Open AccessAssessment of extreme stresses and deflections on wind turbine blades with stochastic material properties using statistical extrapolation methods
Author(s) -
Panagiotis N Schinas,
Giannis P. Serafeim,
Dimitris I. Manolas,
Vasilis A. Riziotis,
Spyros G. Voutsinas,
T.P. Philippidis,
P. Chaviaropoulos
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/5/052029
Subject(s) - extrapolation , deflection (physics) , structural engineering , turbine , turbine blade , material properties , extreme value theory , engineering , mathematics , materials science , statistics , mechanical engineering , composite material , physics , optics
Since the introduction of the 3rd edition of the IEC Standard 61400-1, designers of wind turbines are required to apply statistical extrapolation techniques, to estimate the extreme (ultimate) load values corresponding to fifty-year return period. In the present paper, the certification procedure is assessed under the uncertainty of the material properties using simulated load time series of the NREL 5MW Reference Wind Turbine. The uncertainty of the material properties is introduced in the elastic properties of the composite blades by using input data from the OptiDAT composite material database. Comparison of the estimated extreme loads and deflections of the blades as well as maximum stresses, also in connection to the Tsai Wu failure criterion, is performed for different material sets. It is found that the variability of the material properties does not affect the estimated ultimate flapwise moments (difference <1.5%) but affects the maximum flapwise deflection (differences ∼8%). It is concluded that for the levels of variation considered in the composite material properties, the coefficient of variation of the extreme stresses and the Tsai Wu failure criterion are in the order of 2% and 8% respectively.