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Article

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

1
Institut Technologique de Maintenance Industrielle, 175, rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
2
Wind Energy Research Laboratory (WERL), Université du Québec à Rimouski, 300, allée des Ursulines, Rimouski, QC G5L 3A1, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Dragan Pamucar
Eng 2021, 2(3), 278-295; https://doi.org/10.3390/eng2030018
Received: 19 May 2021 / Revised: 30 June 2021 / Accepted: 1 July 2021 / Published: 5 July 2021
(This article belongs to the Special Issue Stylistic Design Engineering (SDE))
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. View Full-Text
Keywords: wind turbine blades; fatigue life; artificial neural network; optimization algorithms; composite materials; hygrothermal effect wind turbine blades; fatigue life; artificial neural network; optimization algorithms; composite materials; hygrothermal effect
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MDPI and ACS Style

Ziane, K.; Ilinca, A.; Karganroudi, S.S.; Dimitrova, M. Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions. Eng 2021, 2, 278-295. https://doi.org/10.3390/eng2030018

AMA Style

Ziane K, Ilinca A, Karganroudi SS, Dimitrova M. Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions. Eng. 2021; 2(3):278-295. https://doi.org/10.3390/eng2030018

Chicago/Turabian Style

Ziane, Khaled, Adrian Ilinca, Sasan Sattarpanah Karganroudi, and Mariya Dimitrova. 2021. "Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions" Eng 2, no. 3: 278-295. https://doi.org/10.3390/eng2030018

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