In this study, experiments were carried out to investigate aerodynamic characteristics of a high-speed train on viaducts in turbulent crosswinds using a 1:25 scaled sectional model wind-tunnel testing. Pressure measur...In this study, experiments were carried out to investigate aerodynamic characteristics of a high-speed train on viaducts in turbulent crosswinds using a 1:25 scaled sectional model wind-tunnel testing. Pressure measurements of two typical sections, one train-head section and one train-body section, at the windward and leeward tracks were conducted under the smooth and turbulence flows with wind attack angles between-6° and 6°, and the corresponding aerodynamic force coefficients were also calculated using the integral method. The experimental results indicate that the track position affects the mean aerodynamic characteristics of the vehicle, especially for the train-body section. The fluctuating pressure coefficients at the leeward track are more significantly affected by the bridge interference compared to those at the windward track. The effect of turbulence on the train-head section is less than that on the train-body section. Additionally, the mean aerodynamic force coefficients are almost negatively correlated to wind attack angles, which is more prominent for vehicles at the leeward track. Moreover, the lateral force plays a critical role in determining the corresponding overturning moment, especially on the train-body section.展开更多
At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to an...At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to analyze the data.Therefore,we introduce kernel principal component analysis and stacked auto-encoder network(KPCA-SAD)into the fault diagnosis of ZPW-2000 track circuit.According to the working principle and fault characteristics of track circuit,a fault diagnosis model of KPCA-SAE network is established.The relevant parameters of key components recorded in the data collected by field staff are used as the fault feature parameters.The KPCA method is used to reduce the dimension and noise of fault document matrix to avoid information redundancy.The SAE network is trained by the processed fault data.The model parameters are optimized overall by using back propagation(BP)algorithm.The KPCA-SAE model is simulated in Matlab platform and is finally proved to be effective and feasible.Compared with the traditional method of artificially analyzing fault data and other intelligent algorithms,the KPCA-SAE based classifier has higher fault identification accuracy.展开更多
基金Projects(51808563,51925808)supported by the National Natural Science Foundation of ChinaProject(KLWRTBMC18-03)supported by the Open Research Fund of the Key Laboratory of Wind Resistance Technology of Bridges of ChinaProject(2017YFB1201204)supported by the National Key R&D Program of China。
文摘In this study, experiments were carried out to investigate aerodynamic characteristics of a high-speed train on viaducts in turbulent crosswinds using a 1:25 scaled sectional model wind-tunnel testing. Pressure measurements of two typical sections, one train-head section and one train-body section, at the windward and leeward tracks were conducted under the smooth and turbulence flows with wind attack angles between-6° and 6°, and the corresponding aerodynamic force coefficients were also calculated using the integral method. The experimental results indicate that the track position affects the mean aerodynamic characteristics of the vehicle, especially for the train-body section. The fluctuating pressure coefficients at the leeward track are more significantly affected by the bridge interference compared to those at the windward track. The effect of turbulence on the train-head section is less than that on the train-body section. Additionally, the mean aerodynamic force coefficients are almost negatively correlated to wind attack angles, which is more prominent for vehicles at the leeward track. Moreover, the lateral force plays a critical role in determining the corresponding overturning moment, especially on the train-body section.
基金National Natural Science Foundation of China(No.61763023)。
文摘At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to analyze the data.Therefore,we introduce kernel principal component analysis and stacked auto-encoder network(KPCA-SAD)into the fault diagnosis of ZPW-2000 track circuit.According to the working principle and fault characteristics of track circuit,a fault diagnosis model of KPCA-SAE network is established.The relevant parameters of key components recorded in the data collected by field staff are used as the fault feature parameters.The KPCA method is used to reduce the dimension and noise of fault document matrix to avoid information redundancy.The SAE network is trained by the processed fault data.The model parameters are optimized overall by using back propagation(BP)algorithm.The KPCA-SAE model is simulated in Matlab platform and is finally proved to be effective and feasible.Compared with the traditional method of artificially analyzing fault data and other intelligent algorithms,the KPCA-SAE based classifier has higher fault identification accuracy.
