Robust design of turbine-blade low cycle fatigue life based on neural networks and fruit fly optimization algorithm

By combining generalized regression neural network (GRNN) with fruit fly optimization algorithm (FFOA) and using FFOA multi-point global search ability to optimize the random variable which affects the fatigue life, a robust optimization design for low cycle fatigue life of turbine-blade can be made on the base of probability analysis for turbine-blade low cycle fatigue life. Optimization results show that the probability interval of fatigue life decreases 17.9%, and the sensitivity of the low cycle fatigue life of the random variable can be reduced, so the fatigue life can be estimated more accurately. Optimization results indicate that the proposed method is available and feasible for the engineering application.