基于物理信息神经网络的风电机组塔筒概率疲劳寿命预测方法OA

To address the limitations that the traditional fatigue design for wind turbine towers using deterministic S-N curves might not accurately quantify fatigue life dispersion,a probabilistic fatigue life pre-diction method was proposed based on physics-informed neural networks.By embedding the physical prior knowledge such as fatigue life dispersion,monotonicity and nonlinearity into the neural networks,a proba-bilistic prediction model capable of accurately quantifying uncertainty was constructed.Compared with tra-ditional methods,the proposed method reduces the normalized root mean square error(NRMSE)by up to 31.58%.A 16 MW wind turbine simulation model was established in accordance with IEC standards,and tower load data were obtained by using Bladed software.Combined with wind-speed distribution,rain flow counting and the Miner rule,the probabilistic fatigue life prediction of the towers was achieved.The results show that the proposed method effectively characterizes the probabilistic features of fatigue damages,and the tower lifetime varies significantly with reliability requirements(shortening from 83.3 years at 50%probability to 18.2 years at 99.9%probability),which provides a reliable basis for probabilistic fatigue de-sign and safety assessment of wind turbine towers.