Effect of Stochastic Guideway Irregularity on Dynamic Performance of Maglev Train

Maglev trains represent an advanced form of modern rail transportation. The guideway irregularity presents a common disturbance to the safe and reliable operation of the maglev train. Variations in the air gap between the train and the guideway, induced by the guideway irregularities, exert a significant influence on the train’s dynamic performance, thereby impacting both ride comfort and operational safety. Although previous studies have acknowledged the importance of guideway irregularity, the stochastic effects on the car body vibration across different speeds have not been quantitatively assessed. To fill in this gap, this paper presents a 10-degree-of-freedom maglev train model based on multibody dynamics. The guideway is modelled via the finite element method using Euler–Bernoulli beam theory, and a linearized electromagnetic force equation is employed to couple the guideway and the train dynamics. Furthermore, the measurement data of guideway irregularity from the Shanghai Maglev commercial line are incorporated to evaluate their stochastic effect. Analysis results under varying speeds and irregularity wavelengths identify a resonance speed of 127.34 km/h, attributed to the interplay between guideway periodicity and the train’s natural frequency. When the ratio of the train speed versus irregularity wavelength satisfies the train’s natural frequency, a significant resonance can be observed, leading to an increase in train vibration. Based on the Monte Carlo method, stochastic analysis is conducted using 150 simulations per speed in 200–600 km/h. The maximum vertical acceleration remains relatively stable at 200–400 km/h but increases significantly at higher speeds. When the irregularity is present, greater dispersion is observed with increasing speed, with the standard deviation at 600 km/h reaching 2.7 times that at 200 km/h. Across all tested cases, acceleration values are consistently higher than those without irregularities within the corresponding confidence intervals.