Hierarchical model updating for high-speed maglev vehicle/guideway coupled system based on multi-objective optimization

The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration. To improve its accuracy efficiently, this study investigated a hierarchical model updating method integrated with field measurements. First, a high-speed maglev vehicle/guideway coupled model, taking into account the real effect of guideway material properties and elastic restraint of bearings, was developed by integrating the finite element method, multi-body dynamics, and electromagnetic levitation control. Subsequently, simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal parameters. During the hierarchical updating, an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model, thus improving the computational efficiency. The multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration, respectively. Finally, the updated coupled model was compared with the field measurements, and the results demonstrated the model’s accuracy in simulating the actual dynamic response, validating the effectiveness of the updating method.