Resumen
The aim of this paper is to propose a methodology for the modelling, testing, and the parameter identification of viscoelastic supports for rotating machines at the component and system level. Although the use of this type of dampers is common in rotordynamic applications, the strict dependence on the working frequency of the material parameters makes their behavior hard to predict and recommends grounding the design on experimental data of the characteristics. A dedicated test rig is adopted to characterize the supports and validate the modelling approach at component level. A parameter identification procedure is carried out from the experimental results to extract the mechanical properties of the supports. To this end, a Genetic Algorithm (GA) is adopted to search the most fitting values of damping and stiffness of the reference model. The choice of GA is motivated by the need of adopting a technique that can be easily implemented on industrial control units, being that the dedicated machine is supposed to be used in production lines for testing procedures. The results are obtained in terms of complex stiffness as a function of the frequency and are then translated in classical stiffness and damping components that are commonly used in rotordynamics. Afterwards, they are introduced in the model of a turbo-molecular pump adopted as a case study to evaluate the correctness of the proposed methodology at system level at stand-still and with the pump in rotation. The proposed approach is effective, showing a good match between the numerical model and the experimental results even with a relatively low order model of the viscoelastic supports and on a complex rotordynamic system.