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Tire/Road noise is a dominant contribution to a vehicle interior noise and requires significant engineering resources during vehicle development. A process has been developed to support automotive OEMs with road noise engineering during vehicle design and development which has test as its basis but takes advantage of simulation to virtually accelerate road noise improvement. The process uses airborne noise sources measured on a single tire installed on a test stand. The measured sources are then combined with vehicle level transfer functions calculated using a Statistical Energy Analysis (SEA) model to predict the sound at the driver ears. The process can be applied from the early stages of a vehicle development program and allows the evaluation of vehicle road noise performance as perceived by the driver long before the first prototype is available. This process is also extensible to other types of sources and loads impacting vehicle interior acoustics.

Quality and refinement are of paramount importance for luxury vehicles. The rapid electrification of the automotive industry has increased the contribution of aeroacoustics to the consumer perception of sound quality. The ability to predict whole vehicle aeroacoustic interior noise is essential in the development of vehicles with an extraordinary acoustic environment. This publication summarises the development of a process to combine lattice Boltzmann computational fluid dynamics simulations, with a whole vehicle statistical energy analysis model, to predict the aeroacoustic contribution from all relevant sources and paths. The ability to quantify the relative contribution of glazing panels and path modifications was also investigated. The whole vehicle aeroacoustic interior noise predictions developed, were found to be within 2dB(A) of comparable test vehicle wind tunnel measurements, across a broad frequency range (250-5000 Hz).

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

The Deep Orange program is a concept vehicle development program focused on providing hands-on experience in design, engineering, prototyping and production planning as part of students’ two-year MS graduate education. Throughout this project, the team was challenged to create innovative concepts during the ideation phase as part of building the running vehicle. This paper describes the usability studies performed on two of the vehicle concepts that require driver interaction. One concept is a human machine interface (HMI) that uses a holographic companion that can act as a concierge for all functions of the vehicle. After creating a prototype using existing technologies and developing a user interface controlled by hand gestures, a usability study was completed with older adults. The results suggest the input method was not intuitive. Participants demonstrated better performance with tasks using discrete hand motions in comparison to those that required continuous motions.

A key criterion for launching autonomous vehicles on real roads is the knowledge of their capability to ensure traffic safety. In contrast to ADAS, deriving this measure of safety is difficult to achieve as the functional scope of an autonomous driving function exceeds by far the one of ADAS. As a consequence, real-world testing solely is not sufficient enough to cover the required test volume. This assessment problem imposes new requirements on a valid test concept for automated driving. A possible solution represents simulation by enabling it to generate reliable test kilometers. As a first step, we discuss in this paper the feasibility of simulation frameworks to re-simulate a real-world test in certain scenarios. We will demonstrate that even with ground truth information of the vehicle odometry and corresponding environment model an acceptable accordance of functional behavior is not guaranteed.

Wind noise is a major contributor to vehicle noise and a very common consumer complaint for overall vehicle quality [1]. The reduction of wind noise is becoming even more important as powertrain noise is reduced or eliminated (by conversion to hybrid and electric vehicles) and as the importance of quiet interior environment for hands-free device use and voice activation systems becomes more pronounced. In contrast to other noise sources such as tires, engine, intake, exhaust or other component noise whose acoustic loads may be measured in a direct and standardized way with the proper equipment, wind noise is very difficult to predict because the acoustic part of wind noise is a small component of overall fluctuating pressures. It is very challenging to either directly measure or to simulate the acoustic component of fluctuating exterior pressures using CFD (Computational Fluid Dynamics) without a great deal of specialized experience in this area.

Optimizing noise control treatments in the early design phase is crucial to meet new strict regulations for exterior vehicle noise. Contribution analysis of the different sources to the exterior acoustic performance plays an important role in prioritizing design changes. A method to predict Pass-by noise performance of a car, based on source-path-receiver approach, combining data coming from simulation and experimental campaigns, is presented along with its validation. With this method the effect of trim and sound package on exterior noise can be predicted and optimized.

The analysis of the acoustic behavior of flow fields has gained importance in recent years, especially in the automotive industry. The comfort of the driver is heavily influenced by the noise levels and characteristics, especially during long distance drives. Simulation tools can help to analyze the acoustic properties of a car at an early stage of the development process. This work focuses on the low-frequency sound effects, which can be a significant noise component under certain operating conditions. As a first step in the fluid-structure interaction workflow, the flow around a series-production vehicle is simulated, including passenger cabin and underhood flow. The complexity of this model poses extensive demands on the simulation software, concerning meshing, turbulence modeling and level of parallelism. We conducted a transient simulation of the compressible fluid flow, using a hybrid RANS/LES approach.

In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

To develop future electrical and autonomous cars, it is important to virtually test the car in real driving circumstances, including on wet road or under heavy rain conditions. It is especially critical to check that no water prevents the sensors of the driving assistance systems or autonomous cars from working properly, that water intrusion does not disturb electrical equipment, and that the driver’s visibility remains good under rain condition. ESI Group has introduced the Finite Point Method (FPM) in Virtual Performance Solution (VPS) as a CFD mesh free module in order to simulate the interaction of water with the car structure. It was initially specialized for tank sloshing and water drain applications for car closures and is now extended to other application fields. The objective is to enable a holistic prediction of the car behavior under realistic driving conditions, using a virtual car prototype.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.

