Evaluating the fidelity and robustness of calibrated numerical model predictions: An application on a wind turbine blade

Purpose – Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters. Design/methodology/approach – Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model ...

[1]  Zhifeng Liu,et al.  Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach , 2014, Structural and Multidisciplinary Optimization.

[2]  Ian F. C. Smith,et al.  Dynamic behavior and vibration control of a tensegrity structure , 2010 .

[3]  Yuri Bazilevs,et al.  3D simulation of wind turbine rotors at full scale. Part I: Geometry modeling and aerodynamics , 2011 .

[4]  Sven Erik Jørgensen.  Info-Gap, Decision Theory , 2008 .

[5]  J. C. Marín,et al.  Study of fatigue damage in wind turbine blades , 2009 .

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

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

[8]  E. Sadoulet-Reboul,et al.  Robust Model Calibration with Load Uncertainties , 2013 .

[9]  Sez Atamturktur,et al.  Prioritization of Code Development Efforts in Partitioned Analysis , 2013, Comput. Aided Civ. Infrastructure Eng..

[10]  B. Koren,et al.  Review of computational fluid dynamics for wind turbine wake aerodynamics , 2011 .

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

[12]  M. Ramachandran,et al.  Application of multi-criteria decision making to sustainable energy planning--A review , 2004 .

[13]  François M. Hemez,et al.  A forecasting metric for predictive modeling , 2011 .

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

[15]  Ian F. C. Smith,et al.  Measurement System Configuration for Damage Identification of Continuously Monitored Structures , 2012 .

[16]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[17]  Kendra L. Van Buren,et al.  Simulating the Dynamics of Wind Turbine Blades: Part II, Model Validation and Uncertainty Quantification , 2011 .

[18]  Sez Atamturktur,et al.  A Comparative Study: Predictive Modeling of Wind Turbine Blades , 2012 .

[19]  François M. Hemez,et al.  Info-gap robustness for the correlation of tests and simulations of a non-linear transient , 2004 .

[20]  T. L. Saaty,et al.  The computational algorithm for the parametric objective function , 1955 .

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

[22]  Ian Fleming,et al.  A model for the structural dynamic response of the CX‐100 wind turbine blade , 2014 .

[23]  Sankaran Mahadevan,et al.  Multivariate significance testing and model calibration under uncertainty , 2008 .

[24]  M. Caramia,et al.  Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithms , 2008 .

[25]  Gyuhae Park,et al.  Full-scale fatigue tests of CX-100 wind turbine blades. Part I: testing , 2012, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[26]  Kendra L. Van Buren,et al.  Simulating the Dynamics of Wind Turbine Blades: Part I, Model Development and Verification , 2011 .

[27]  I. Smith,et al.  Structural identification with systematic errors and unknown uncertainty dependencies , 2013 .

[28]  S. G. Pierce,et al.  Info-Gap Decision Theory—Decisions under Severe Uncertainty, 2nd ed., Yakov Ben-Haim , 2008 .

[29]  Thomas D. Ashwill,et al.  Materials and Innovations for Large Blade Structures: Research Opportunities in WInd Energy Technology , 2009 .

[30]  Nielen Stander,et al.  Multi-Objective Optimization Using LS-OPT , 2007 .

[31]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[32]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[33]  Y. Ben-Haim.  Info-Gap Decision Theory: Decisions Under Severe Uncertainty , 2006 .

[34]  François M. Hemez,et al.  Improved best estimate plus uncertainty methodology, including advanced validation concepts, to license evolving nuclear reactors , 2011 .

[35]  François M. Hemez,et al.  Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments , 2012 .

[36]  François M. Hemez,et al.  Robustness, fidelity and prediction-looseness of models , 2012, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[37]  I. Kim,et al.  Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation , 2006 .