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For most car manufacturers, aerodynamic noise is becoming the dominant high frequency noise source (> 500 Hz) at highway speeds. Design optimization and early detection of issues related to aeroacoustics remain mainly an experimental art implying high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the development of a reliable numerical prediction capability. The goal of this paper is to present a computational approach developed to predict the greenhouse windnoise contribution to the interior noise heard by the vehicle passengers. This method is based on coupling an unsteady Computational Fluid Dynamics (CFD) solver for the windnoise excitation to a Statistical Energy Analysis (SEA) solver for the structural acoustic behavior.

This paper presents an approach to numerically simulate greenhouse windnoise. The term “greenhouse windnoise” here describes the sound transferred to the interior through the glass panels of a series vehicle. Different panels, e.g. the windshield or sideglass, are contributing to the overall noise level. Attached parts as mirrors or wipers are affecting the flow around the vehicle and thus the pressure fluctuations which are acting as loads onto the panels. Especially the wiper influence and the effect of different wiper positions onto the windshield contribution is examined and set in context with the overall noise levels and other contributors. In addition, the effect of different flow yaw angles on the windnoise level in general and the wiper contributions in particular are demonstrated. As computational aeroacoustics requires accurate, highly resolved simulation of transient and compressible flow, a Lattice-Boltzmann approach is used.

Acoustic comfort of automotive cabins has progressively become one of the key attributes of passenger comfort within vehicle design. Wind noise and the heating, ventilation, and air conditioning (HVAC) system noise are two of the key contributors to noise levels heard inside the car. The increasing prevalence of hybrid technologies and electrification has an associated reduction in powertrain noise levels. As such, the industry has seen an increasing focus on understanding and minimizing HVAC noise, as it is a main source of noise in the cabin particularly when the vehicle is stationary. The complex turbulent flow path through the ducts, combined with acoustic resonances can potentially lead to significant noise generation, both broadband and tonal.

Over the past decades, interior noise from wind noise or engine noise have been significantly reduced by leveraging improvements of both the overall vehicle design and of sound package. Consequently, noise sources originating from HVAC systems (Heat Ventilation and Air Conditioning), fans or exhaust systems are becoming more relevant for perceived quality and passenger comfort. This study focuses on HVAC systems and discusses a Flow-Induced Noise Detection Contributions (FIND Contributions) numerical method enabling the identification of the flow-induced noise sources inside and around HVAC systems. This methodology is based on the post-processing of unsteady flow results obtained using Lattice Boltzmann based Method (LBM) Computational Fluid Dynamics (CFD) simulations combined with LBM-simulated Acoustic Transfer Functions (ATF) between the position of the sources inside the system and the passenger’s ears.

Exhaust and muffler noise is a challenging problem in the transport industry. While the main purpose of the system is to reduce the intensity of the acoustic pulses originating from the engine exhaust valves, the back pressure induced by these systems must be kept to a minimum to guarantee maximum performance of the engine. Emitted noise levels have to ensure comfort of the passengers and must respect community noise regulations. In addition, the exhaust noise plays an important role in the brand image of vehicles, especially with sports car where it must be tuned to be “musical”. However, to achieve such performances, muffler and exhaust designs have become quite complex, often leading to the rise of undesired self-induced noise. Traditional purely acoustic solvers, like Boundary Element Methods (BEM), have been applied quite successfully to achieve the required acoustic tuning.

Exhaust systems are a necessary solution to reduce combustion engine noise originating from flow fluctuations released at each firing cycle. However, exhaust systems also generate a back pressure detrimental for the engine efficiency. This back pressure must be controlled to guarantee optimal operating conditions for the engine. To satisfy both optimal operating conditions and optimal noise levels, the internal design of exhaust systems has become complex, often leading to the emergence of undesired noise generated by turbulent flow circulating inside a muffler. Associated details needed for the manufacturing process, such as brackets for the connection between parts, can interact with the flow, generating additional flow noise or whistles. To minimize the risks of undesirable noise, multiple exhaust designs must be assessed early to prevent the late detection of issues, when design and manufacturing process are frozen. However, designing via an experimental approach is challenging.

