press-button CFD code, compare its performance and cost to other models used in the industry, and discuss its possible limitations.

Main body of abstract

The core of the CFD code used here is the last version of a CFD code developed for over 10 years [2] and used on over 10 GWs of wind farm projects.
The automated version of the code requires user input that can be limited to turbine layout. Topographic and roughness maps can be uploaded by the user if available; otherwise global datasets are automatically selected for the area of interest. The algorithm sets up CFD computations according to site characteristics, using a set of pre-defined parameters. Once computations are completed results are available as standard CFD outputs (wind speed, turbulence, wind shear, inflow angle, wind veer) at selected positions.
This code was run on a set of 20* mast pairs using detailed topographic and roughness maps and results were compared to outputs from other models (coupled CFD, standard CFD, mesoscale and linear models), showing high performance of the automated process. The performance of the code using global datasets will also be presented.
Limitations of the process (computational divergence) will be discussed, including performance on particularly complex wind farm sites.

[1] Corbett J.-F., Horn U., Bleeg J. and Whiting R.: A systematic validation of CFD flow modelling on commercial wind farms sites. Proceedings of the 2014 Ewea conference

[2] Castro F.A., Palma J.M.L.M. and Silva Lopes A.: Simulation of the askervein flow. Part 1 : Reynolds averaged navier-stokes equations. Boundary-Layer Meteorology, 2003

*at the time this abstract is being written


Conclusion

The performance of the automated CFD process will be discussed in the light of associated runtime and costs.


Learning objectives
Can CFD can be automated, at what cost and for which performance
Uncertainty and sensitivity of CFD modelling and of wind flow modeling in general