Aerodynamic Resistance of Train Running in Vacuum Tube: A Simulation Study

The flow field,around the high-speed train in vacuum tube,was mathematically formulated with 3 D standard k-ε turbulence model and numerically simulated with Fluent software.To begin with,the influence of the possible situations,including the 100-cases specified in a combination of variables of blocking-ratio,train-speed and pressure,on the aerodynamic resistance was investigated; second,96-sets of simulated results were analyzed as the training samples of the three-layer RBF and BP neural network models,for determination and optimization of the hidden-layer-neurons number and training functions; and finally,the two neural network models were verified with the 4 randomly selected samples,respectively.The analysis results show that when it comes to prediction of the largest resistance,RBF neural network model,with the largest prediction error of ≤ 5%,outperforms that of BP because of better accuracy.