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Research Papers: Gas Turbines: Turbomachinery

Wind Turbine Design Optimization Under Environmental Uncertainty

[+] Author and Article Information
Marco Caboni

School of Engineering,
University of Glasgow,
Glasgow G12 8QQ, UK
e-mails: m.caboni.1@research.gla.ac.uk;
caboni@ecn.nl

M. Sergio Campobasso

Department of Engineering,
Lancaster University,
Lancaster LA1 4YR, UK
e-mail: m.s.campobasso@lancaster.ac.uk

Edmondo Minisci

Department of Mechanical and
Aerospace Engineering,
University of Strathclyde,
Glasgow G1 1XJ, UK
e-mail: edmondo.minisci@strath.ac.uk

1Present address: Energy research Centre of the Netherlands, ECN, P.O. Box 1, 1755 ZG Petten, The Netherlands.

2Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received December 22, 2015; final manuscript received January 1, 2016; published online March 15, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(8), 082601 (Mar 15, 2016) (10 pages) Paper No: GTP-15-1576; doi: 10.1115/1.4032665 History: Received December 22, 2015; Revised January 01, 2016

Wind turbine design optimization is typically performed considering a given wind distribution. However, turbines so designed often end up being used at sites characterized by different wind distributions, resulting in significant performance penalties. This paper presents a probabilistic integrated multidisciplinary approach to the design optimization of multimegawatt wind turbines accounting for the stochastic variability of the mean wind speed. The presented technology is applied to the design of a 5 MW rotor for use at sites of wind power class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing mean and standard deviation of the levelized cost of energy (LCOE). Airfoil shapes, spanwise distributions of blade chord and twist, blade internal structural layup, and rotor speed are optimized concurrently, subject to structural and aeroelastic constraints. The probabilistically designed turbine achieves a more favorable probabilistic performance than the initial baseline turbine. The presented probabilistic design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity.

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Figures

Grahic Jump Location
Fig. 1

Parameterization of root, midspan, and tip airfoils. Each airfoil is defined by a composite Bezier curve based on 14 points, here shown only for the root airfoil. Horizontal and vertical arrows denote the actual degrees-of-freedom.

Grahic Jump Location
Fig. 2

Parameterization of chord profile (left subplot) and twist profile (right subplot). The chord profile is defined by a cubic spline based on five points, and the twist profile is defined by a cubic spline based on four points. Horizontal and vertical arrows denote the actual degrees-of-freedom.

Grahic Jump Location
Fig. 3

Rotor speed (left subplot) and electric power (right subplot) against wind speed. Left subplot reports different control regions.

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Fig. 4

Comparison of airfoil shapes, and lift (CL) and drag (CD) coefficients of the reference and robust designs for a Reynolds number of 12 × 106

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Fig. 5

Comparison of chord and twist distributions (left and right top subplots, respectively), and rotor speed and electric power against wind speed (left and right bottom subplots, respectively) of the reference and robust designs

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Fig. 6

Displacement and applied forces of (a) the reference blade design and (b) the robust blade design at the 3 o'clock azimuth position for a wind speed of 12.0 m/s and a rotor speed of 12.1 rpm

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Fig. 7

Laminate normal stress of (a) the reference blade and (b) the probabilistically designed blade at the 3 o'clock azimuth position for a wind speed of 12.0 m/s and a rotor speed of 12.1 rpm

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