scholarly journals Development of a High-fidelity Experimental Substructure Test Rig for Grid-scored Sandwich Panels in Wind Turbine Blades

Strain ◽  
2013 ◽  
Vol 50 (2) ◽  
pp. 111-131 ◽  
Author(s):  
S. Laustsen ◽  
E. Lund ◽  
L. Kühlmeier ◽  
O. T. Thomsen
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Sandwich Panels ◽  
High Fidelity ◽  
Test Rig ◽  
Wind Turbine Blades

Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms

2016 ◽  
Author(s):  
Taylor Regan ◽  
Rukiye Canturk ◽  
Elizabeth Slavkovsky ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

High Fidelity Analysis of Dynamic Stresses in Wind Turbine Blades

2020 ◽  
Author(s):  
MASOUD SAFDARI ◽  
MOHAMMAD MEHRABADI ◽  
SETH PEMBERTON ◽  
ALESSANDRO GONDOLO
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
High Fidelity ◽  
Wind Turbine Blades ◽  
Dynamic Stresses

scholarly journals Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Taylor Regan ◽  
Christopher Beale ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Learning Algorithms ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather-induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring (SHM) in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision-making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

scholarly journals High-fidelity finite element models of composite wind turbine blades with shell and solid elements

2018 ◽  
Vol 200 ◽  
pp. 521-531 ◽  
Author(s):  
Mathijs Peeters ◽  
Gilberto Santo ◽  
Joris Degroote ◽  
Wim Van Paepegem
Keyword(s):  
Finite Element ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Finite Element Models ◽  
High Fidelity ◽  
Wind Turbine Blades

scholarly journals High Fidelity Aero-Structural Simulation of Occluded Wind Turbine Blades

2021 ◽  
Author(s):  
Thomas R. Wainwright ◽  
Daniel J. Poole ◽  
Christian B. Allen ◽  
Jamil Appa
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
High Fidelity ◽  
Wind Turbine Blades ◽  
Structural Simulation

Experiments of Wind Turbine Blades with Rocket Triggered Lightning

2009 ◽  
Vol 129 (5) ◽  
pp. 689-695
Author(s):  
Masayuki Minowa ◽  
Shinichi Sumi ◽  
Masayasu Minami ◽  
Kenji Horii
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Triggered Lightning
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