terence connelly

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. Additionally, SEA can be
used to identify those components of the booth that are
likely to be problematic from either a transmission loss
or a leakage perspective.

SEA models are used to predict the performance of acoustic packages when assessing the performance of vehicle level
or body noise reduction targets. One of the challenges faced by CAE engineers is the ability to estimate the performance of
different materials used in the sound package at the design stage. Analysts can use measured data in the form of insertion
loss and random incidence absorption if available or can predict the performance of materials using a Biot type description.
The use of the full poro-elastic Biot model for materials requires knowledge of the fluid and elastic properties, however a
limp or rigid model can be used to describe the material based only on the fluid properties and this is often sufficient to
describe fibrous materials.
In this paper a method will be outlined which will allow the material properties of fibrous materials to be estimated
from basic normal incidence data that is provided by material suppliers. This approach will then allow the effect of
compression on the material properties to be predicted and the impact on the acoustic performance to be estimated. In
addition it will allow the analyst to estimate the effects of increasing or reducing the fiber weight. Predictions will be
compared with measured data.

SEA models are used to predict the performance of acoustic packages when assessing the performance of vehicle level or body noise reduction targets. One of the challenges faced by CAE engineers is the ability to estimate the performance of different materials used in the sound package at the design stage. Analysts can use measured data in the form of insertion loss and random incidence absorption if available or can predict the performance of materials using a Biot type description. The use of the full poro-elastic Biot model for materials requires knowledge of the fluid and elastic properties, however a limp or rigid model can be used to describe the material based only on the fluid properties and this is often sufficient to describe fibrous materials. In this paper a method will be outlined which will allow the material properties of fibrous materials to be estimated from basic normal incidence data that is provided by material suppliers. This approach will then allow the effect of compression on the material properties to be predicted and the impact on the acoustic performance to be estimated. In addition it will allow the analyst to estimate the effects of increasing or reducing the fiber weight. Predictions will be compared with measured data.

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. Additionally, SEA can be used to identify those components of the booth that are likely to be problematic from either a transmission loss or a leakage perspective.

Abstract Automotive interior noise at mid and high frequencies is typically dominated by the airborne noise from acoustic sources that are spatially distributed around a vehicle. Each source is typically spatially compact (for example, a tire contact patch) but the source ...