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Many solutions exist to insure the NVH comfort of ground and air vehicles, like heavy mass (bitumen pads), viscoelastic treatments and absorbing foams. The trim foam appears as an alternative to heavy solutions. To know the potential of these foams, a study of their capacities to damp vibration is done. A system, composed of a suspended plate, with a foam on it, is characterized in different contact conditions at the foam-plate interface (glued or not) and with different foam type. An experimental test facility is developed to identify the global damping of the structure: a laser vibrometer measures the displacement field of the foam-plate structure, and then an inverse method is used to determine the structural parameters. By changing the contact at the interface, it is possible to identify the contribution of the friction forces to the global damping of the structure. Another type of damping is the viscoelastic damping due to the intrinsic characteristics of the trim foam.

This paper aims at identifying the flexural and shear complex moduli of a sandwich beam by simply measuring the displacement field and applying an inverse resolution of the Timoshenko beam problem. A first development [1] employed the RIFF technique (from the french "Resolution Inverse Filtrée Fenêtrée") [2]. This article presents an improvement, using the RIC method ("Résolution Inverse Corrigée" in french) that involves a correction of the finite difference scheme as originally suggested in [3]. By applying this method specifically to the Timoshenko beam problem [4], one can asses the viscoelastic parameters of composite beams, based on a coarse mesh measurement of the displacement field using a simple accelerometer and an instrumented hammer. An experimental validation conducted on a sandwich honeycomb beam with fibreglass faces allows satisfactory identifications despite a low spatial resolution (down to 2.1 samples per wavelength).

The major difficulty of the structure borne noise source characterization is the strong dependence of the coupling forces with respect to the input mobility of the reception structures and/or the mechanical link between the source and the receiver. The placement of a force sensor can then be too intrusive and the use of inverse techniques on the real reception structure is difficult to apply, because the accessibility can be limited and tests must then be applied for all structures likely to be linked with the studied source. The aim of this paper is to propose a general technique to identify the intrinsic characteristics that are the blocked forces and moments on a simple test bench hosting the source. The test bench consists in mounting the source on a vibrating beam where the mechanical link corresponds exactly to the link between the source and the final receiver.

Identification of vibration sources, defects and/or material properties consists generally in solving inverse problems. The called RIFF method (French acronym meaning Windowed and Filtered Inverse Solving) is one way to solve this kind of inverse problem. The basic principle of the RIFF approach consists in measuring vibration displacement on a meshgrid in a local area of interest, injecting measured data in the motion equation and calculating the searched unknown. Compared to other usual inverse techniques, the RIFF method has the curious particularity of needing the knowledge of the local motion equation only. Boundary conditions, sources or dynamic behaviors outside the area of interest can be completely ignored, whereas they are required for the direct problem solving. The searched unknown can then be identified locally with respect to the frequency and can be mapped by using a scanning process of the area of interest.

In this paper, a local method of structure-borne noise source characterization is presented. It is based on measurements of transverse displacement and local structural operator knowledge and allows to localize and quantify sources without any need of boundary condition information. To fix the instability caused by measurement noise, the regularization step inherent to inverse problem is realized with a probabilistic approach, within the Bayesian framework. When a priori distributions about noise and sources are considered as Gaussian, the Bayesian regularization is equivalent to the well-known Tikhonov regularization. The optimization of the regularization is then performed by the Gibbs Sampling (GS) algorithm, which is part of Markov Chain Monte Carlo (MCMC) techniques. The whole probability of the regularized solution is inferred, providing access to confidence intervals.