scholarly journals 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

2021 ◽  
Vol 11 (12) ◽  
pp. 5327
Author(s):  
Yun-Jung Jang ◽  
Hyeong-Jin Kim ◽  
Hak-Geun Kim ◽  
Ki-Weon Kang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Natural Frequency ◽  
Structural Integrity ◽  
Natural Frequencies ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Artificial Neural ◽  
The Relationship

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.

scholarly journals Identification of ice mass accumulated on wind turbine blades using its natural frequencies

2017 ◽  
Vol 42 (1) ◽  
pp. 66-84 ◽  
Author(s):  
Sudhakar Gantasala ◽  
Jean-Claude Luneno ◽  
Jan-Olov Aidanpää
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Natural Frequencies ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Explicit Relation

This work demonstrates a technique to identify information about the ice mass accumulation on wind turbine blades using its natural frequencies, and these frequencies reduce differently depending on the spatial distribution of ice mass along the blade length. An explicit relation to the natural frequencies of a 1-kW wind turbine blade is defined in terms of the location and quantity of ice mass using experimental modal analyses. An artificial neural network model is trained with a data set (natural frequencies and ice masses) generated using that explicit relation. After training, this artificial neural network model is given an input of natural frequencies of the iced blade (identified from experimental modal analysis) corresponding to 18 test cases, and it identified ice masses’ location and quantity with a weighted average percentage error value of 17.53%. The proposed technique is also demonstrated on the NREL 5-MW wind turbine blade data.

scholarly journals Review of Icing Effects on Wind Turbine in Cold Regions

2018 ◽  
Vol 72 ◽  
pp. 01007 ◽  
Author(s):  
Faizan Afzal ◽  
Muhammad S. Virk
Keyword(s):  
Wind Turbine ◽  
Structural Integrity ◽  
Turbine Blades ◽  
Leading Edge ◽  
Added Mass ◽  
Cold Climate ◽  
Ice Accretion ◽  
Wind Turbine Blades ◽  
Turbine Performance ◽  
Ice Shedding

This paper describes a brief overview of main issues related to atmospheric ice accretion on wind turbines installed in cold climate region. Icing has significant effects on wind turbine performance particularly from aerodynamic and structural integrity perspective, as ice accumulates mainly on the leading edge of the blades that change its aerodynamic profile shape and effects its structural dynamics due to added mass effects of ice. This research aims to provide an overview and develop further understanding of the effects of atmospheric ice accretion on wind turbine blades. One of the operational challenges of the wind turbine blade operation in icing condition is also to overcome the process of ice shedding, which may happen due to vibrations or bending of the blades. Ice shedding is dangerous phenomenon, hazardous for equipment and personnel in the immediate area.

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

scholarly journals Solving multiple linear regression problem using artificial neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 770
Author(s):  
Mohammad S. Khrisat ◽  
Ziad A. Alqadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Problem ◽  
Simple Method ◽  
Scientific Experiments ◽  
Linear Regression Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship ◽  
Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

Acoustic Emission for Structural Integrity Assessment of Wind Turbine Blades

2014 ◽  
pp. 369-382 ◽  
Author(s):  
Nikolaos K. Tsopelas ◽  
Dimitrios G. Papasalouros ◽  
Athanasios A. Anastasopoulos ◽  
Dimitrios A. Kourousis ◽  
Jason W. Dong
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Structural Integrity ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Integrity Assessment
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