Global sensitivity analysis of the blade geometry variables on the wind turbine performance

Fernando Echeverría; Fermin Mallor; Unai San Miguel

doi:10.1002/we.2111

journal article May 25, 2017

Global sensitivity analysis of the blade geometry variables on the wind turbine performance

Fernando Echeverría Fermin Mallor

Unai San Miguel

Wind Energy Vol. 20 No. 9 pp. 1601-1616 · Wiley

View at Publisher Save 10.1002/we.2111

Abstract

AbstractThe need for implementing efficient blade designs gains relevance as wind turbine developments require longer blades. The design of blade geometry, traditionally divided in 2D airfoils and spanwise distributions, is usually addressed as an optimization problem. A correct identification of the design variables is crucial to avoid unnecessary computational cost or insufficient exploration of the design space. This paper deals with the identification of the design variables that affect the wind turbine performance.First, the number of design variables for an accurate airfoil representation is resolved. A methodology, based on statistical hypothesis testing applied to the airfoil approximation errors, is presented to assess the accuracy of types of B‐splines.Second, the study is extended to chord and twist distributions besides airfoil geometry with the purpose of assessing the sensitive blade variables in the wind turbine performance. Global sensitivity analysis as multi‐variable linear regressions and variance‐based methods are used. Latin hypercube sampling is applied to generate efficient inputs. MATLAB‐based code is developed to obtain outputs: annual energy production, maximum blade tip deflection, overall sound power level and blade mass.As result of the study, a list of non‐affecting variables is deduced. These variables can be avoided in the optimization without loss of gain in the performance. The method is a powerful tool to analyse in a preliminary phase a design problem involving a high amount of variables and complex physical relations by means of combining different multi‐disciplinar calculation codes and performing statistical treatments. Copyright © 2017 John Wiley & Sons, Ltd.

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References

42

[1]

10.2172/1072784

[2]

Drela M

[3]

10.1016/s0167-6105(98)00191-3

[4]

10.1016/0167-6105(94)00095-u

[5]

10.2514/1.j052268

[6]

Rao SS (2009)

[7]

Bellman R (1957)

[8]

Sobester A (2008)

[9]

HanX.An evolutionary geometry parametrization aerodynamic shape optimization. University of Toronto;2011. 10.2514/6.2011-3536

[10]

Fuglsang P (1999)

[11]

10.1109/41.824144

[12]

10.1007/978-3-642-59223-2

[13]

10.1088/1742-6596/524/1/012041

[14]

10.1016/j.apm.2011.12.026

[15]

Bak C "Airfoil design: finding the balance between design lift and structural stiffness" Journal of Physics: Conference Series (Online) (2014)

[16]

Nilsson K "Airfoil data sensitivity analysis for actuator disc simulations used in wind turbine applications" Journal of Physics: Conference Series (Online) (2014)

[17]

Saltelli A (2004)

[18]

10.1016/j.cageo.2013.06.006

[19]

(2012)

[20]

DrelaM.XFOIL: an analysis and design system for low Reynolds number airfoils.Conference on Low Reynolds Number Aerodynamics University of Notre Dame1989 10.1007/978-3-642-84010-4_1

[21]

Bir G

[22]

Boorsma K

[23]

MATLAB (2014)

[24]

10.2514/2.1391

[25]

10.2514/3.58379

[26]

10.1016/j.ast.2013.11.006

[27]

Boor C (2001)

[28]

10.1002/zamm.19700500646

[29]

10.1016/s1570-579x(06)80004-2

[30]

10.1016/0010-4485(89)90003-1

[31]

(2013)

[32]

Crawley MJ (2011)

[33]

Team RC (2013)

[34]

UIUC Airfoil Data Site.UIUC Airfoil Data Site. <http://m‐selig.ae.illinois.edu/ads/coord_database.html>.

[35]

Good PI (2004)

[36]

A review of techniques for parameter sensitivity analysis of environmental models

D. M. Hamby

Environmental Monitoring and Assessment 10.1007/bf00547132

[37]

McKayMD.Latin hypercube sampling as a tool in uncertainty analysis of computer models. In: Proceedings 24th Conference Winter Simulation. WSC ′92. Arlington Virginia USA: ACM;1992:557–564. https://doi.org/10.1145/167293.167637. 10.1145/167293.167637

[38]

Fishman GS (1996)

[39]

A global sensitivity analysis tool for the parameters of multi-variable catchment models

A. van Griensven, T. Meixner, S. Grunwald et al.

Journal of Hydrology 10.1016/j.jhydrol.2005.09.008

[40]

ViternaLA JanetzkeDC.Theoretical and experimental. Power from large horizontal axis. Wind turbines.1982.https://doi.org/10.2172/6763041. 10.2172/6763041

[41]

Schlichting H "Boundary‐layer theory" Quarterly Journal of the Royal Meteorological Society (1956)

[42]

EfronB.The jackknife bootstrap other resampling plans. Society for Industrial and Applied Mathematics;1982.https://doi.org/10.1137/1.9781611970319. 10.1137/1.9781611970319

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Details

Published: May 25, 2017
Vol/Issue: 20(9)
Pages: 1601-1616
License: View

Authors

F

Fernando Echeverría

ACCIONA Windpower Avenida Innovacion 3 Sarriguren Pamplona 31621 Spain

F

Fermin Mallor

UPNA Universidad Pública de Navarra, Statistics Pamplona Navarra Spain

U

Unai San Miguel

ACCIONA Windpower Avenida Innovacion 3 Sarriguren Pamplona 31621 Spain

Cite This Article

Fernando Echeverría, Fermin Mallor, Unai San Miguel (2017). Global sensitivity analysis of the blade geometry variables on the wind turbine performance. Wind Energy, 20(9), 1601-1616. https://doi.org/10.1002/we.2111

Global sensitivity analysis of the blade geometry variables on the wind turbine performance

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