scholarly journals A Staged Approach to Erosion Analysis of Wind Turbine Blade Coatings

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 681
Author(s):  
David Nash ◽  
Grant Leishman ◽  
Cameron Mackie ◽  
Kirsten Dyer ◽  
Liu Yang
Keyword(s):  
Computed Tomography ◽  
Wind Turbine ◽  
Incubation Period ◽  
Leading Edge ◽  
X Ray ◽  
Erosion Testing ◽  
Staged Approach ◽  
X Ray Computed ◽  
Droplet Impacts ◽  
Rain Erosion

The current wind turbine leading-edge erosion research focuses on the end of the incubation period and breakthrough when analysing the erosion mechanism. This work presented here shows the benefits of splitting and describing leading-edge erosion progression into discrete stages. The five identified stages are: (1) an undamaged, as-new, sample; (2) between the undamaged sample and end of incubation; (3) the end of incubation period; (4) between the end of incubation and breakthrough, and (5) breakthrough. Mass loss, microscopy and X-ray computed tomography were investigated at each of the five stages. From this analysis, it was observed that notable changes were detected at Stages 2 and 4, which are not usually considered separately. The staged approach to rain erosion testing offers a more thorough understanding of how the coating system changes and ultimately fails due to rain droplet impacts. It is observed that during microscopy and X-ray computed tomography, changes unobservable to the naked eye can be tracked using the staged approach.

scholarly journals Time-Lapse Helical X-ray Computed Tomography (CT) Study of Tensile Fatigue Damage Formation in Composites for Wind Turbine Blades

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2340 ◽  
Author(s):  
Ying Wang ◽  
Lars Mikkelsen ◽  
Grzegorz Pyka ◽  
Philip Withers
Keyword(s):  
Computed Tomography ◽  
Wind Turbine ◽  
Fatigue Damage ◽  
Turbine Blades ◽  
Time Lapse ◽  
Ex Situ ◽  
Wind Turbine Blades ◽  
Damage Mechanisms ◽  
X Ray ◽  
X Ray Computed

Understanding the fatigue damage mechanisms in composite materials is of great importance in the wind turbine industry because of the very large number of loading cycles rotor blades undergo during their service life. In this paper, the fatigue damage mechanisms of a non-crimp unidirectional (UD) glass fibre reinforced polymer (GFRP) used in wind turbine blades are characterised by time-lapse ex-situ helical X-ray computed tomography (CT) at different stages through its fatigue life. Our observations validate the hypothesis that off-axis cracking in secondary oriented fibre bundles, the so-called backing bundles, are directly related to fibre fractures in the UD bundles. Using helical X-ray CT we are able to follow the fatigue damage evolution in the composite over a length of 20 mm in the UD fibre direction using a voxel size of (2.75 µm)3. A staining approach was used to enhance the detectability of the narrow off-axis matrix and interface cracks, partly closed fibre fractures and thin longitudinal splits. Instead of being evenly distributed, fibre fractures in the UD bundles nucleate and propagate locally where backing bundles cross-over, or where stitching threads cross-over. In addition, UD fibre fractures can also be initiated by the presence of extensive debonding and longitudinal splitting, which were found to develop from debonding of the stitching threads near surface. The splits lower the lateral constraint of the originally closely packed UD fibres, which could potentially make the composite susceptible to compressive loads as well as the environment in service. The results here indicate that further research into the better design of the positioning of stitching threads, and backing fibre cross-over regions is required, as well as new approaches to control the positions of UD fibres.

X-Ray Computed Tomography of Ultralightweight Metals

1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
X Ray Computed

scholarly journals Nano- scale X-ray Computed Tomography: Morphological Analysis of Fuel Cell Electrodes

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster
Keyword(s):  
Computed Tomography ◽  
Fuel Cell ◽  
Morphological Analysis ◽  
Fuel Cell Electrodes ◽  
X Ray ◽  
Nano Scale ◽  
X Ray Computed

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

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