Abstract
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.
Highlights
Blades are one of the most critical components of wind turbines
The materials ineffectiveness question are in predicting the fatigue life of wind turbine blade materials
For illustration and comparative purposes, we have presented in the figure and for each studied temperature, four plots for different experimental and presame figure and for each studied temperature, four plots for different experimental and dicted values obtained with BPNN, Particle Swarm Optimization (PSO)-artificial neural network (ANN), and CS-Based NN (CSNN), where they show typical fapredicted values obtained with BPNN, PSO-ANN, and CSNN, where they show typical tigue life predictions
Summary
Blades are one of the most critical components of wind turbines. They capture wind energy and convert it into mechanical energy for the production of electricity. Defective blades significantly affect the energy conversion efficiency of the wind turbines, and blade failures have a significant impact on the cost of energy (repair, maintenance, etc.). The increased reliability and lifetime of wind turbine blades are important for the cost of energy reduction. 95% of the modern wind turbine blades are made of fiber-reinforced composites because of their good mechanical characteristics: high stiffness, low density, and long fatigue life [1]. Fiber-reinforced composites have other advantages in terms of weight, cost, quality, technical feasibility, market expectation, environmental impact, and health and safety. Several key properties are dictated by the matrix resin, including fatigue strength, which is a dominant failure mode in composite material structures, leading to the breakdown of structural integrity in areas such as the trailing edge, spars, and root connections [2,3]
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