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Cooling airflow is an essential factor when it comes to vehicle performance and operating safety. In recent years, significant efforts have been made to maximize the flow efficiency through the heat exchangers in the under-hood compartment. Grille shutters, new fan shapes, better sealings are only some examples of innovations in this field of work. Underhood cooling airflow simulations are an integral part of the vehicle development process. Especially in the early development phase, where no test data is available to verify the cooling performance of the vehicle, computational fluid dynamics simulations (CFD) can be a valuable tool to identify the lack of fan performance and to develop the appropriate strategy to achieve airflow goals through the heat exchangers. For vehicles with heat exchangers in the underhood section the airflow through those components is of particular interest.

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.

Automobile hood design is driven by many factors, such as strict government regulations, fuel economy, weight, manufacturability, aerodynamic performance, aesthetics, structural integrity, and pedestrian safety standards. The requirement of improved fuel economy and safety regulations like pedestrian protection drive designers to reduce the thickness of the hood parts and use lighter materials. This leads to significant reduction in the hood stiffness. The hood needs to withstand steady and unsteady aerodynamic loads and meet deflection and vibration targets. The susceptibility of the hood to adverse aero load response is increased as the stiffness of the hood is reduced. The objective of this study is to develop a methodology to simulate hood behavior under transient aerodynamic loads in controlled environments. This study mainly focuses on developing fluid structure interaction methodology to simulate the behavior of the hood system under transient aerodynamic loads.

One of the key inputs 1-D transient simulation takes is a detailed front end cooling flow map. These maps that are generated using a full vehicle Three-dimensional Computational Fluid Dynamics (3D CFD) model require expensive computational resources and time. This paper describes how an adaptive sampling of the design space allowed the reduction of computational efforts while keeping desired accuracy of the analysis. The idea of the method was to find a pattern of Design of Experiments (DOE) sampling points for 3D CFD simulations that would allow a creation of an approximation model accurate enough to predict output parameter values in the entire design space of interest. Three procedures were implemented to get the optimal sampling pattern.

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.

A vehicle driving down the road naturally pitches, rolls and heaves due to road inputs (for example, bumps, potholes, driving dynamics, etc.) and also due to the influence of aerodynamic forces. The vehicle attitude changes directly as a result of aerodynamic forces that can be seen during wind tunnel testing of production level vehicles, with some measurements possible in order to evaluate the aerodynamics effects. This naturally occurring phenomenon is not always represented in aerodynamics simulations, either for reduced scale models or computational fluid dynamics (CFD) simulations or even rigid body full scale testing. It can be shown through visual techniques how much deflection is typically occurring, including both vehicle attitude changes as well as vehicle body distortions. From the analysis, an adjustment to the CFD models can be made to compensate for the aerodynamics effects.

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.

The General Motors Reduced Scale Wind Tunnel Facility, which came into operation in the fall of 2015, is a new state-of-the-art scale model aerodynamic test facility that expands GM’s test capabilities. The new facility also increases GM’s aerodynamic testing through-put and provides the resources needed to achieve the growing demand for higher fuel economy requirements for next generation of vehicles. The wind tunnel was designed for a nominal model scale of 40%. The nozzle and test section were sized to keep wind tunnel interference effects to a minimum. Flow quality and other wind tunnel performance parameters are on par with or better than the latest industry standards. A 5-belt system with a long center belt and boundary layer suction and blowing system are used to model underbody flow conditions. An overhead probe traverse system is installed in the test section along with a model positioning robot used to move the model in an out of the test section.

Cost benefit and teraflop restrictions imposed by racing sanctioning bodies make steady-state RANS CFD simulation a widely accepted first approximation tool for aerodynamics evaluations in motorsports, in spite of its limitations. Research involving generic and simplified vehicle bodies has shown that the veracity of aerodynamic CFD predictions strongly depends on the turbulence model being used. Also, the ability of a turbulence model to accurately predict aerodynamic characteristics can be vehicle shape dependent as well. Modifications to the turbulence model coefficients in some of the models have the potential to improve the predictive capability for a particular vehicle shape. This paper presents a systematic study of turbulence modeling effects on the prediction of aerodynamic characteristics of a NASCAR Gen-6 Cup racecar. Steady-state RANS simulations are completed using a commercial CFD package, STAR-CCM+, from CD-Adapco.

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.

One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.

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.

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.