An engineering model for 3-D turbulent wind inflow based on a limited set of random variables

Fluck, Manuel; Crawford, Curran

doi:https://doi.org/10.5194/wes-2-507-2017

Articles | Volume 2, issue 2

https://doi.org/10.5194/wes-2-507-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/wes-2-507-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 2, issue 2

Research article

|

16 Nov 2017

Research article |

| 16 Nov 2017

An engineering model for 3-D turbulent wind inflow based on a limited set of random variables

Manuel Fluck and Curran Crawford

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Final revised paper (published on 16 Nov 2017)
Preprint (discussion started on 01 Mar 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Revision_ wes-2017-7', Anonymous Referee #1, 07 Apr 2017
- AC1: 'Response to comments from anonymous reviewer #1', Manuel Fluck, 27 Jun 2017
RC2: 'An engineering model for 3D turbulent wind inflow based on a limited set of random variables', Anonymous Referee #2, 14 Jun 2017
- AC2: 'Response to comments from anonymous reviewer #2', Manuel Fluck, 27 Jun 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (15 Sep 2017) by Horia Hangan

AR by Manuel Fluck on behalf of the Authors (25 Sep 2017)

ED: Publish as is (28 Sep 2017) by Horia Hangan

ED: Publish as is (10 Oct 2017) by Carlo L. Bottasso (Chief editor)

AR by Manuel Fluck on behalf of the Authors (10 Oct 2017)

Short summary

We present an engineering model of 3-D turbulent wind inflow which reduces the number of random variables required from tens of thousands to ~ 20. This new model is a vital step towards stochastic modelling of wind turbines. Such models can quickly assess turbine lifetime loads and fluctuating power output and thus can be used to design better turbines. However, stochastic models are only viable when the input is expressed with very few random variables, hence the new wind model presented here.