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In traditional FE based structure-borne noise analysis, interior trims are normally modeled as lump masses in the FE structure model and acoustic specific impedance of the trim is assigned to the FE acoustics model when necessary. This simplification has proven to be effective and sufficient for low frequency analysis. However, as the frequency goes into the mid-frequency range, the elastic behavior of the trim may impose some effects on the structural and acoustic responses. The approach described in this paper is based on the structural FE and acoustic SEA coupling analysis developed by ESI, aiming to improve the modeling efficiency for a possible quick turnaround in virtual assessments.

The Door system is one of the major paths for vehicle interior noise under a variety of load conditions. In this paper we consider the elements of the door lower (excluding glass) in terms of noise transmission. Passenger car doors are comprised of the outer skin, door cavity, door inner sheet metal, vapor barrier, and interior trim. Statistical Energy Analysis (SEA) models must effectively describe these components in terms of their acoustic properties and capture the dominant behaviors relative to the overall door system. In addition, the models must interface seamlessly with existing vehicle level SEA models. SEA modeling techniques for the door components are discussed with door STL testing and model correlation results.

The application of Statistical Energy Analysis (SEA) to vehicle development is discussed, with a new technique to implement noise path analysis within a SEA model to enable efficient solution and optimization of acoustic trim. A whole vehicle Performance-Based SEA model is used, in which Sound Transmission Loss (STL) and acoustic absorption coefficient characterize subsystem performance. In such a model, the net contribution from each body panel/path, such as the floor, to a specific interior subsystem, such as the driver's head space, is extremely important for vehicle interior noise development. First, it helps to identify the critical path to root-cause potential problems. Second, it is necessary in order to perform balancing of path contributions. With current software, the power based noise contribution analysis is for direct paths/adjacent subsystems.