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Volume 39 Issue 9
Sep.  2017
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Article Contents
SHUANG Miao, SONG Bo. Reliability analysis of the fatigue life of wind turbines under a non-Gaussian wind field with a full-direction inflow[J]. Chinese Journal of Engineering, 2017, 39(9): 1453-1462. doi: 10.13374/j.issn2095-9389.2017.09.020
Citation: SHUANG Miao, SONG Bo. Reliability analysis of the fatigue life of wind turbines under a non-Gaussian wind field with a full-direction inflow[J]. Chinese Journal of Engineering, 2017, 39(9): 1453-1462. doi: 10.13374/j.issn2095-9389.2017.09.020

Reliability analysis of the fatigue life of wind turbines under a non-Gaussian wind field with a full-direction inflow

doi: 10.13374/j.issn2095-9389.2017.09.020
  • Received Date: 2017-01-11
  • Using the Hermite moment model, three types of wind fields with different probability characteristics, namely the Gaussian, hardening non-Gaussian, and softening non-Gaussian processes, were generated via the Kaimal spectrum for a typical wind turbine under operational conditions. Reliability analysis of the fatigue life of the wind turbine was performed by taking tower base connections as an example with consideration of the joint probability density distribution of the wind direction and mean wind speed. The dynamic response was calculated by an aerodynamic model of the blade and multi-body dynamics, and the time-and frequency-domain characteristics of the response were analyzed. Using linear damage accumulation theory and the Paris equation, the fatigue crack initiation life and crack growth life of the turbine were discussed in detail. Fatigue estimation shows that the crack initiation life of the turbine is more sensitive to non-Gaussian winds, whereas its crack propagation life is less sensitive to the non-Gaussian characteristics of wind load. The influence of the non-Gaussian characteristics of wind load on the fatigue damage of the wind turbine should be considered. In terms of the full-direction inflow, the failure positions of the crack initiation and propagation stages are identical and in the dominant wind direction.

     

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  • [1]
    Saravia C M, Machado S P, Cortínez V H. A composite beam finite element for multibody dynamics:application to large wind turbine modeling. Eng Struct, 2013, 56:1164
    [2]
    Chen X B, Li J, Chen J Y. Wind-induced response analysis of a wind turbine tower including the blade-tower coupling effect. J Zhejiang Univ-SCIENCE A, 2009, 10(11):1573
    [3]
    Jonkman J M, Buhl Jr M L. FAST User's Guide. Technical Report No. NREL/EL-500-38230, 2005
    [4]
    Larsen T J, Hansen A M. How 2 HAWC2 the User's Manual. Risø National Laboratory, 2007
    [5]
    Do T Q, van de Lindt J W, Mahmoud H. Fatigue life fragilities and performance-based design of wind turbine tower base connections. J Struct Eng, 2014, 141(7):04014183-1
    [6]
    Do T Q, Mahmoud H, van de Lindt J W. Fatigue life of wind turbine tower bases throughout Colorado. J Perform Constr Fac, 2015, 29(4):04014109-1
    [7]
    Dawood M, Goyal R, Dhonde H, et al. Fatiguelife assessment of cracked high-Mast Illumination Poles. J Perform Constr Fac, 2014, 28(2):311
    [8]
    Repetto M P, Solari G. Closed-form prediction of the alongwindinduced fatigue of structures. J Struct Eng, 2012, 138(9):1149
    [9]
    Repetto M P, Solari G. Closed form solution of the alongwind-induced fatigue damage to structures. Eng Struct, 2009, 31(10):2414
    [10]
    Repetto M P. Cycle counting methods for bi-modal stationary Gaussian processes. Probabilist Eng Mech, 2005, 20(3):229
    [11]
    Gong K M, Chen X Z. Influence of non-Gaussian wind characteristics on wind turbine extreme response. Eng Struct, 2014, 59:727
    [12]
    Gong K M, Ding J, Chen X Z. Estimation of long-term extreme response of operational and parked wind turbines:validation and some new insights. Eng Struct, 2014, 81:135
    [13]
    Ding J, Gong K M, Chen X Z. Comparison of statistical extrapolation methods for the evaluation of long-term extreme response of wind turbine. Eng Struct, 2013, 57:100
    [14]
    Chou J S, Tu W T. Failure analysis and risk management of a collapsed large wind turbine tower. Eng Fail Anal, 2011, 18(1):295
    [15]
    Jonkman J, Butterfield S, Musial W, et al. Definition of a 5MW Reference Wind Turbine for Offshore System Development. Technical Report, NREL/TP-500-38060, National Renewable Energy Laboratory,2009
    [16]
    Akdağ S A, Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage, 2009, 50(7):1761
    [17]
    Clifton A. 135 m Meteorological Towers at the National Wind Technology Center. NREL Report TP-500-55915, 2016
    [18]
    Ding J,Chen X Z. Moment-based translation model for hardening non-Gaussian response processes. J Eng Mech, 2016, 142(2):06015006-1
    [19]
    Grigoriu M. Spectralrepresentation for a class of non-Gaussian processes. J Eng Mech, 2004, 130(5):541
    [20]
    Shields M D, Deodatis G. A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic vector process by a translation process with applications in wind velocity simulation. Probabilist Eng Mech, 2013, 31:19
    [21]
    Shields M D, Deodatis G, Bocchini P. A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic process by a translation process. Probabilist Eng Mech, 2011, 26(4):511
    [22]
    Bocchini P. Probabilistic Approaches in Civil Engineering:Generation of Random Fields and Structural Identification with Genetic Algorithms[Dissertation]. Bologna:Università di Bologna, 2008
    [23]
    Bengtsson A, Rychlik I. Uncertainty in fatigue life prediction of structures subject to Gaussian loads. Probabilist Eng Mech, 2009, 24(2):224
    [24]
    Low Y M. Variance of the fatigue damage due to a Gaussian narrowband process. Struct Saf, 2012, 34(1):381
    [25]
    Ding J, Chen X Z, Zuo D L, et al. Fatigue life assessment of traffic-signal support structures from an analytical approach and long-term vibration monitoring data. J Struct Eng, 2016, 142(6):04016017-1
    [26]
    Hanaki S, Yamashita M, Uchida H, et al. On stochastic evaluation of S-N data based on fatigue strength distribution. Int J Fatigue, 2010, 32(3):605
    [27]
    Stam A, Richman N, Pool C, et al. Fatigue Life of Steel Base Plate to Pole Connections for Traffic Structures. Austin:Center for Transportation Research, University of Texas at Austin, 2011
    [28]
    Chung H, Manuel L, Frank K H. Optimal Inspection of FractureCritical Steel Trapezoidal Girders. Austin:Center for Transportation Research, University of Texas at Austin, 2003
    [29]
    Mahmoud H N, Dexter R J. Propagation rate of large cracks in stiffened panels under tension loading. Mar Struct, 2005, 18(3):265
    [30]
    Dowling N E. Mechanical Behavior of Materials:Engineering Methods for Deformation, Fracture, and Fatigue. 4th Ed. Boston:Pearson, 2012
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