Published 4 issues per year
ISSN Print: 2152-2057
ISSN Online: 2152-2073
Indexed in
PARTICLE SWARM OPTIMIZATION-BASED NEURAL NETWORK FOR PREDICTING FATIGUE STRENGTH IN COMPOSITE LAMINATES OF WIND TURBINE BLADES
ABSTRACT
In this paper, the fatigue strength in multidirectional (MD)/unidirectional (UD) composite laminates of wind turbine blades is predicted by using particle swarm optimization-based artificial neural networks (PSO-ANN). In the PSO-ANN approach used in this work, the objective 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. Different materials based on different reinforcing fabrics and resins are compared in terms of the maximum tensile fatigue stress. Tension–tension constant amplitude fatigue loads were applied to thermoset materials including glass-fiber/epoxy, polyester and vinyl esters. All materials were treated in closed molds with resin infusion process, which were molded into their final dogbone shape without machining. The results show that the PSO-ANN can provide accurate fatigue strength prediction for different MD/UD composite laminates under different values of fiber orientation.
-
Ziane Khaled, Ilinca Adrian, Karganroudi Sasan Sattarpanah, Dimitrova Mariya, Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions, Eng, 2, 3, 2021. Crossref