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Quality and refinement are of paramount importance for luxury vehicles. The rapid electrification of the automotive industry has increased the contribution of aeroacoustics to the consumer perception of sound quality. The ability to predict whole vehicle aeroacoustic interior noise is essential in the development of vehicles with an extraordinary acoustic environment. This publication summarises the development of a process to combine lattice Boltzmann computational fluid dynamics simulations, with a whole vehicle statistical energy analysis model, to predict the aeroacoustic contribution from all relevant sources and paths. The ability to quantify the relative contribution of glazing panels and path modifications was also investigated. The whole vehicle aeroacoustic interior noise predictions developed, were found to be within 2dB(A) of comparable test vehicle wind tunnel measurements, across a broad frequency range (250-5000 Hz).

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.

Wind noise is a major contributor to vehicle noise and a very common consumer complaint for overall vehicle quality [1]. The reduction of wind noise is becoming even more important as powertrain noise is reduced or eliminated (by conversion to hybrid and electric vehicles) and as the importance of quiet interior environment for hands-free device use and voice activation systems becomes more pronounced. In contrast to other noise sources such as tires, engine, intake, exhaust or other component noise whose acoustic loads may be measured in a direct and standardized way with the proper equipment, wind noise is very difficult to predict because the acoustic part of wind noise is a small component of overall fluctuating pressures. It is very challenging to either directly measure or to simulate the acoustic component of fluctuating exterior pressures using CFD (Computational Fluid Dynamics) without a great deal of specialized experience in this area.

Statistical Energy Analysis (SEA) is widely used for modeling the vibro-acoustic response of large and complex structures. SEA makes simulations practical thanks to its intrinsic statistical approach and the lower computational cost compared to FE-based techniques. However, SEA still requires underlying models for subsystems and junctions to compute the SEA coefficients which appear in the power balance equations of the coupled system. Classically, such models are based on simplified descriptions of the structures to allow analytical or semi-analytical developments. To overcome this limitation, the authors have proposed a general approach to SEA which only requires the knowledge of impedances of the structures to compute SEA coefficients. Such impedances can always be computed from an accurate FE model of each component of a coupled system.

Predicting Vehicle Pass-by noise using simulation enables efficient development of adequate countermeasures to meet legislative targets while reducing development time and the number of physical trial-and-error prototypes and tests. It has already been shown that deterministic simulation methods such as the Boundary Element Method (BEM), which may also include directivity of sources, can support the trim package optimization process for Pass-by noise, especially for low to mid frequencies. At higher frequencies, the Ray Tracing technique, can represent an efficient alternate providing options to trade off speed versus accuracy compared to wave-based technique such as FE/BEM. This paper presents a Ray Tracing approach with high order diffraction effect. Moreover, source directivity and sound package effect are accounted for.

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.

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.