Multi-fidelity wake modelling based on Co-Kriging method

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Abstract

The article presents an approach to combine wake models of multiple levels of fidelity, which is capable of giving accurate predictions with only a small number of high fidelity samples. The G. C. Larsen and k-ε-fP based RANS models are adopted as ensemble members of low fidelity and high fidelity models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed. The analysis shows that the multi-fidelity wake models could tremendously reduce the high fidelity model evaluations needed in building an accurate surrogate.
Original languageEnglish
Article number032065
Book seriesJournal of Physics: Conference Series (Online)
Volume753
Issue number3
Number of pages11
ISSN1742-6596
DOIs
Publication statusPublished - 2016
EventThe Science of Making Torque from Wind 2016 - Technische Universität München (TUM), Munich, Germany
Duration: 5 Oct 20167 Oct 2016
Conference number: 6
https://www.events.tum.de/?sub=29

Conference

ConferenceThe Science of Making Torque from Wind 2016
Number6
LocationTechnische Universität München (TUM)
CountryGermany
CityMunich
Period05/10/201607/10/2016
Internet address

Cite this

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title = "Multi-fidelity wake modelling based on Co-Kriging method",
abstract = "The article presents an approach to combine wake models of multiple levels of fidelity, which is capable of giving accurate predictions with only a small number of high fidelity samples. The G. C. Larsen and k-ε-fP based RANS models are adopted as ensemble members of low fidelity and high fidelity models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed. The analysis shows that the multi-fidelity wake models could tremendously reduce the high fidelity model evaluations needed in building an accurate surrogate.",
author = "Wang, {Y. M.} and Pierre-Elouan R{\'e}thor{\'e} and {van der Laan}, Paul and {Murcia Leon}, {Juan Pablo} and Liu, {Y. Q.} and L. Li",
year = "2016",
doi = "10.1088/1742-6596/753/3/032065",
language = "English",
volume = "753",
journal = "Journal of Physics: Conference Series (Online)",
issn = "1742-6596",
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}

Multi-fidelity wake modelling based on Co-Kriging method. / Wang, Y. M.; Réthoré, Pierre-Elouan; van der Laan, Paul; Murcia Leon, Juan Pablo; Liu, Y. Q.; Li, L.

In: Journal of Physics: Conference Series (Online), Vol. 753, No. 3, 032065, 2016.

Research output: Contribution to journalConference articleResearchpeer-review

TY - GEN

T1 - Multi-fidelity wake modelling based on Co-Kriging method

AU - Wang, Y. M.

AU - Réthoré, Pierre-Elouan

AU - van der Laan, Paul

AU - Murcia Leon, Juan Pablo

AU - Liu, Y. Q.

AU - Li, L.

PY - 2016

Y1 - 2016

N2 - The article presents an approach to combine wake models of multiple levels of fidelity, which is capable of giving accurate predictions with only a small number of high fidelity samples. The G. C. Larsen and k-ε-fP based RANS models are adopted as ensemble members of low fidelity and high fidelity models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed. The analysis shows that the multi-fidelity wake models could tremendously reduce the high fidelity model evaluations needed in building an accurate surrogate.

AB - The article presents an approach to combine wake models of multiple levels of fidelity, which is capable of giving accurate predictions with only a small number of high fidelity samples. The G. C. Larsen and k-ε-fP based RANS models are adopted as ensemble members of low fidelity and high fidelity models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed. The analysis shows that the multi-fidelity wake models could tremendously reduce the high fidelity model evaluations needed in building an accurate surrogate.

U2 - 10.1088/1742-6596/753/3/032065

DO - 10.1088/1742-6596/753/3/032065

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JO - Journal of Physics: Conference Series (Online)

JF - Journal of Physics: Conference Series (Online)

SN - 1742-6596

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