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This paper presents a pioneer work and first time application of Multi Objective Robust Optimization to analytically improve Idle Shake Performance. The method is developed to obtain a robust design with multiple objectives under consideration along with managing material property variation. It was a Robust Optimization on top of Multi Objective Genetic Algorithm, Robu-MOGA. The design variables in the study included the nominal values and tolerances of Sound Transmission Loss property, and interior material Absorption property. The analytical objective was not only to minimize the peak airborne noise at each specified frequency, but also to reduce the total cost and the total mass of the materials. In the study, AutoSEA (statistical energy analysis) from ESI Software, Inc. was used as the solver. AutoSEA was integrated with iSIGHT from Engineous Software, Inc.

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.

With Statistical Energy Analysis (SEA) proven to be a powerful tool for airborne noise analysis, the capability of predicting the exterior sound field around a vehicle at high frequencies (the load case in the SEA analysis) is of particular interest to OEMs and suppliers. This paper employs the High Frequency Boundary Element Method (HFBEM) to simulate the scattered exterior sound field distribution due to a monopole source. It is shown that the proposed method is able to efficiently predict the spatial and frequency averaged sound pressure levels reasonably well up to 10 kHz, even at points in the near field of the vehicle body.