To study the influence of the pantograph fixing position on aerodynamic characteristics of high-speed trains, the aerodynamic models of high-speed trains with eight cars were established based on the theory of com- pu...To study the influence of the pantograph fixing position on aerodynamic characteristics of high-speed trains, the aerodynamic models of high-speed trains with eight cars were established based on the theory of com- putational fluid dynamics, and eight cases with pantographs fixed on different positions and in different operational orientations were considered. The pantographs were fixed on the front or the rear end of the first middle car or fixed on the front or the rear end of the last middle car. The external flow fields of the high-speed trains were numeri- cally simulated using the software STAR-CCM＋. The results show that the pantograph fixing position has little effect on the aerodynamic drag force of the head car and has a large effect on the aerodynamic drag force of the tail car. The influences of the pantograph fixing position on the aerodynamic lift forces of the head car, tail car and pan- tographs are obvious. Among the eight cases, considering the total aerodynamic drag force of the train and the aerodynamic lift force of the lifted pantograph, when the pantographs are fixed on the rear end of the last middle car and the lifted pantograph is in the knuckle-upstream ori- entation, the aerodynamic performance of the high-speed train is the best.展开更多
构建了一种基于IF模型的侧抑制神经网络群,用以实现位置定位.采用基于H-H模型简化的IF模型构造神经网络群并基于概率密度分布进行位置定位.在神经网络群学习过程中,运用PITS(progressive interactive training scheme)方法进行参数学习...构建了一种基于IF模型的侧抑制神经网络群,用以实现位置定位.采用基于H-H模型简化的IF模型构造神经网络群并基于概率密度分布进行位置定位.在神经网络群学习过程中,运用PITS(progressive interactive training scheme)方法进行参数学习,利用信息中心(IC)储存每次训练的结果,在保证输出收敛的情况下,比较跟踪结果的误差函数给出权值调整公式进行自学习.实验结果表明:基于IF模型构建的神经网络群可以实现位置定位.采用H-H模型简化的IF模型提高了学习效率和定位速度;运用PITS算法进行参数学习提高了定位精度.展开更多
基金supported by the High-Speed Railway Basic Research Fund Key Project of China(Grant No.U1234208)the National Natural Science Foundation of China(Grant Nos.51475394 and 51605397)
文摘To study the influence of the pantograph fixing position on aerodynamic characteristics of high-speed trains, the aerodynamic models of high-speed trains with eight cars were established based on the theory of com- putational fluid dynamics, and eight cases with pantographs fixed on different positions and in different operational orientations were considered. The pantographs were fixed on the front or the rear end of the first middle car or fixed on the front or the rear end of the last middle car. The external flow fields of the high-speed trains were numeri- cally simulated using the software STAR-CCM＋. The results show that the pantograph fixing position has little effect on the aerodynamic drag force of the head car and has a large effect on the aerodynamic drag force of the tail car. The influences of the pantograph fixing position on the aerodynamic lift forces of the head car, tail car and pan- tographs are obvious. Among the eight cases, considering the total aerodynamic drag force of the train and the aerodynamic lift force of the lifted pantograph, when the pantographs are fixed on the rear end of the last middle car and the lifted pantograph is in the knuckle-upstream ori- entation, the aerodynamic performance of the high-speed train is the best.
文摘构建了一种基于IF模型的侧抑制神经网络群,用以实现位置定位.采用基于H-H模型简化的IF模型构造神经网络群并基于概率密度分布进行位置定位.在神经网络群学习过程中,运用PITS(progressive interactive training scheme)方法进行参数学习,利用信息中心(IC)储存每次训练的结果,在保证输出收敛的情况下,比较跟踪结果的误差函数给出权值调整公式进行自学习.实验结果表明:基于IF模型构建的神经网络群可以实现位置定位.采用H-H模型简化的IF模型提高了学习效率和定位速度;运用PITS算法进行参数学习提高了定位精度.