The object of the validation study presented in this paper is a generic vehicle, the so-called SAE body, developed by a consortium of German car manufacturers (Audi, Daimler, Porsche, Volkswagen). Many experiments have been performed by the abovementioned consortium on this object in the past to investigate its behavior when exposed to fluid flow. Some of these experiments were used to validate the simulation results discussed in the present paper. It is demonstrated that the simulation of the exterior flow is able to represent the transient hydrodynamic structures and at the same time both the generation of the acoustic sources and the propagation of the acoustic waves. Performing wave number filtering allows to identify the acoustic phenomena and separate them from the hydrodynamic effects. In a next step, the noise transferred to the interior of the cabin through the glass panel was calculated, using a Statistical Energy Analysis approach.

Wind noise has become an important indicator for passenger automobile quality. Several transmission paths can be related to different parts of the vehicle exterior. While the greenhouse (side glasses, windshield, seals & others) often dominates the interior noise level above 500 Hz, the contribution coming from the underbody area usually dominates the interior noise spectrum at lower frequencies. This paper describes a framework of numerical tools which is capable of determining realistic underbody turbulent and acoustic loads being generated for typical driving conditions, as well as performing the noise transmission through underbody panels and the propagation of sound to the drivers ear location.

Developing a low interior noise level of vehicles is a big challenge - even a greater one if one thinks about aeroacoustics. Aeroacoustic noise and its origins are usually identified with the help of prototypes when exterior design changes or the replacement of exterior parts like side mirrors are very limited. However, computational aeroacoustic (CAA) methods in virtual project phases offer more design options for the vehicle's geometric shape. The early consideration of aeroacoustic relevant design changes helps to keep project costs low by avoiding tool changes. This paper describes MAGNA STEYR's virtual aeroacoustic process starting from standardized model generation and simulation of wind noise, including the validation of computational results via comparison with measurement data gathered in an acoustic wind tunnel. The simulations are carried out using the commercial CAA code “PowerFLOW” (Exa) based on the Lattice-Boltzmann method.

Design of HVAC system plays an important role in acoustic comfort for passengers. With automotive world moving towards electrical vehicles where powertrain noise is low, designing low noise HVAC system is becoming more important. For an automobile manufacturer, ability to predict the production vehicle cabin noise at the early design stage is important as it allows more freedom for design changes, which can be incorporated in the vehicle at lower cost. Although HVAC prototype and system level testing at early design stage is possible for noise estimation but flow field is not visible in test that makes difficult to improve design. CFD simulation can provide detailed information on flow field, noise source strength and location. But in such a simulation, accurate prediction has been a challenge due to the inability of CFD tools to model acoustic absorptive characteristics of interior walls of cabin.

Wind noise is a significant source of interior noise in automobiles at cruising conditions, potentially creating dissatisfaction with vehicle quality. While wind noise contributions at higher frequencies usually originate with transmission through greenhouse panels and sealing, the contribution coming from the underbody area often dominates the interior noise spectrum at lower frequencies. Continued pressure to reduce fuel consumption in new designs is causing more emphasis on aerodynamic performance, to reduce drag by careful management of underbody airflow at cruise. Simulation of this airflow by Computational Fluid Dynamics (CFD) tools allows early optimization of underbody shapes before expensive hardware prototypes are feasible. By combining unsteady CFD-predicted loads on the underbody panels with a structural acoustic model of the vehicle, underbody wind noise transmission could be considered in the early design phases.

With the rise of electric autonomous vehicles, it has become clear that the cabin of tomorrow will drastically evolve to both improve ride experience and reduce energy consumption. In addition, autonomy will change the transportation paradigm, leading to a reinvention of the cabin seating layout which will offer the opportunity to climate systems team to design quiet and even more energy efficient systems. Consequently, Heat and Ventilation Air Conditioning (HVAC) systems designers have to deliver products which perform acoustically better than before, but often with less development time. To success under such constraints, designers need access to methods providing both assessment of the system (or subsystems) acoustic performance, and identification of where the designs need to be improved to reduce noise levels. Such methods are often needed before a physical prototype is requested, and thus can only be achieved in a timely manner through digital testing.