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.