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In vehicle NVH development, vibroacoustic simulations with Finite Element (FE) models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time, the need for multiple evaluations of the same model with different input parameters, e.g., for uncertainty quantification, optimization, or robustness investigations, is also increasing. Therefore, it is crucial to reduce the computational costs in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

Air conditioning acoustics have become of paramount importance in electric vehicles, where noise from electromechanical components is no longer masked by the presence of the internal combustion engine. In a car HVAC systems, the coolant compressor is one of the most important sources in terms of vibration and noise generation. The paper, the generated structural noise is studied in detail on a prototype installation, and the noise transmission and propagation mechanisms are analyzed and discussed. Through ”in situ” measurements and virtual point transformation, the rotor unbalance forces and torque acting within the component are identified. The dynamic properties of the rubber mounts, installed between the compressor and its support, are identified thanks to matrix inversion methods. To assess the quality of the proposed procedure, the synthesized sound pressure level is compared with experimental SPL measurements in different operational conditions.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

It is particularly easy to get tunnel vision as a domain expert, and focus only on the improvements one could provide in their area of expertise. To make matters worse, many Original Equipment Manufacturers (OEMs) are silo-ed by domain of expertise, unconsciously promoting this single mindedness in design. Unfortunately, the successful and profitable development of a vehicle is dependent on the delicate balance of performance across many domains, involving multiple physics and departments. Taking for instance the design of a Heating, Ventilation & Air Conditioning (HVAC) system, the device’s primary function is to control the climate system in vehicle cabins, and more importantly to make sure that critical areas on the windshield can be defrosted in cold weather conditions within regulation time. With the advent of electric and autonomous vehicles, further importance is now also placed on the energy efficiency of the HVAC, and its noise.

It is essential to include uncertainties in the simulation process in order to perform reliable vibroacoustic predictions in the early design phase. In this contribution, uncertainties are quantified using the generalized Polynomial Chaos (gPC) expansion in combination with a Finite Element (FE) model of a vehicle body in white. It is the objective to particularly investigate the applicability of the gPC method in the industrial context with a high number of uncertain parameters and computationally expensive models. A non-intrusive gPC expansion of first and second order is implemented and the approximation of a stochastic response process is compared to a Latin Hypercube sampling based reference solution with special regard to accuracy and computational efficiency. Furthermore, the method is examined for other input distributions and transferred to another FE model in order to verify the applicability of the gPC method in practical applications.

During the last decades, big steps have been taken towards a realistic simulation of NVH (Noise Vibration Harshness) behavior of vehicles using the Finite Element (FE) method. The quality of these computation models has been substantially increased and the accessible frequency range has been widened. Nevertheless, to perform a reliable prediction of the vehicle vibroacoustic behavior, the consideration of uncertainties is crucial. With this approach there are many challenges on the way to valid and useful simulation models and they can be divided into three areas: the input uncertainties, the propagation of uncertainties through the FE model and finally the statistical output quantities. Each of them must be investigated to choose sufficient methods for a valid and fast prediction of vehicle body vibroacoustics. It can be shown by rough estimation that dimensionality of the corresponding random space for different types of uncertainty is tremendously high.

The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce.

This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

Windscreen wipers are an integral component of the windscreen cleaning systems of most vehicles, trains, cars, trucks, boats and some planes. Wipers are used to clear rain, snow, and dirt from the windscreen pushing the water from the wiped surface. Under certain conditions however, water which has been driven to the edge of the windscreen by the wiper can be drawn back into the driver’s field of view by aerodynamic forces introduced by the wiper motion. This is wiper drawback, an undesirable phenomenon as the water which is drawn back on to the windscreen can reduce driver’s vision and makes the wiper less effective. The phenomena of wiper drawback can be tested for in climatic tunnels using sprayer systems to wet the windscreen. However, these tests require a bespoke test property or prototype vehicle, which means that the tests are done fairly late in the development of the vehicle.

Vehicle thermal management (VTM) simulations constitute an important step in the early development phase of a vehicle. They help in predicting the temperature profiles of critical components over a drive cycle and identify components which are exceeding temperature design limits. Parts with the highest temperatures in a vehicle with an internal combustion engine are concentrated in the engine bay area. As packaging constraints grow tighter, the components in the engine bay are packed closer together. This makes the thermal protection in the engine bay even more crucial. The fan influences the airflow into the engine bay and plays an important role in deciding flow distribution in this region. This makes modelling of the fan an important aspect of VTM simulations. The challenge associated with modelling the fan is the accurate simulation of the rotation imparted by the fan to the incoming flow. Currently, two modelling approaches are prevalent in the industry.

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.

