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Tire/Road noise is a dominant contribution to a vehicle interior noise and requires significant engineering resources during vehicle development. A process has been developed to support automotive OEMs with road noise engineering during vehicle design and development which has test as its basis but takes advantage of simulation to virtually accelerate road noise improvement. The process uses airborne noise sources measured on a single tire installed on a test stand. The measured sources are then combined with vehicle level transfer functions calculated using a Statistical Energy Analysis (SEA) model to predict the sound at the driver ears. The process can be applied from the early stages of a vehicle development program and allows the evaluation of vehicle road noise performance as perceived by the driver long before the first prototype is available. This process is also extensible to other types of sources and loads impacting vehicle interior acoustics.

To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing.

The development of an effective Acoustic Vehicle Alert System (AVAS) is not solely about adhering to safety regulations; it also involves crafting an auditory experience that aligns with the expectations of vulnerable road users. To achieve this, a deep understanding of the acoustic transfer function is essential, as it defines the relationship between the sound emitter (the speaker inside the vehicle) and the receiver (the vulnerable road user). Maintaining the constancy of this acoustic transfer function is paramount, as it ensures that the sound emitted by the vehicle aligns with the intended safety cues and brand identity that is defined by the car manufacturer. In this research paper, three distinct methodologies for calculating the acoustic transfer function are presented: the classical Boundary Element method, the H-Matrix BEM accelerated method, and the Ray tracing method.

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.

In this paper, we present the process we propose to evaluate the effect of the laminated glass interlayer material on the Noise, Vibration and Harshness (NVH) performance of vehicles. The process starts from lab measurements to evaluate the glass damping and sound transmission loss and demonstrate the benefit of using optimized interlayer material. The results are then used to develop filters to adapt a vehicle model created in a driving simulator for NVH. With this process, it is not necessary to physically install the windshield in the vehicle to be able to listen and experience the benefits of switching to an optimized interlayer material. We use results from a real application case to verify the validity of the process and we share in our conclusions the future direction for this work to make the process completely virtual by using CAE simulation data.

Powertrain NVH and Powertrain Sound Quality requirements are among the key attributes to meet when developing new engines or vehicles. Source-Path-Contribution (SPC) solutions are commonly used to support the vehicle design and development. They allow to quantify the relative contributions of the different excitation sources, whether airborne or structure-borne, and the transfer paths to the noise and vibration measured at the receiver locations. When performed in time domain, SPC analysis is also a very effective tool to evaluate interactively the powertrain Sound Quality and how it can be affected by design changes. In this paper, we present a joint project performed by B&K Global Engineering Services together with Subaru where the team leveraged SPC models for powertrain noise of existing vehicles to create a new virtual vehicle assembly when the powertrain from the first vehicle is installed in the body of the second vehicle.

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.