Details
| Original language | English |
|---|---|
| Article number | 052044 |
| Number of pages | 10 |
| Journal | Journal of Physics: Conference Series |
| Volume | 2767 |
| Issue number | 5 |
| Publication status | Published - 2024 |
| Event | Science of Making Torque from Wind, TORQUE 2024 - Florence, Italy Duration: 29 May 2024 → 31 May 2024 |
Abstract
The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Mechanics of Materials
Research Area (based on ÖFOS 2012)
- TECHNICAL SCIENCES
- Construction Engineering
- Civil Engineering
- Computational engineering
- TECHNICAL SCIENCES
- Environmental Engineering, Applied Geosciences
- Environmental Engineering
- Renewable energy
- TECHNICAL SCIENCES
- Mechanical Engineering
- Mechanical Engineering
- Micromechanics
- TECHNICAL SCIENCES
- Mechanical Engineering
- Mechanical Engineering
- Computational engineering
Sustainable Development Goals
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In: Journal of Physics: Conference Series, Vol. 2767, No. 5, 052044, 2024.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Leveraging Vision Transformers for Detecting Porosity in Wind Energy Composites
AU - Khan, A. W.
AU - Balzani, C.
N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.
PY - 2024
Y1 - 2024
N2 - The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.
AB - The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.
UR - http://www.scopus.com/inward/record.url?scp=85196517291&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2767/5/052044
DO - 10.1088/1742-6596/2767/5/052044
M3 - Conference article
AN - SCOPUS:85196517291
VL - 2767
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 5
M1 - 052044
T2 - Science of Making Torque from Wind, TORQUE 2024
Y2 - 29 May 2024 through 31 May 2024
ER -