Search Results

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.

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.

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.