A Comparative Study: Predictive Modeling of Wind Turbine Blades

Author:

Van Buren Kendra L.,Atamturktur Sez1

Affiliation:

1. Assistant Professor

Abstract

The structural analysis of wind turbine blades is completed using vastly different computational modeling strategies with varying levels of model sophistication and detail. Typically, preference of one modeling strategy over the other is decided according to subjective judgment of the expert. The central question that arises is how to justify the chosen level of sophistication and detail through quantitative, objective and scientifically defendable metrics. This manuscript takes a step toward answering this question and investigates the necessary level of sophistication and detail needed while modeling the cross-section of wind turbine blades by: i) rigorously quantifying the model incompleteness resulting from simplifying assumptions and ii) comparing the predictive maturity index associated with alternative modeling strategies. The concept of predictive maturity is illustrated on a prototype blade. The incompleteness of five alternative models with varying sophistication in the cross section of the shell elements are assessed through model form error and predictive maturity index. While model form error is observed as constant for varying levels of sophistication, through the predictive maturity index, it is found that models with lesser sophistication may have predictive capabilities comparable to more sophisticated, computationally expensive models.

Publisher

SAGE Publications

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Model calibration of locally nonlinear dynamical systems;Engineering Computations;2019-03-11

2. Evaluating the fidelity and robustness of calibrated numerical model predictions;Engineering Computations;2015-05-05

3. A Resource Allocation Framework for Experiment-Based Validation of Numerical Models;Mechanics of Advanced Materials and Structures;2014-06-30

4. Validation of Strongly Coupled Models: A Framework for Resource Allocation;Model Validation and Uncertainty Quantification, Volume 3;2014

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