scholarly journals The Effect of Ply Waviness for the Fatigue Life of Composite Wind Turbine Blades

2013 ◽  
pp. 1195-1199
Author(s):  
U.I.K. Galappaaththi ◽  
Anthony Pickett ◽  
Milos Draskovic ◽  
Mark Capellaro ◽  
A.M. De Silva
Keyword(s):  
Fatigue Life ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Wind Turbine Blades

Effects of shallow angle on static strength and fatigue life of multi-directional laminates for wind turbine blades

2016 ◽  
Vol 51 (18) ◽  
pp. 2549-2563 ◽  
Author(s):  
Sung Kyu Ha ◽  
Alvaro Gorostidi Martinez de Lece ◽  
Carlos Donazar Moriones ◽  
Carlos Alberto Cimini Junior ◽  
Chengzhu Jin
Keyword(s):  
Fatigue Life ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Failure Criteria ◽  
Static Strength ◽  
Experimental Results ◽  
Multiscale Approach ◽  
Wind Turbine Blades ◽  
Static Tests ◽  
Angle Effect

The effects of the shallow angle on the static strength and the fatigue life of the multi-directional glass fiber-reinforced plastics for wind turbine blades were presented based on experimental results and predictions. The static tests and the tension–tension fatigue tests under cyclic fatigue loads with a stress ratio of 0.1 were performed on bi-axial (BX, [±θ]), tri-axial 1 (TA, [0/±θ2]), and tri-axial 2 (TX, [02/±θ]) laminates with ply angles θ of 25°, 35°, and 45°. A multiscale approach was applied to predict the static tensile and compressive strengths and the S–N curves of BX, TA, and TX laminates based on the constituents: fiber, matrix, and interface. Three ply-based failure criteria (Hashin, Puck, and Tsai–Wu) were also employed to predict the static strength and compare with the experimental results. The predictions and the experimental results show that the tensile strength increases as θ becomes shallower, while laminates with a shallow ply angle of 35° showed similar or even lower compressive strengths, especially for TA and TX laminates. The laminate fatigue life increases as θ becomes shallower. The shallow angle effect on strength and fatigue life is greater for BX than TA and TX laminates since the ply angle θ plays a more important role in BX. By using the multiscale approach, the shallow angle effect on the laminate static and fatigue behaviors were also explained based on the ply stresses as well as the constitutive micro stresses.

scholarly journals A cuckoo search based neural network to predict fatigue life in rotor blade composites

2020 ◽  
Vol 14 (1) ◽  
pp. 6430-6442
Author(s):  
Khaled Ziane ◽  
Adrian Ilinca ◽  
Abdullah Khan ◽  
Soraya Zebirate
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Wind Turbine ◽  
Composite Laminates ◽  
Turbine Blades ◽  
Cuckoo Search ◽  
Wind Turbine Blades ◽  
Target Values ◽  
Predicted Values ◽  
The Cost

In modern wind turbine blades industry, fiber-reinforced composites are mostly used for their good mechanical characteristics: high stiffness, low density and long fatigue life. Wind turbine blades are constructed in different structural elements from a variety of composite laminates, often including Unidirectional (UD) material in spars and multiple forms of Multidirectional (MD) in skins and webs.  The purpose of this paper is to identify materials that have appropriate fiber orientations to improve fatigue life. By using Cuckoo Search-based Neural Network (CSNN), we have developed a model to predict fatigue life under tension-tension charges for five composite materials, with different fiber stacking sequences embedded in three types of resin matrices (epoxy, polyester and vinylester), which are all appropriate for the design of wind turbine blades. In the CSNN approach used in this work, the cost function was assessed using the Mean Square Error (MSE) computed as the squared difference between the predicted values and the target values for a number of training set samples, obtained from an experimental fatigue database. The results illustrate that the CSNN can provide accurate fatigue life prediction for different MD/UD composite laminates, under different angles of fiber orientation.

scholarly journals Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions

Eng ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 278-295
Author(s):  
Khaled Ziane ◽  
Adrian Ilinca ◽  
Sasan Sattarpanah Karganroudi ◽  
Mariya Dimitrova
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Cuckoo Search ◽  
Resin Matrix ◽  
Accuracy Evaluation ◽  
Vinyl Esters ◽  
Wind Turbine Blades ◽  
Air Temperatures

Moisture and temperature are the most important environmental factors that affect the degradation of wind turbine blades, and their influence must be considered in the design process. They will first affect the resin matrix and then, possibly, the interface with the fibers. This work is the first to use a series of metaheuristic approaches to analyze the most recent experimental results database and to identify which resins are the most robust to moisture/temperature in terms of fatigue life. Four types of resin are compared, representing the most common types used for wind turbine blades manufacturing. Thermoset polymer resins, including polyesters and vinyl esters, were machined as coupons and tested for the fatigue in air temperatures of 20 °C and 50 °C under “dry” and “wet” conditions. The experimental fatigue data available from Sandia National Laboratories (SNL) for wind turbine-related materials have been used to build, train, and validate an artificial neural network (ANN) to predict fatigue life under different environmental conditions. The performances of three algorithms (Backpropagation BP, Particle Swarm Optimization PSO, and Cuckoo Search CS) are compared for adjusting the synaptic weights of the ANN and evaluating the efficiency in predicting the fatigue life of the materials studied, under the conditions mentioned above. For accuracy evaluation, the mean square error (MSE) is used as an objective function to be optimized by the three algorithms.

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