Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating

Heather Turnbull, Piotr Omenzetter

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

8 Downloads (Pure)

Abstract

The recent shift towards development of clean, sustainable energy sources has provided a new challenge in terms of structural safety and reliability: with aging, manufacturing defects, harsh environmental and operational conditions, and extreme events such as lightning strikes wind turbines can become damaged resulting in production losses and environmental degradation. To monitor the current structural state of the turbine, structural health monitoring (SHM) techniques would be beneficial. Physics based SHM in the form of calibration of a finite element model (FEMs) by inverse techniques is adopted in this research. Fuzzy finite element model updating (FFEMU) techniques for damage severity assessment of a small-scale wind turbine blade are discussed and implemented. The main advantage is the ability of FFEMU to account in a simple way for uncertainty within the problem of model updating. Uncertainty quantification techniques, such as fuzzy sets, enable a convenient mathematical representation of the various uncertainties. Experimental frequencies obtained from modal analysis on a small-scale wind turbine blade were described by fuzzy numbers to model measurement uncertainty. During this investigation, damage severity estimation was investigated through addition of small masses of varying magnitude to the trailing edge of the structure. This structural modification, intended to be in lieu of damage, enabled non-destructive experimental simulation of structural change. A numerical model was constructed with multiple variable additional masses simulated upon the blades trailing edge and used as updating parameters. Objective functions for updating were constructed and minimized using both particle swarm optimization algorithm and firefly algorithm. FFEMU was able to obtain a prediction of baseline material properties of the blade whilst also successfully predicting, with sufficient accuracy, a larger magnitude of structural alteration and its location.

Original languageEnglish
Title of host publicationNondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017
EditorsH. Felix Wu, Andrew L. Gyekenyesi, Peter J. Shull, Tzu-Yang Yu
PublisherSPIE
Number of pages14
Volume10169
ISBN (Electronic)9781510608245
ISBN (Print)9781510608238
DOIs
Publication statusPublished - 19 Apr 2017
EventConference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XI 2017 - Portland, United States
Duration: 26 Mar 201729 Mar 2017

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume10169
ISSN (Electronic)0277-786X

Conference

ConferenceConference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XI 2017
CountryUnited States
CityPortland
Period26/03/1729/03/17

Fingerprint

damage assessment
Damage Assessment
Model Updating
Turbine Blade
turbine blades
wind turbines
Wind Turbine
Wind turbines
Finite Element Model
Turbomachine blades
Health Monitoring
Blade
Updating
Damage
Structural health monitoring
structural health monitoring
trailing edges
Uncertainty
Uncertainty Quantification
Extreme Events

Keywords

  • Damage detection
  • Firefly algorithm
  • Fuzzy finite element model updating
  • Particle swarm optimization
  • Structural health monitoring
  • Uncertainty quantification
  • Wind turbine blade

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Turnbull, H., & Omenzetter, P. (2017). Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating. In H. F. Wu, A. L. Gyekenyesi, P. J. Shull, & T-Y. Yu (Eds.), Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017 (Vol. 10169). [101692E] (Proceedings of SPIE; Vol. 10169). SPIE. https://doi.org/10.1117/12.2257917

Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating. / Turnbull, Heather; Omenzetter, Piotr.

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017. ed. / H. Felix Wu; Andrew L. Gyekenyesi; Peter J. Shull; Tzu-Yang Yu. Vol. 10169 SPIE, 2017. 101692E (Proceedings of SPIE; Vol. 10169).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Turnbull, H & Omenzetter, P 2017, Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating. in HF Wu, AL Gyekenyesi, PJ Shull & T-Y Yu (eds), Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017. vol. 10169, 101692E, Proceedings of SPIE, vol. 10169, SPIE, Conference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XI 2017, Portland, United States, 26/03/17. https://doi.org/10.1117/12.2257917
Turnbull H, Omenzetter P. Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating. In Wu HF, Gyekenyesi AL, Shull PJ, Yu T-Y, editors, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017. Vol. 10169. SPIE. 2017. 101692E. (Proceedings of SPIE). https://doi.org/10.1117/12.2257917
Turnbull, Heather ; Omenzetter, Piotr. / Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating. Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017. editor / H. Felix Wu ; Andrew L. Gyekenyesi ; Peter J. Shull ; Tzu-Yang Yu. Vol. 10169 SPIE, 2017. (Proceedings of SPIE).
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