From an acoustical point of view, the integration of seatbelt retractors in a vehicle is a real challenge that has to be met early in the vehicle development process. The buzz and rattle noise of seat belt retractors is a weak yet disturbing interior noise. Street irregularities excite the wheels and this excitation is transferred via the car body to the mounting location of the retractor. Ultimately, the inertia sensor of the locking mechanism is also excited. This excitation can be amplified by structural resonances and generate a characteristic impact noise. The objective of this paper is to describe a simulation method for an early development phase that predicts the noise-relevant low frequency local modes and consequently the contact of the retractor with the mounting panel of the car body via the finite element method.

Ground transportation industry contributes to about 14% of the global CO2 emissions. Therefore, any effort in reducing global CO2 needs to include the design of cleaner and more energy efficient vehicles. Their design needs to be optimized for the real-world conditions. Using wind tunnels that can only reproduce idealized conditions quite often does not translate into real-world on-road CO2 reduction and improved energy efficiency. Several recent studies found that very rarely can the real-world environment be represented by turbulence-free conditions simulated in wind tunnels. The real-world conditions consist of both transversal flow velocity component (causing an oncoming yaw flow) as well as large-scale turbulent fluctuations, with length scales of up to many times the size of a vehicle. The study presented in this paper shows how the realistic wind affects the aerodynamics of the vehicle.

The DrivAer model, a detailed generic open source vehicle geometry, was introduced a few years ago and accepted widely from industry and academia for research in the field of automotive aerodynamics. This paper presents the evaluation of the aerodynamic properties of the 25% scale DrivAer model in both, CFD and in wind tunnel experiment. The results not only include aerodynamic drag and lift but also provide detailed investigations of the flow field around the vehicle. In addition to the available geometries of the DrivAer model, individual changes were introduced created by morphing the geometry of the baseline model. A good correlation between CFD and experiment could be achieved by using a CFD setup including the geometry of the wind tunnel test section. The results give insight into the aerodynamics of the DrivAer model and lead to a better understanding of the flow around the vehicle.

Charge air temperature needs to be kept low for optimum engine operation. If charge air temperature is too high, engine performance reduction strategies are invoked to protect engines by limiting torque available to drivers. A 1D-3D coupling simulation methodology is developed to accurately predict internal air temperature after charge air cooler (CAC) during a racing drive cycle. The 3D flow simulation is used to characterize external air flow before CAC in steady-state cases. Then, interpolated 3D simulation results between steady operating points are used as transient external air boundary conditions in front of CAC in a 1D system model. 3D flow simulation is also used to predict internal flow rate ratio between CAC tubes. Finally, an 1D system model is used to predict time-trace of charge air temperature at CAC internal outlet during the racing drive cycle. The simulation results show that prediction errors are within 5 degrees for charge air temperature at internal outlets.

The management of surface water flows driven from the wind screen by the action of wipers and aerodynamic shear is a growing challenge for automotive manufacturers. Pressure to remove traditional vehicle features, such as A-Pillar steps for aesthetic, aeroacoustic and aerodynamic reasons increases the likelihood that surface water may be convected over the A-Pillar and onto the front side glass where it can compromise drivers’ vision. The ability to predict where and under which conditions the A-Pillar will be breached is important for making correct design decisions. The use of numerical simulation in this context is desirable, as experimental testing relies on the use of aerodynamics test properties which will not be fully representative, or late-stage prototypes, making it difficult and costly to correct issues. This paper provides an update on the ability of simulation to predict A-Pillar overflow, comparing physical and numerical results for a test vehicle.

At SAIC-GM-Wuling (SGMW) the greenhouse wind noise performance of their vehicles has gained a lot of attention in the development process. In order to evaluate and improve the noise quality of a newly developed SUV a digital simulation based process has been employed during the early stage of the design. CFD simulation was used for obtaining the flow induced exterior noise sources. Performance metrics for the quality were based on interior noise levels which were calculated from the exterior sources using a SEA approach for the noise transmission through the glass panels and propagation to the driver’s or passenger’s head space. Detailed analysis of the CFD results allowed to identify noise sources and related flow structures. Based on this analysis, design modifications were then applied and tested in a sequential iterative process. As a result an improvement of more than 2 dB in overall sound pressure level could be achieved.

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 implementation of an advanced process for the aerodynamic development of cab-over type heavy trucks at China FAW Group Corporation (FAW) requires a rigorous validation of the tools employed in this process. The final objective of the aerodynamic optimization of a heavy truck is the reduction of the fuel consumption. The aerodynamic drag of a heavy truck contributes up to 50% of the overall resistance and thus fuel consumption. An accurate prediction of the aerodynamic drag under real world driving conditions is therefore very important. Tools used for the aerodynamic development of heavy trucks include Computational Fluid Dynamics (CFD), wind tunnels and track and road testing methods. CFD and wind tunnels are of particular importance in the early phase development.