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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.

In the state of the art of modeling and simulation of wind turbines, verification and validation (V&V) is a somewhat underdeveloped field. The purpose of this paper is to spotlight the process of a completely integrated V&V procedure, as it is applied to a wind turbine blade. The novelty, besides illustrating the application of V&V to blade modeling, is to challenge the conventional separation between verification and validation activities. First, simple closed‐form solutions for bending stress, torsional stress and mode shapes of a hollow cylinder are derived analytically to verify the ANSYS finite element software. Shell‐281 elements are used to approximate these closed‐form solutions and demonstrate that the software runs properly. The grid convergence index is used to quantify the degree of numerical uncertainty that results. Next, model development and verification activities are applied to the CX‐100 blade designed at the Sandia National Laboratories. A three‐dimensional model is developed based on the actual geometry of the CX‐100 blade. For simplicity, the model assumes smeared cross sections with uniform, isotropic material properties. Solution verification is performed to quantify the numerical uncertainty due to mesh discretization of the finite element model. The mesh refinement study provides evidence that the model leads to numerical solutions located in the regime of asymptotic convergence. We depart from the conventional V&V paradigm by proposing that the level of mesh discretization should be based on an assessment of experimental variability. Instead of choosing the mesh size ‘in a vacuum’, it is selected such that the overall numerical uncertainty caused by truncation effects is similar to, or smaller than, the test‐to‐test variability. This rationale guarantees that predictions are sufficiently accurate relative to the level of uncertainty with which physical tests can be replicated. Part II of this work highlights the V&V steps implemented to quantify sensitivities of the model and further quantify the prediction uncertainty caused by our imperfect knowledge of the idealized material description. Copyright © 2012 John Wiley & Sons, Ltd.

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SUMMARYStructural health monitoring requires tracking the progression of damage in a structure over time. In this paper, we propose a model for progressive damage that utilizes a hidden Markov model where multiple states represent the severity and type of damage. Sensor observations are generated according to a process conditional on each state. An efficient algorithm for sequential inference of damage from collected data is proposed and tested using experimental data from a structural frame.