In the framework of noise reduction of HVAC (Heating, Ventilating and Air Conditioning) systems designed for cars, the present study deals with the numerical prediction of aeroacoustics phenomena encountered inside such devices for industrial purposes, i.e. with a reasonable CPU time. It is then proposed in this paper to assess the validity of the chaining, via Lighthill-Curle analogy, of a DES (Detached Eddy Simulation) resulting from the CFD code OpenFOAM (ESI Group) versus a RANS-LES (Large Eddy Simulation) and a BEM calculation resulting from the Vibro/Aeroacoustics software VA One (ESI Group) on an academic case of air passing through a rectangular diaphragm at a low Mach number. The BEM code being parallelized, the performances of DMP (Distributed Memory Processing) solution will also be assessed.

The quiet nature of hybrid and electric vehicles has triggered developments in research, vehicle manufacturing and legal requirements. Currently, three countries require fitting an Approaching Vehicle Alerting System (AVAS) to every new car capable of driving without a combustion engine. Various other geographical areas and groups are in the process of specifying new legal requirements. In this paper, the design challenges in the on-going process of designing the sound for quiet cars are discussed. A proposal is issued on how to achieve the optimum combination of safety, environmental noise, subjective sound character and technical realisation in an iterative sound design process. The proposed sound consists of two layers: the first layer contains tonal components with their pitch rising along with vehicle speed in order to ensure recognisability and an indication of speed.

As computational methodologies become more integrated into industrial vehicle pre-development processes the potential for high transient vehicle thermal simulations is evident. This can also been seen in conjunction with the strong rise in computing power, which ultimately has supported many automotive manufactures in attempting non-steady simulation conditions. The following investigation aims at exploring an efficient means of utilizing the new rise in computing resources by resolving high time-dependent boundary conditions through a series of averaging methodologies. Through understanding the sensitivities associated with dynamic component temperature changes, optimised boundary conditions can be implemented to dampen irrelevant input frequencies whilst maintaining thermally critical velocity gradients.

Within the pre-development phase of a vehicle validation process, the role of computational simulation is becoming increasingly prominent in efforts to ensure thermal safety. This gain in popularity has resulted from the cost and time advantages that simulation has compared to experimental testing. Additionally many of these early concepts cannot be validated through experimental means due to the lack of hardware, and must be evaluated via numerical methods. The Race Track Simulation (RTS) can be considered as the final frontier for vehicle thermal management techniques, and to date no coherent method has been published which provides an efficient means of numerically modeling the temperature behavior of components without the dependency on statistical experimental data.

Over the past 30 years, the computer-aided engineering (CAE) tools have been applied extensively in the automotive industry. In order to accelerate time-to-market while coping with legal limits that have become increasingly restrictive over the last decades, CAE has become an indispensable tool covering all major fields in a modern automotive product design process. However, when tackling complex real-life engineering problems, the computational models might become rather involved and thus less efficient. Therefore, the overall trend in the automotive industry is currently heading towards combined approaches, which allow the best of the both worlds, namely the experimental measurement and numerical simulation, to be merged into one integrated scheme. In this paper, the so-called patch transfer function (PTF) approach is adopted to solve coupled vibro-acoustic problems. In the PTF scheme, the interfaces between fluid and structure are discretised in terms of patches.

According to upcoming legislative regulations in certain countries, electric and hybrid-electric vehicles (EVs and HEVs) will have to be equipped with devices to compensate for the lack of engine noise needed to warn pedestrians against the vehicles. This leads to the question of appropriate sound design which has to meet specific psychoacoustic requirements. The present paper focuses on auditory features of warning sounds to enhance pedestrians' safety with a major focus on the detectability of the exterior noise of the vehicle in an ambient noise. For the evaluation of detectability, the psychoacoustic model developed by Kerber and Fastl will be introduced allowing for the prediction of masked thresholds of the approaching vehicle. The instrumental assessment yields estimates of the distance of an approaching vehicle at the point it becomes audible to the pedestrians.

Aerodynamic performance assessment of automotive shapes is typically performed in wind tunnels. However, with the rapid progress in computer hardware technology and the maturity and accuracy of Computational Fluid Dynamics (CFD) software packages, evaluation of the production-level automotive shapes using a digital process has become a reality. As the time to market shrinks, automakers are adopting a digital design process for vehicle development. This has elevated the accuracy requirements on the flow simulation software, so that it can be used effectively in the production environment. Evaluation of aerodynamic performance covers prediction of the aerodynamic coefficients such as drag, lift, side force and also lift balance between the front and rear axle. Drag prediction accuracy is important for meeting fuel efficiency targets, prediction of front and rear lifts as well as side force and yawing moment are crucial for high speed handling.

The comfort assessment of seats in the automotive industry has historically been accomplished by subjective ratings. This approach is expensive and time consuming since it involves multiple prototype seats and numerous people in supporting processes. In order to create a more efficient and robust method, objective metrics must be developed and utilized to establish measurable boundaries for seat performance. Objective measurements already widely accepted, such as IFD (Indentation Force Deflection) or CFD (Compression Force Deflection) [1], have significant shortcomings in defining seat comfort. The most obvious deficiency of these component level tests is that they only deal with a seats' foam rather than the system response. Consequently, these tests fail to take into account significant factors that affect seat comfort such as trim, suspension, attachments and other components.