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A Hybrid method that rigorously couples Statistical Energy Analysis (SEA) and Finite Element Analysis (FEA) has been used to predict interior noise levels in a trimmed vehicle due to broadband structure-borne excitation from 200Hz to 1000Hz. This paper illustrates how the Hybrid FE-SEA technique was applied to successfully predict the car response by partitioning the full vehicle into stiff components described with FE and modally dense components described with SEA. Additionally, it is demonstrated how detailed local FE models can be used to improve SEA descriptions of car panels and couplings. The vibration response of the untrimmed body-in-white is validated against experiments. Next, the radiation efficiency and vibration response of bare and trimmed vehicle panels are compared against reference numerical results. Finally, interior noise levels in bare and trimmed configurations are predicted and results from a noise path contribution analysis are presented.

As customer awareness of product sound grows, the need exists to ensure that product sound quality is maintained in the manufacturing process. To this end in-process controls that employ a variety of traditional acoustical and alternate sound quality metrics are utilized, usually partly or wholly housed in a test enclosure. Often times these test cells are required to attenuate the background noise in the manufacturing facility so that the device under test can be accurately assessed. While design guidelines exist the mere size and cost of such booths make an iterative build and test approach costly in terms of materials as well as engineering and testing time. In order to expedite the design process and minimize the number of confirmation prototypes, SEA can be utilized to predict the transmission loss based upon material selection and booth construction techniques.

Statistical Energy Analysis (SEA) was used during the design of a new heavy duty truck. This paper provides an overview of the building and validation process of an airborne SEA model of a typical commercial vehicle. Predictions of interior noise levels are compared against tests. A noise path contribution analysis is presented, demonstrating how the impact of potential design changes on the interior sound levels can be evaluated with an SEA model.

Attenuation of noise from the rear of a vehicle was evaluated for different trunk insulation systems using a combination of poro-elastic material modeling and a full vehicle SEA model. The model considered the interaction between the trunk and the passenger cabin. The sound absorption coefficients and acoustic impedance for each of the material systems used in the trunk were measured and the poro-elastic Biot properties were calculated to define the acoustic treatments in the SEA model. Several levels of acoustical treatment for the trunk were studied ranging from a trunk with no decorative liner to a trunk with a liner and maximum acoustical treatment. The results show the contribution of the trunk material in reducing cabin noise for different levels of noise originating at the rear of the vehicle. These results demonstrate the value of combining poro-elastic material modeling and SEA models for selecting efficient material systems early in a vehicle design.

A process to create a vehicle resistance curve based on airflow predictions using Computational Fluid Dynamics (CFD) simulation technique is presented. 1-dimensional engine cooling system simulation tool KULI is used to compute the coefficients of vehicle resistance curve. A full factorial Design of Experiment (DOE) established the relationship between the coefficients and the sum of absolute difference between KULI and CFD predictions. The NLPQL optimization routine is used to accurately predict the coefficients so that sum of absolute difference between KULI and CFD predictions is minimized.

There are many software tools in use today that are implementing the Biot, or complementary, method for the evaluation of foam and fiber materials. The justification of this process is to understand which mechanisms of the noise control material are contributing to the noise reduction and to optimize the material based on its acoustic properties. The disadvantage of this method is that it is quite complex and time consuming to test a material in order to extract the underlying properties that govern the acoustic performance. An alternative inverse method for material characterization based on simple impedance tube measurements has been developed lately. This paper recalls the physics and mathematics behind the method and concentrates on its validation.

The excitation of structural modes of vehicle roofs due to structure-borne excitations from the road and powertrain can generate boom and noise issues inside the passenger cabin. The use of elastomeric foams between the roof bows and roof panel can provide significant damping to the roof and reduce the vibration. If computer-aided engineering (CAE) can be used to predict the effect of elastomeric foams accurately on vibration and noise, then it would be possible to optimize the properties and placement of foam materials on the roof to attenuate vibration. The properties of the different foam materials were characterized in laboratory tests and then applied to a flat test panel and a vehicle body-in-white. This paper presents the results of an investigation into the testing and CAE analysis of the vibration and radiated sound power of flat steel panels and the roof from the BIW of an SUV with anti-flutter foam and Terophon® high damping foam (HDF) materials.

Porous materials have been applied increasingly for absorbing noise energy and improving the acoustic performance. Different models have been proposed to predict the performance of these materials, and much progress has been achieved. However, most of the foregoing researches have been conducted on a single layer of porous material. In real application, porous materials are usually combined with other kinds of materials to compose a multilayered noise control treatment. This paper investigates the acoustic performance of such treatments with a combination of porous and non-porous media. Results from numerical simulation are compared to experimental measurements. Transfer matrix method is adopted to simulate the insertion loss and absorption associated with three samples of a noise control treatment product, which has two porous layers bonded by an impervious screen.

Sound propagation through noise control treatment is governed by fluid, mechanical and geometric properties of the materials. The knowledge of material properties is important to improve the acoustical performance of the resulting noise control products. A method based on optimization together with genetic algorithm is used to estimate material properties of multi-layered treatments. Unlike previous inverse characterization approaches based on normal incidence performance metrics measured using standing wave impedance tubes, the current approach is based on random incidence performance metrics. Specially, the insertion loss ‘measured’ from two room transmission loss suite is utilized. To validate the applicability of the proposed method, numerically synthesized insertion loss computed from known material properties are used. In order to properly represent the ‘measured’ values, noise is added to the numerically synthesized insertion loss values.

Optimizing noise control treatments in the early design phase is crucial to meet new strict regulations for exterior vehicle noise. Contribution analysis of the different sources to the exterior acoustic performance plays an important role in prioritizing design changes. A method to predict Pass-by noise performance of a car, based on source-path-receiver approach, combining data coming from simulation and experimental campaigns, is presented along with its validation. With this method the effect of trim and sound package on exterior noise can be predicted and optimized.

Designing an effective AVAS system, not only to meet safety regulations, but also to create the expected perception for the vulnerable road user, relies on knowledge of the acoustic transfer function between the sound actuator and the receiver. It is preferable that the acoustic transfer function be as constant as possible to allow transferring the sound designed by the car OEM to ensure the safety of vulnerable road users while conveying the proper brand image. In this paper three different methodologies for the acoustic transfer function calculations are presented and compared in terms of accuracy and calculation time: classic Boundary Element method, H-Matrix BEM accelerated method and Ray tracing method. An example of binaural listening experience at different certification positions in the modeled simulated space is also presented.

Noise reduction is generally accomplished by applying appropriate noise control treatments at strategic locations. Noise control treatments consisting of poroelastic materials in layers are extensively used in noise control products. Sound propagation through poroelastic materials is governed by macroscopic material and geometric properties. Thus, a knowledge of material properties is important to improve the acoustical performance of the resulting noise control products. Since the direct measurement of these properties is cumbersome, these have been usually estimated indirectly from easily measurable acoustic performance metrics such as normal incidence sound transmission and/or absorption coefficient, measured using readily available impedance tube. The existing inverse characterization approaches fulfilled the estimation by curve fitting measured and predicted acoustic models.