Fatigue strength prediction in composite materials of wind turbine blades under dry–wet conditions: An artificial neural network approach

Abstract

In this article, the fatigue strength in composite materials of wind turbine blades under dry–wet conditions is predicted using artificial neural networks. Compression–compression constant amplitude fatigue tests were performed on thermoset polymer resins including polyesters and vinyl esters. Coupons were tested under an air temperature of 20°C and 50°C in both “dry” and “wet” conditions. The results show that artificial neural network can provide accurate fatigue strength prediction for different resin matrices under different values of temperature.