A Meta-heuristic Optimization Procedure for the Identification of the Nonlinear Model Parameters of Hydraulic Dampers Based On Experimental Dataset of Real Working Conditions

Hydraulic dampers are widely implemented in railway vehicle suspension stages, especially in high-speed passenger trains. They are designed to be mounted in different positions to improve comfort, stability, and safety performances. Numerical simulations are often used to assist the design and optimization of these components. Unfortunately, hydraulic dampers are highly nonlinear due to the complex fluid dynamic phenomena taking place inside the chambers and through the by-pass orifices. This requires accurate damper models to be developed to estimate the influence of the nonlinearities of such components during the dynamic performances of the whole vehicle. This work aims at presenting a new parametric damper model based on a nonlinear lumped element approach. Moreover, a new model tuning procedure will be introduced. Differently from the typical sinusoidal characterization cycles, this routine is based on experimental tests of real working conditions. The set of optimal model parameters will be found through a meta-heuristic iterative approach able to minimize the differences between numerical and experimental damper forces. The performances of the optimal model will be compared with the ones of the most common Maxwell model generally implemented in railway multibody software programs.