Research Papers: Alternative Energy Sources

Uncertainty Quantification of Aerodynamic Icing Losses in Wind Turbine With Polynomial Chaos Expansion

[+] Author and Article Information
Narges Tabatabaei, Michel J. Cervantes

Department of Engineering Sciences and
Luleå University of Technology,
Luleå, Norrbotten 97187, Sweden

Mehrdad Raisee

Hydraulic Machinery Research Institute,
School of Mechanical Engineering,
College of Engineering,
University of Tehran,
Tehran 11155-4563, Iran

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received June 29, 2018; final manuscript received January 31, 2019; published online April 1, 2019. Assoc. Editor: Ryo Amano.

J. Energy Resour. Technol 141(5), 051210 (Apr 01, 2019) (11 pages) Paper No: JERT-18-1463; doi: 10.1115/1.4042732 History: Received June 29, 2018; Revised January 31, 2019

Icing of wind turbine blades poses a challenge for the wind power industry in cold climate wind farms. It can lead to production losses of more than 10% of the annual energy production. Knowledge of how the production is affected by icing is of importance. Complicating this reality is the fact that even a small amount of uncertainty in the shape of the accreted ice may result in a large amount of uncertainty in the aerodynamic performance metrics. This paper presents a numerical approach using the technique of polynomial chaos expansion (PCE) to quantify icing uncertainty faster than traditional methods. Time-dependent bi-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics (RANS-CFD) simulations are considered to evaluate the aerodynamic characteristics at the chosen sample points. The boundary conditions are based on three-dimensional simulations of the rotor. This approach is applied to the NREL 5 MW reference wind turbine allowing to estimate the power loss range due to the leading-edge glaze ice, considering a radial section near the tip. The probability distribution function of the power loss is also assessed. The results of the section are nondimensionalized and assumed valid for the other radial sections. A correlation is found allowing to model the load loss with respect to the glaze ice horn height, as well as the corresponding probability distribution. Considering an equal chance for any of the ice profiles, load loss is estimated to be lower than 6.5% for the entire blade in half of the icing cases, while it could be roughly 4–6 times in the most severe icings.

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Fig. 1

NREL 5 MW blade, segments, airfoils, and icing profiles

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Fig. 2

Radial distribution of the ice accretion on the blade, according to the specifications of a horn ice shown on the right

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Fig. 3

Surface streamlines at section 12 of the NREL 5 MW blade; left: 3D view and right: top view

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Fig. 4

Ice shapes on the airfoil sections: (a) simulated in Ref. [22], (b) simulated in Ref. [23], and (c) curve fitted in this work

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Fig. 5

Ice shapes scaled for the range of ice mass variations

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Fig. 6

Ice horn height versus the ice mass at section 12

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Fig. 7

The 2D model domain, defined boundaries and the mesh configuration around the airfoil for two arbitrary ice profiles

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Fig. 8

The airfoil data for NACA 64: lift—drag and pitching moment coefficients are named CL, CD, CM, respectively

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Fig. 9

Tangential and normal forces definition

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Fig. 10

Flow streamlines at two cases of ice accretion, top: 36 kg; bottom: 1 kg

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Fig. 11

Linear expansion of PC and the corresponding PDF of the uncertain function

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Fig. 12

Comparison of surrogate maps with PC expansions of different orders

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Fig. 13

Top—PCE surrogate map for a polynomial order 4 using different number of samples: np = 1,2,3. Bottom—PDF function corresponding to any of the top cases, as well as the probability bars.

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Fig. 14

Convergence of PCE error versus the number of sample points

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Fig. 15

Drag coefficient; right: PCE surrogate map, left: probability

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Fig. 16

Lift coefficient; right: PCE surrogate map, left: probability

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Fig. 17

Polynomial chaos expansions normalized load loss versus the ice horn height on section 12

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Fig. 18

Midload loss value Δ*; PDF function of normalized load loss

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Fig. 19

Radial distribution of power loss regarding the relative ice horn height



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