Trim-structure interface modelling and simulation approaches for FEM applications 2024-01-2954

Trim materials are often used for vibroacoustic energy absorption purposes within vehicles. To estimate the sound impact at a driver’s ear, the substructuring approach can be applied. Thus, transfer functions are calculated starting from the acoustic source to the car body, from the car body to the trim and, finally, from the trim to the inner cavity where the driver is located. One of the most challenging parts is the calculation of the transfer functions from the car body inner surface to the bottom trim surface. Commonly, freely laying mass-spring systems (trims) are simulated with a fixed boundary and interface phenomena such as friction, stick-slip or discontinuities are not taken into consideration. Such an approach allows for faster simulations but results in simulations strongly overestimating the energy transfer, particularly in the frequency range where the mass-spring system’s resonances take place. In the current work, several methods to model and simulate the above-mentioned interface phenomena have been studied. To provide reference results for simulations, a series of shaker measurements have been conducted on various trim samples with different boundary conditions. Further on, frequency response functions have been calculated and used as target functions for simulations with different interface modelling strategies. As the first simulation method to account for the interface influence, an isotropic interface layer approach is discussed. As the agreement between simulation and reference results has been shown to be insufficient, an orthotropic layer has been proposed instead. Moreover, Morris sensitivity analysis has been performed to determine most influential parameters of the intermediate layer. The dependency of the influential parameter values on the trim system configuration has been investigated with the help of genetic algorithms. Finally, artificial intelligence tools have been applied to train a linear regression neural network to predict intermediate layer parameter values for mass-spring systems of different configurations.