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In South America and other developing markets the NVH development of a vehicle is often limited by the cost of the sound package components. In an era where cost reduction is crucial not only in developing markets, but also in developed markets where any cost or weight savings is a large competitive advantage, lessons learned from considerations for NVH analysis for vehicle design in developing markets can be applied to vehicle NVH design everywhere. A Statistical Energy Analysis (SEA) model was used to target and identify the dominant paths in need of sound package modifications to decrease the over sound pressure levels and also to identify paths in which sound package (and cost) could be reduced or deleted with no discernable degradation to the overall interior levels. This model will be used to support or challenge ongoing proposed sound package modifications to the vehicle and serve as a baseline template for design phase work for other vehicles of a similar body style.

Predictions for vehicle interior noise and vibration levels can be made analytically using Statistical Energy Analysis (SEA), particularly for the middle and high frequency ranges. A SEA model can be effectively used together with some minimal baseline measurements to identify and predict changes to the dominant airborne and structureborne paths and to predict the effects that changes to the sound package or structure will have on these paths. Especially for relative changes in noise or vibration level, good accuracy is expected for acoustic or vibration response points such as driver's ear. An SEA model that has been validated with some baseline and various data, which may even come from a previous generation vehicle or component-level testing, can predict if a change to a sound package component will achieve a transmission target or if a proposed change will not be effective.

The concept of using the capabilities of Statistical Energy Analysis (SEA) as the basis for a hybrid technique together with Finite Element Analysis (FEA) or measurements to make acoustic and vibration predictions for the mid-frequency range has been previously established. With advancements in computer memory and speed, FEA calculations such as drive-point mobility and mode counts can now be obtained for some vehicle components or assemblies up to 1000 Hz. This allows for a larger mid-frequency range to be modeled with a hybrid SEA-FEA model when the system is not suitable for modeling with either pure SEA or pure FEA. This can enable a big improvement of the speed and accuracy of a structural-acoustic prediction in the mid-frequency range and can be the best possible analytical prediction in this frequency range when hardware and measured data are not available or testing is to be reduced.

Statistical Energy Analysis (SEA) vehicle models are well-accepted tools for predicting the high-frequency interior acoustic effects of a design change to the structure or sound package of the vehicle. [1] SEA models do not strongly depend on geometric details, which allows SEA to be uniquely used as an analysis tool very early in the vehicle design phase to identify potential Noise, Vibration, and Harshness (NVH) issues caused by proposed changes to acoustic or vibration source levels, component materials, construction details, or sound package details of the vehicle. SEA models can also be used to suggest alternatives while the vehicle is still in the development stages to compensate for a predicted or known degradation to NVH in a vehicle due to a design or source level change. This paper presents a case study in which validation testing and an SEA model were combined to obtain recommendations for the most effective sound package changes to meet NVH targets.

Statistical Energy Analysis (SEA) is an established high-frequency analysis technique for generating acoustic and vibration response predictions in the automotive, aerospace, machinery, and ship industries. SEA offers unique NVH prediction and target-setting capabilities as a design tool at early stages of vehicle design where geometry is still undefined and evolving and no prototype hardware is available yet for testing. The exact frequencies at which SEA can be used effectively vary according to the size and the amount of damping in the vehicle subsystems; however, for automotive design the ability to predict acoustic and vibration responses due to both airborne and structure-borne sources has been established to frequencies of 500 Hz and above. This paper presents the background, historical use, and current industrial applications of structure-borne SEA. The history and motivation for the development of structure-borne SEA are discussed.

Statistical Energy Analysis (SEA) is used to predict high-frequency acoustic and vibration response in vehicle NVH design. Early in the design cycle prototype hardware is not yet available for testing and the geometry is still too poorly defined and changing too quickly for Finite Element Analysis or Boundary Element Analysis to be an effective NVH analysis tool. For most of the concept phase and early design phase, SEA uniquely offers the ability to virtually predict the main noise transfer paths and to support target setting for component and full vehicle NVH design. At later stages of the design process, SEA combines with NVH testing to provide more accurate predictions and to provide guidance for more efficient testing. This paper describes the established uses of SEA in the vehicle industry and presents an overview of the NVH design cycle and how SEA is used to support NVH development at different stages.

Transient Statistical Energy Analysis (SEA) is applied as an analysis technique and compared to measured data in this study. A transient SEA model for a door closure event is developed and compared to measured data to validate this model with measured acoustic and vibration responses. The validated model is then used to predict the effect of changes to component absorption, damping, stiffness, materials, and other properties. The basic theory of transient SEA and the transient SEA model used in the study are described, the validation between analytical model and measured data is shown, and the conclusions from the analysis of design changes to the vehicle components using this model are presented.