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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.

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.

Automotive Original Equipment Manufacturers (OEMs) are always striving to deliver fast Air-Conditioning (AC) pulldown performance with consistent distribution of cabin temperature to meet customer expectations. The ultimate test is the OEM standard, called “AC Pull Down,” conducted at high ambient temperature and solar load conditions with a prescribed vehicle drive cycle. To determine whether the AC system in the vehicle has the capacity to cool the cabin, throughout the drive cycle test, cabin temperature measurements are evaluated against the vehicle target. If the measured cabin temperatures are equal or lower than the required temperatures, the AC system is deemed conventional for customer usage. In this paper, numerical predictions of the cabin temperatures to replicate the AC pulldown test are presented. The AC pulldown scenario is carried out in a digital Climatic Wind Tunnel simulation. The solution used in this study is based on a coupled approach.

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.

The demand for low noise level in vehicle cabin continues to rise lately. In particular, noise generated by eco-friendly cars such as hybrid and electric ones tends to become lower and lower. In this market environment, the noise contributions caused by HVAC systems are also increasing. Therefore, it becomes increasingly important to accurately predict noise generated by HVAC systems and analyze the noise sources and resolve the noise issue. In this study, direct acoustics prediction approach based on Lattice Boltzmann Method is applied to predict the flow-induced noise from HVAC systems including blower and ducts and find noise sources. In order to validate the simulation result, acoustics measurements are performed on HVAC systems in an anechoic room and the results are compared to each other. A new technique is applied to finding a noise source for a specific frequency and shows improved noise level through modifying the geometry related to noise sources detected by the simulation.

Exhaust systems including mufflers are commonly mounted on engines to reduce the firing cycle noise originating from the combustion process. However, mufflers also produce flow-induced self-noise, originating from the complex flow path throughout the muffler. As an engine prototype is not available in the early stages of a development program, it is challenging to assess the acoustic performance of the full system when only experiment is available. It is also difficult to pinpoint the design features of a muffler generating noise, as a portion of the noise is generated internally. Numerical approaches are a possible alternative. However, capturing non-linear dissipation mechanisms and thermal fluctuations of exhaust flows is challenging, while necessary to accurately predict flow noise.

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.

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.

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.

Acoustics comfort is a key point for the ground transportation market and in particular in the automotive area. A significant contributor to the noise levels in the cabin in the range 200Hz to 3000Hz is the HVAC (Heating, Ventilating, and Air Conditioning) system, consisting of sub-systems such as the air intake duct, thermal mixing unit, blower, ducts, and outlet vents. The noise produced by an HVAC system is mainly due to aeroacoustics mechanisms related to the flow fluctuations induced by the blower rotation. The structure borne noise related to the surface induced vibrations and to the noise transmission through the dash or plastic panels may also contribute but is not considered in this study. This study presents a digital approach for HVAC aeroacoustics noise predictions related to the ducts and outlet vents. In order to validate the numerical method flow and acoustics measurements are performed on production HVAC systems placed in an anechoic room.

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.