Identification of Debonding Damage at Spar Cap-Shear Web Joints by Artificial Neural Network Using Natural Frequency Relevant Key Features of Composite Wind Turbine Blades

Autor: Hyeong-Jin Kim, Hak-Geun Kim, Yun-Jung Jang, Ki-Weon Kang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Technology
Turbine blade
natural frequency relevant key features
QH301-705.5
020209 energy
Modal analysis
QC1-999
02 engineering and technology
Turbine
law.invention
0203 mechanical engineering
law
composite blade
0202 electrical engineering
electronic engineering
information engineering
medicine
General Materials Science
Spar
Biology (General)
Instrumentation
QD1-999
Fluid Flow and Transfer Processes
Wind power
debonding damage
business.industry
Process Chemistry and Technology
Physics
General Engineering
Stiffness
Natural frequency
Structural engineering
spar cap-shear web joint debonding damage
Engineering (General). Civil engineering (General)
Computer Science Applications
Shear (sheet metal)
Chemistry
020303 mechanical engineering & transports
medicine.symptom
TA1-2040
business
Geology
artificial neural network
Zdroj: Applied Sciences
Volume 11
Issue 12
Applied Sciences, Vol 11, Iss 5327, p 5327 (2021)
ISSN: 2076-3417
DOI: 10.3390/app11125327
Popis: As the size and weight of blades increase with the recent trend toward larger wind turbines, it is important to ensure the structural integrity of the blades. For this reason, the blade consists of an upper and lower skin that receives the load directly, a shear web that supports the two skins, and a spar cap that connects the skin and the shear web. Loads generated during the operation of the wind turbine can cause debonding damage on the spar cap-shear web joints. This may change the structural stiffness of the blade and lead to a lack of integrity
therefore, it would be beneficial to be able to identify possible damage in advance. In this paper we present a model to identify debonding damage based on natural frequency. This was carried out by modeling 1105 different debonding damages, which were classified by configuration type, location, and length. After that, the natural frequencies, due to the debonding damage of the blades, were obtained through modal analysis using FE analysis. Finally, an artificial neural network was used to study the relationship between debonding damage and the natural frequencies.
Databáze: OpenAIRE
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