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Statistical Energy Analysis (SEA) models are routinely being adopted in up-front automotive sound package design. SEA models serve two important functions. First they provide a means of assessing noise and vibration performance relative to absolute targets. Secondly, they are used to assess various alternative designs or changes required to meet targets. This paper addresses how to objectively evaluate both the absolute and relative predictive capability of SEA models. The absolute prediction is assessed using a hypothesis test to determine membership of the analytical prediction relative to a set of test data. The relative prediction is assessed using hardware-designed experiments to estimate design sensitivities. Both have been found useful to drive model improvement efforts. Being able to objectively document model capability also improves the credibility of SEA model predictions and the design information they deliver.

Modern product design cycles are requiring faster turnaround of design changes and the accompanying noise, vibration and harshness analyses. Design sensitivity analysis (DSA) guides the designer in making changes by indicating the model variables that cause the greatest benefit in noise or weight reduction. The use of analytical models based on statistical energy analysis (SEA) is attractive for predicting noise and vibration environments because of their simplicity and solution speed compared with deterministic models. This paper describes the implementation of DSA in an SEA computer code. Examples of SEA/DSA for controlling airborne and structureborne noise problems are described.

A computational design study, performed in conjunction with experiments, to reduce the howl noise caused by the roof rack crossbars of a production automobile is presented. This goals were to obtain insight into the flow phenomenon causing the noise, and to do a design iteration study that would lead to a small number of cross-section recommendations for crossbars that would be tested in the wind tunnel. The flow condition for this study is 0 yaw at 30 mph inlet speed, which experimentally gives the strongest roof rack howl for the vehicle considered for this study. The numerical results have been obtained using the commercial CFD/CAA software PowerFLOW. The simulation kernel of this software is based on the numerical scheme known as the Lattice Boltzmann Method (LBM), combined with a two-equation RNG turbulence model.

Unusual noises during vehicle acceleration often reflect poorly on customer perception of product quality and must be removed in the product development process. Flow simulation can be a valuable tool in identifying root causes of exhaust noises created due to tailpipe openings surrounded by fascia structure. This paper describes a case study where an unsteady Computational Fluid Dynamics (CFD) simulation of the combined flow and acoustic radiation from an exhaust opening through fascia components provided valuable insight into the cause of an annoying flow noise. Simulation results from a coupled thermal/acoustic analysis of detailed tailpipe opening geometry were first validated with off-axis microphone spectra under wide open throttle acceleration. After studying the visualizations of unsteady flow velocity and pressure from the CFD, a problem that had proved difficult to solve by traditional “cut and try” methods was corrected rapidly.

Statistical energy analysis (SEA) has recently emerged as an effective tool for design assessment in the automotive industry. Automotive OEM companies develop vehicle models to aid design of body and chassis systems. The tire and wheel suppliers develop and supply component models to OEM companies in the engineering stage. In the model development process, some information on the vehicle side or component side is necessary for model development and correlation. A suitable termination representation of the vehicle characteristics on the tire/wheel model is required. This termination should account for the dissipation of energy on vehicle body and chassis side, otherwise the component model will overestimate the vibration responses and energy levels. On the vehicle model side, a representative simplified tire/wheel model may be sufficient for full vehicle road noise simulation.

Airborne sound transmission through vehicle panels with penetrations and sound insulation is a major component of high frequency interior noise in cars and trucks. Accurate analytical models of interior noise require high fidelity simulation of these paths in order to perform upfront design of the sound package. This paper describes a modeling approach based on Statistical Energy Analysis (SEA) that provides a general and flexible capability for incorporating sound package parameters within an analytical model of high frequency interior noise. Validation of the model for sound transmission through panels with holes and with typical sound insulation material is achieved through innovative testing methods that reveal dynamics of the decoupler and barrier layers. Refinements of the general approach that consider more deterministic features of the specific decoupler material are also suggested.

This paper reports on a case study involving the use of SEA methods in the acoustic design of an advanced design luxury sedan. The power of the analytical method was used to advantage in a case of a vehicle with very challenging NVH targets. Three practical issues are highlighted; review of a method to handle adding components that contribute acoustic absorption, presentation of data to aid vehicle content decisions, and design sensitivity analysis. This effort demonstrates an example in which SEA modeling provided relevant and timely input to the vehicle design team to aid decision making for sound package content.