Simulating the Dynamics of Wind Turbine Blades: Part II, Model Validation and Uncertainty Quantification

Verification and validation (V&V) offers the potential to play an indispensable role in the development of credible models for the simulation of wind turbines. This paper highlights the development of a three-dimensional finite element model of the CX-100 wind turbine blade. The scientific hypothesis that we wish to confirm by applying V&V activities is that it is possible to develop a fast-running model capable of predicting the low-order vibration dynamics with sufficient accuracy. A computationally efficient model is achieved by segmenting the geometry of the blade into six sections only. It is further assumed that each cross section can be homogenized with isotropic material properties. The main objectives of V&V activities deployed are to, first, assess the extent to which these assumptions are justified and, second, to quantify the resulting prediction uncertainty. Designs of computer experiments are analyzed to understand the effects of parameter uncertainty and identify the significant sensitivities. A calibration of model parameters to natural frequencies predicted by the simplified model is performed in two steps with the use of, first, a free–free configuration of the blade and, second, a fixed–free configuration. This two-step approach is convenient to decouple the material properties from parameters of the model that describe the boundary condition. Here, calibration is not formulated as an optimization problem. Instead, it is viewed as a problem of inference uncertainty quantification where measurements are used to learn the uncertainty of model parameters. Gaussian process models, statistical tests and Markov chain Monte Carlo sampling are combined to explore the (true but unknown) joint probability distribution of parameters that, when sampled, produces bounds of prediction uncertainty that are consistent with the experimental variability. An independent validation assessment follows the calibration and is applied to mode shape vectors. Despite the identification of isolated issues with the simulation code and model developed, the overarching conclusion is that the modeling strategy is sound and leads to an accurate-enough, fast-running simulation of blade dynamics. This publication is Part II of a two-part effort that highlights the V&V steps required to develop a robust model of a wind turbine blade, where Part I emphasizes code verification and the quantification of numerical uncertainty. Approved for unlimited public release on August 26, 2011, LA-UR-11-4997. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Daniel L. Laird,et al.  Estimation of uncertain material parameters using modal test data , 1998 .

[2]  François M. Hemez,et al.  Simulating the dynamics of wind turbine blades: part I, model development and verification , 2011 .

[3]  Wenyi Liu,et al.  Status and problems of wind turbine structural health monitoring techniques in China , 2010 .

[4]  Ola Carlson,et al.  Validation of fixed speed wind turbine dynamic models with measured data , 2007 .

[5]  J. Sørensen,et al.  Wind turbine wake aerodynamics , 2003 .

[6]  Walter Musial,et al.  Determining equivalent damage loading for full-scale wind turbine blade fatigue tests , 2000 .

[7]  Brian Ray Resor,et al.  An Evaluation of Wind Turbine Blade Cross Section Analysis Techniques , 2010 .

[8]  F. M. Jensen,et al.  Structural testing and numerical simulation of a 34 m composite wind turbine blade , 2006 .

[9]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[10]  Jung-Ryul Lee,et al.  Structural health monitoring for a wind turbine system: a review of damage detection methods , 2008 .

[11]  Daniel L. Laird,et al.  Extraction of equivalent beam properties from blade models , 2007 .

[12]  Christopher A. Walford,et al.  Wind Turbine Reliability: Understanding and Minimizing Wind Turbine Operation and Maintenance Costs , 2006 .

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

[14]  Thomas D. Ashwill,et al.  Concepts to Facilitate Very Large Blades. , 2007 .

[15]  A. O'Hagan,et al.  Predicting the output from a complex computer code when fast approximations are available , 2000 .

[16]  Thomas G. Carne,et al.  Development of Validated Blade Structural Models , 2008 .

[17]  Mark A. Rumsey,et al.  Modal Analysis of CX-100 Rotor Blade and Micon 65/13 Wind Turbine , 2011 .

[18]  Joseph A. C. Delaney Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[19]  D. Askeland,et al.  The science and engineering of materials , 1984 .

[20]  Sandia Report,et al.  Design of 9-Meter Carbon-Fiberglass Prototype Blades: CX-100 and TX-100 , 2007 .

[21]  Thomas G. Carne,et al.  Modal Testing for Validation of Blade Models , 2008 .

[22]  François M. Hemez,et al.  The Good , The Bad , and The Ugly of Predictive Science , 2005 .

[23]  G. E. Wilson,et al.  The role of the PIRT process in experiments, code development and code applications associated with Reactor Safety analysis , 1998 .

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

[25]  Kevin M. Farinholt,et al.  Modal Analysis and SHM Investigation of CX-100 Wind Turbine Blade , 2011 .

[26]  Philip Clausen,et al.  STRUCTURAL DESIGN OF A COMPOSITE WIND TURBINE BLADE USING FINITE ELEMENT ANALYSIS , 1997 .

[27]  Walter Musial,et al.  Trends in the Design, Manufacture and Evaluation of Wind Turbine Blades , 2003 .

[28]  D. Higdon,et al.  Computer Model Calibration Using High-Dimensional Output , 2008 .

[29]  Daniel L. Laird,et al.  Finite Element Modeling of Wind Turbine Blades , 2005 .