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With stricter pass-by norms, reducing engine noise radiation is becoming more important. Adding ribs to improve stiffness is one efficient approach to achieve this goal. This paper performs the optimization of ribs which are added on the surface of an inline six-cylinder engine block. The ribs are placed orthogonally. For the optimization, optimization variables are set up to update the dimensions of the ribs in each iteration. The limits of the size changes are defined by the optimization constraints. The overall sound power radiated from the engine block surface between 500Hz and 1450Hz is chosen as the optimization objective. In each iteration, the radiated sound power is obtained by numerical analysis of a fully coupled structural-acoustic model, while the FEM (finite element method) is adopted for calculating the structural response and BEM (boundary element method) is used to compute the noise radiation from the engine block surface.

Welding processes are complex in nature. They affect the mechanical properties of a weldment in and around the welding joint (in the heat affected zone: HAZ), causing deformation and inducing high level of residual stress and plastic strain which are detrimental to the weldment performance. After welding some materials soften while others harden in the heat affected zone, depending on the process heat input, the thickness of the material and its chemical composition. Traditionally, finite element (FE) performance analyses (crash, rupture, fatigue, static and dynamic tests) of weldments are performed without accounting for the effects of welding processes and as such the real performance of a weldment is not accurately predicted. On one hand, if base material properties are used to represent a weldment which hardens in the heat affected zone, the performance analysis results would be too conservative which would hinder/limit potential weight reduction strategies.

The truck cab is made of many structural members like hinge, A/B/C - pillar, rocker, roof rails, headliner, quarter panels, cross-members at the floor and other body panels. For an acoustic example, the source energy travels easily from one end to another end through pillars. To reduce these acoustic effects, the filler foams were added inside the pillars. The proper usage of filler design and filler material type produces the optimal sound response at the driver head space location. In this paper, an analytical method is used to evaluate the acoustic performance of the fillers as described above and the method also avoids the expensive full vehicle tests. The statistical energy analysis (SEA) model simulations and post-processing techniques were used to evaluate the results quickly with an acceptable level of accuracy.

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.

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.

An existing system-level Statistical Energy Analysis (SEA) model of an enclosed operator station (cab) for a combine-harvester was improved through component-level analyses using Finite Element (FE) and hybrid FE-SEA methods. At mid to high audio frequencies, airborne transmission of machine noise is a dominant path for the cab. An SEA model was created for the cab using the VA One product. When model results were validated against experimental data derived from three idealized insonification load cases, the original model did not compare well with the measured data. The structural panels used in the cab feature various non-uniform cross-sections and varying radii of curvature. The former are not appropriately modeled with standard beam stiffeners, and the latter must be accounted for by some average curvature. Geometrically accurate Finite Element (FE) models of the panels were employed to estimate parameters including effective material stiffness, and effective material density.

A model of a tractor cab was built using Statistical Energy Analysis (SEA) best practices. In this paper, it is shown how this model was correlated using p/Q transfer functions measured in the lab with a volume velocity source. After correlation, the model was excited using acoustic loads measured during tractor operation. It was found that the data predicted by the model is in good agreement with the data measured inside the cabin during this test. It was concluded that SEA can be used as an engineering tool to predict the behavior under many different conditions and can be used to guide the development process.

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.

Electric and Hybrid vehicles have standards for emitting enough noise to reduce danger and risk to pedestrians when operating at low speeds. Simulation can help to support development and deployment of these systems while avoiding a time-consuming, test-based approach to design these AVAS (Acoustic Vehicle Alerting System) warning systems. Traditionally, deterministic simulation methods such as Finite Element Method (FEM) and Boundary Element Method (BEM) are used at low frequencies and statistical, energy-based methods such as Statistical Energy Analysis (SEA) are used at high frequencies. The deterministic methods are accurate, but computationally inefficient, particularly when the frequency increases. SEA is computationally efficient but does not capture well the physics of exterior acoustic propagation. An alternative method commonly used in room acoustics, based on geometrical or ray acoustics, is “Ray Tracing” and can be used for sound field prediction.

The demand for seating comfort is growing - in cars as well as trucks and other commercial vehicles. This is expected as the seat is the largest surface area of the vehicle that is in contact with the occupant. While it is predominantly luxury cars that have been equipped with climate controlled seats, there is now a clear trend toward this feature becoming available in mid-range and compact cars. The main purpose of climate controlled seats is to create an agreeable microclimate that keeps the driver comfortable. It also reduces the “stickiness” feeling which is reported by perspiring occupants on leather-covered seats. As part of the seat design process, a physical test is performed to record and evaluate the life cycle and the performance at ambient and extreme temperatures for the climate controlled seats as well as their components. The test calls for occupied and unoccupied seats at several ambient temperatures.

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.

Finite element analysis (FEA) is commonly used in the automotive industry to predict low frequency NVH behavior (<150 Hz) of structures. Also, statistical energy analysis (SEA) framework is used to predict high frequency (>400 Hz) noise transmission from the source space to the receiver space. A comprehensive approach addressing the entire spectrum (>20 Hz) by taking into account structure-borne and air-borne paths is not commonplace. In the works leading up to this paper a hybrid methodology was employed to predict structure-borne and air-borne transfer functions up to 1000 Hz by combining FEA and SEA. The dash panel was represented by FE structural subsystems and the noise control treatments (NCTs) and the pass-throughs were characterized via testing to limit uncertainty in modeling. The rest of the structure and the fluid spaces were characterized as SEA subsystems.

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.