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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.

In this paper, a model condensation technique is developed to ensure consistent modeling of STL (Sound Transmission Loss) between coarse and detailed SEA model. In the Performance-Based coarse SEA Model, the component level performance (STL and absorption) is assigned to each path, which comes from various ways including detailed analytical SEA model. From the detailed SEA model for the component or even the whole vehicle, the equivalent performance data needs to be condensed and extracted for the coarse model. The condensation theory for equivalent STL is presented in this paper. The extra work needed to apply this technique to detailed SEA model is negligible by using AutoSEA script. An example for condensation of a detailed component model is given at the end. Comparison between the detailed analytical SEA model and the coarse SEA Model is consistent.

Damping treatments are widely used in passenger vehicles, but the knowledge of damping treatments is often fragmentary in the industry. In this study, vibro-acoustics behavior of a set of vehicle floor and dash panels with various types of damping treatments was investigated. Sound transmission loss, sound radiation efficiency as well as damping loss factor were measured. The damping treatments ranged from laminated steel construction (thin viscoelastic layer) and doubler plate construction (thick viscoelastic layer) to less structural “bake-on” damping and self-adhesive aluminum foil-backed damping treatments. In addition, the bare vehicle panels were tested as a baseline and the fully carpeted floor panel was tested as a reference. The test data were then examined together with analytical modeling of some of the test configurations. As expected, the study found that damping treatments add more than damping. They also add mass and change body panel stiffness.