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diva-portal.org
article
https://www.diva-portal.org/smash/get/diva2:1560523/FULLTEXT01.pdf
v List of Abbreviations and Symbols DEL: Damage Equivalent Load ANN: Artificial Neural Network GPU: Graphics Processing Unit SCADA: Supervisory Control and Data Acquisition RMSE: Root Mean Square Error SGD: Stochastic Gradient Descent NN: Neural Network MLP: Multilayer Perceptron FFNN: Feed-Forward Neural Network ReLU: Rectified Linear Unit Function STD: Standard Deviation MAPE: Mean Absolute Percentage Error NRMSE: Normalized Root Mean Square Error PCE: Polynomial Chaos Expansion MAE: Mean Absolute Error FEM: Finite Element Method PCA: Principal Component Analysis NCA: Neighbourhood Component Analysis NEWA: New European Wind Atlas TKE: Turbulence Kinetic Energy IDE: Integrated Development Environment π
π
π
π
: Reynoldβs number ππ: fluid density ππ: flow speed vi πΏπΏ: characteristic length ππ: dynamic viscosity of the fluid ππ ππππππ: relative inflow velocity ππππ: normal force per length πππ‘π‘: tangential force per length ππ: local radius of blade ππππ: incremental length π
π
: rotor radius/Pearsonβs correlation coefficient ππ ππππππππππππππππ,ππππππππ: flapwise bending moment at blade root ππππππππππππππππππ,ππππππππ: flapwise bending moment at blade root ππ= ππ: stress ππ: strain πΏπΏππππππππππππππππ: consumed fatigue life πΏπΏππππππππππππππππππ: remaining fatigue life ππππππ: equivalent bending moment ππ: WΓΆhler number πΌπΌ: learning rate D: rotor diameter vii Table of Contents Abstract ................................................................................................................................................. For instance, in two separate studies by Zhou et al., (2018) and Vera-Tudela & KΓΌhn, (2017), DEL values for turbine blades in flapwise and edgewise directions have been predicted using 10-min SCADA data by ANNs. Although there are similarities between the methodologies, they have resulted in different results regarding the used features, the model structure, pre-processing steps, and achieved accuracies.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S096014811731073X
Predicting progressive failure and consequential loss in the load-bearing capability of large-scale composite wind blades is vital for accurately assessing
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osti.gov
official
https://www.osti.gov/servlets/purl/1111678
The fatigue analysis of a wind turbine component typically uses representative samples of cyclic loads to determine lifetime loads. In this paper, several
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linkedin.com
article
https://www.linkedin.com/pulse/silent-threat-analyzing-fatigue-failure-wind-tβ¦
β οΈ Common Causes of Fatigue in Blades Β· Variable Wind Loads Constantly changing wind speeds and turbulence create irregular stress cycles.
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pubs.aip.org
article
https://pubs.aip.org/aip/adv/article/13/6/065123/2897391/Experimental-on-the-β¦
The dominant type of vibration in wind turbine blades is flapwise vibration, which causes fatigue loading owing to the out-of-plane alternating load.
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mdpi.com
article
https://www.mdpi.com/1996-1073/16/24/7934
permission is required to reuse all or part of the article published by MDPI, including figures and tables. In this paper, we discuss the application of durability and damage tolerance analysis (DADTA) approaches to trailing edge service life prediction. DADTA is mandated in the aerospace sector to support airworthiness certification and to provide an updated life prediction of the structure based on the different stages of their service life. The current paper provides an extensive review of these methods and shows how these can be applied to the wind turbine blade industry, specifically for predicting the structural design life of the trailing edge of composite wind turbine blades. The review includes (a) defining wind turbine trailing edge failure modes, (b) trailing edge design procedures, and (c) a detailed discussion of the application of durability and damage tolerance analysis for trailing edge life prediction.
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papers.ssrn.com
article
https://papers.ssrn.com/sol3/Delivery.cfm/60bff25e-4d82-43cc-9e1b-c25e75d9fcaβ¦
There are deterministic loads due to bending caused by the blade weight, these loads are usually within the plane of rotation or edge direction (edgewise, FYB)
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pmc.ncbi.nlm.nih.gov
official
https://pmc.ncbi.nlm.nih.gov/articles/PMC9101399
# Root Causes and Mechanisms of Failure of Wind Turbine Blades: Overview. A review of the root causes and mechanisms of damage and failure to wind turbine blades is presented in this paper. In particular, the mechanisms of leading edge erosion, adhesive joint degradation, trailing edge failure, buckling and blade collapse phenomena are considered. The role of manufacturing defects (voids, debonding, waviness, other deviations) for the failure mechanisms of wind turbine blades is highlighted. It is concluded that the strength and durability of wind turbine blades is controlled to a large degree by the strength of adhesive joints, interfaces and thin layers (interlaminar layers, adhesives) in the blade. In this paper, the mechanisms of degradation and failure of wind turbine blades under service conditions are reviewed, with a view also on the role of manufacturing defects and possible solutions.