Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty

This paper studies reliability-based design optimization (RBDO) of a 5-MW wind turbine blade for designing reliable as well as economical wind turbine blades. A novel dynamic wind load uncertainty model has been developed using 249 groups of wind data to consider wind load variation over a large spatiotemporal range. The probability of fatigue failure during a 20-year service life is estimated using the uncertainty model in the RBDO process and is reduced to meet a desired target reliability. Meanwhile, the cost of composite materials used in the blade is minimized by optimizing the composite laminate thicknesses of the blade. In order to obtain the RBDO optimum design efficiently, deterministic design optimization (DDO) of the 5-MW wind turbine blade is carried out first using the mean wind load obtained from the wind load uncertainty model. The RBDO is then initiated from the DDO optimum. During the RBDO iterations, fatigue hotspots for RBDO are identified among the laminate section points. For an efficient RBDO process, surrogate models of 10-min fatigue damages D10 at the hotspots are accurately created using the Kriging method. Using the wind load uncertainty model and surrogate models, probability of fatigue failure during a 20-year lifespan at the hotspots and the design sensitivities are calculated at given design points. Using the probability of fatigue failure and design sensitivity, RBDO of the 5-MW wind turbine blade has been successfully carried out, satisfying the target probability of failure of 2.275 %.

[1]  Lance Manuel,et al.  PARAMETRIC MODELS FOR ESTIMATING WIND TURBINE FATIGUE LOADS FOR DESIGN , 2000 .

[2]  Dick Veldkamp.  A probabilistic evaluation of wind turbine fatigue design rules , 2008 .

[3]  S. Report,et al.  The Sandia 100-meter All-glass Baseline Wind Turbine Blade: SNL100-00 , 2011 .

[4]  B. Jonkman.  Turbsim User's Guide: Version 1.50 , 2009 .

[5]  Kyung K. Choi,et al.  Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation , 2016 .

[6]  C. Kong,et al.  Structural investigation of composite wind turbine blade considering various load cases and fatigue life , 2005 .

[7]  Yoshihiko Sugiyama,et al.  Investigation of fatigue life for a medium scale composite wind turbine blade , 2006 .

[8]  Kyung K. Choi,et al.  Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades , 2016 .

[9]  Nicholas J. Gaul,et al.  Reliability Analysis of Wind Turbine Blades for Fatigue Life under Wind Load Uncertainty , 2012 .

[10]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[11]  Sankaran Mahadevan,et al.  Probabilistic fatigue life prediction of multidirectional composite laminates , 2005 .

[12]  N. Jenkins,et al.  Wind Energy Handbook: Burton/Wind Energy Handbook , 2011 .

[13]  C. P. van Dam,et al.  Innovative Design Approaches for Large Wind Turbine Blades , 2005 .

[14]  J. Mcgowan,et al.  Wind Energy Explained , 2002 .

[15]  M. Hohenbichler,et al.  Improvement Of Second‐Order Reliability Estimates by Importance Sampling , 1988 .

[16]  Michael Taylor.  Measure Theory and Integration , 2006 .

[17]  Steven R. Winterstein,et al.  Application of Measured Loads to Wind Turbine Fatigue and Reliability Analysis , 1998 .

[18]  Knut O. Ronold,et al.  Reliability-based design of wind-turbine rotor blades against failure in ultimate loading , 2000 .

[19]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[20]  P. S. Veers,et al.  Fatigue case study and reliability analyses for wind turbines , 1994 .

[21]  Po Wen Cheng,et al.  A reliability based design methodology for extreme responses of offshore wind turbines , 2002 .

[22]  N. J. Tarp-Johansen.  Examples of Fatigue Lifetime and Reliability Evaluation of Larger Wind Turbine Components , 2003 .

[23]  Kyung K. Choi,et al.  Identification of marginal and joint CDFs using Bayesian method for RBDO , 2009 .

[24]  Achintya Haldar,et al.  Probability, Reliability and Statistical Methods in Engineering Design (Haldar, Mahadevan) , 1999 .

[25]  Patrick Moriarty,et al.  AeroDyn Theory Manual , 2005 .

[26]  Kyung K. Choi,et al.  Metamodeling Method Using Dynamic Kriging for Design Optimization , 2011 .

[27]  Henrik O. Madsen,et al.  Structural Reliability Methods , 1996 .

[28]  Knut O. Ronold,et al.  Reliability-based fatigue design of wind-turbine rotor blades , 1999 .

[29]  John Dalsgaard Sørensen,et al.  Reliability-Based Design of Wind Turbine Blades , 2011 .

[30]  Roham Rafiee,et al.  Simulation of fatigue failure in a full composite wind turbine blade , 2006 .

[31]  W. E. Holley,et al.  Extrapolation of Extreme and Fatigue Loads Using Probabilistic Methods , 2004 .

[32]  Michael F. Goodchild,et al.  On the prediction error variance of three common spatial interpolation schemes , 2006, Int. J. Geogr. Inf. Sci..

[33]  Tetsuo Tomiyama,et al.  Reliability of wind turbine technology through time , 2008 .

[34]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[35]  K. Choi,et al.  Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables , 2011, DAC 2010.

[36]  B. Jonkman,et al.  TurbSim User's Guide , 2005 .

[37]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[38]  Kyung K. Choi,et al.  A New Fatigue Analysis Procedure for Composite Wind Turbine Blades , 2014 .

[39]  Knut O. Ronold,et al.  Optimization of a design code for wind-turbine rotor blades in fatigue , 2001 .