运用有限元和神经网络为SHPB装置构造理想冲头

针对大直径岩样专用SHPB试验装置存在的三维效应问题,从三维实体研究入手,利用非线性动力分析有限元程序对不同形状系列冲头进行了实体模拟;并运用径向基函数神经网络对模拟所得数据系列进行网络仿真,然后利用该网络对理想冲头形状进行自适应识别,构造了半正弦波加载SHPB装置的理想冲头,进一步完善了大直径SHPB试验技术;最后与基于一维应力波理论反演设计的冲头进行了比较。结果表明,基于三维实体模拟和径向基函数神经网络构造的冲头产生的应力波形更为理想。

CONSTRUCTION OF IDEAL STRIKER FOR SHPB DEVICE BASED ON FEM AND NEURAL NETWORK

In light of the three-dimensional effect of split Hopkinson pressure bar(SHPB) device for large diameter rock specimen,solid simulation is conducted with three-dimensional nonlinear dynamic finite element code;and the resultant data serials are then simulated with radial basis function(RBF) neural network and the ideal striker is self-adaptively recognized with the network.Finally,the constructed striker producing perfectly half-sine wave is compared with that back designed with one-dimensional stress wave theory;and it is demonstrated that the newly constructed striker by using three-dimensional nonlinear FEM and neural network has better half-sine waveform.