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Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The novel method is based on non-linear theories in the frequency domain. It uses the harmonic balance method in combination with the alternating frequency/time domain method to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked.

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 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.

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.

Modern exhaust systems contain not only a piping network to transport hot gas from the engine to the atmosphere, but also functional components such as the catalytic converter and turbocharger. The turbocharger is common place in the automotive industry due to their capability to increase the specific power output of reciprocating engines. As the exhaust system is a main heat source for the under body of the vehicle and the turbocharger is located within the engine bay, it is imperative that accurate surface temperatures are achieved. A study by K. Haehndel [1] implemented a 1D fluid stream as a replacement to solving 3D fluid dynamics of the internal exhaust flow. To incorporate the 3D effects of internal fluid flow, augmented Nusselt correlations were used to produce heat transfer coefficients. It was found that the developed correlations for the exhaust system did not adequately represent the heat transfer of the turbocharger.

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.

The thermal prediction of a vehicle under-body environment is of high importance in the design, optimization and management of vehicle power systems. Within the pre-development phase of a vehicle's production process, it is important to understand and determine regions of high thermally induced stress within critical under-body components. Therefore allowing engineers to modify the design or alter component material characteristics before the manufacture of hardware. As the exhaust system is one of the primary heat sources in a vehicle's under-body environment, it is vital to predict the thermal fluctuation of surface temperatures along corresponding exhaust components in order to achieve the correct thermal representation of the overall under-body heat transfer. This paper explores a new method for achieving higher accuracy exhaust surface temperature predictions.

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.

The G-equation model was implemented in the commercial code ANSYS CFX and validated against experimental data in order to successfully simulate turbulent premixed combustion in internal combustion engines. The model is based on the level-set approach. Two transport equations are solved respectively for the G-scalar mean value, representing the local distance function from the time-averaged mean flame front, and its variance, correlated to the turbulent flame brush thickness. The model closure for tracking the flame front is based on an algebraic expression for the turbulent burning velocity. The composition of the reacted mixture is evaluated by coupling the code with flamelet libraries generated with the ANSYS CFX-RIF package by means of a reaction progress variable computed as a function of the G-related quantities.

Today digital 3D human models are widely used to support the development of future products and in planning and designing production systems. However, these virtual models are generally not sufficiently intuitive and configuring accurate and real body postures is very time consuming. Furthermore, additionally using a human model to virtually examine manual assembly operations of a vehicle is currently synonymous with increased user inputs. In most cases, the user is required to have in-depth expertise in the deployed simulation system. In view of the problems described, in terms of human-computer interaction, it is essential to research and identify the requirements for simulation with digital human models. To this end, experienced staff members gathered the requirements which were then evaluated and weighted by the potential user community. Weaknesses of the simulation software will also be detected, permitting optimisation recommendations to be identified.

This paper presents an integrated simulation process which has been performed in order to assess the riding comfort performance of a vehicle seat system virtually. Present methods of seat comfort design rely on the extensive testing of numerous hardware prototypes. In order to overcome the limitations of this expensive and time-consuming process, and to fasten innovation, simulation-based design has to be used to predict the seat comfort performance very early in the seat design process, leading to a drastic reduction in the number of physical prototypes. The accurate prediction of the seat transfer function by numerical simulation requires a complete simulation chain, which takes into account the successive stages determining the final seat behaviour when submitted to vibrations. First the manufacturing stresses inside the cushion, resulting from the trimming process, are computed.

AM-SC1™ is a high temperature Mg alloy that was originally developed as a sand casting alloy for automotive powertrain applications. The alloy has been selected as the engine block material for both the AVL Genios LE and the USCAR lightweight magnesium engine projects. The present work assesses the potential of this alloy for permanent-mould die cast applications. Thermo-physical and mechanical properties of AM-SC1 were determined for material derived from a permanent-mould die casting process. The mechanical properties determined included: tensile, creep, bolt load retention/relaxation and both low and high cycle fatigue. To better assess the creep performance, a comparative analysis of the normalized creep properties was carried out using the Mukherjee-Dorn parameter, which confirmed the high viscoplastic performance of AM-SC1 compared with common creep resistant high pressure die cast (HPDC) Mg-alloys.

In large-scale industrial simulations the numerical prediction of fracture in sheet metal forming operations as well as in crash events is still a challenging task of high social and economic relevance. Among several approaches presented in literature, the authors and their colleagues developed a model which accounts each for three different mechanisms leading finally to fracture in thin sheet metals: the local instability (necking), ductile normal fracture and ductile shear fracture. The focus of this paper is to develop and validate a new approach to improve the predictive capabilities for fracture triggered by localized necking for a wide variety of steel grades. It is well known that after the onset of a local instability additional strain is still necessary to induce fracture. In a numerical simulation using shell elements this post instability strain becomes of increasing importance when the ratio of the characteristic shell element edge length to its thickness decreases.

As the popularity of vehicle navigation systems rises, incorporating Real Time Traffic Information (RTTI) has been shown to enhance the systems' value by helping drivers avoid traffic delays. As an innovative premium automaker, BMW has developed a testing process to acquire and analyze RTTI data in order to ensure delivery of a high quality service and to enhance the customer experience compared to audible broadcast services. With a methodology to obtain valid and repeatable RTTI data quality measurements, BMW and its service partner, Clear Channel's Total Traffic Network (TTN), can improve its offered service over time, implement corrective measures when appropriate, and confidently ensure the service meets its premium objectives. BMW has partnered with TTN and SoftSolutions GmbH to implement a traffic data quality process and software tools.

A computational analysis of underbody windnoise sources on a production automobile at 180 km/h free stream air speed and 0° yaw is presented. Two different underbody geometry configurations were considered for this study. The numerical results have been obtained using the commercial software PowerFLOW. The simulation kernel of this software is based on the numerical scheme known as the Lattice-Boltzmann Method (LBM), combined with a two-equation RNG turbulence model. This scheme accurately captures time-dependent aerodynamic behavior of turbulent flows over complex detailed geometries, including the pressure fluctuations causing wind noise. Comparison of pressure fluctuations levels mapped on a fluid plane below the underbody shows very good correlation between experiment and simulation. Detailed flow analysis was done for both configurations to obtain insight into the transient nature of the flow field in the underbody region.

Being able to integrate consumer electronics (CE) devices into the automobile is an increasingly important goal. In this paper, we focus on the HMI (human machine interaction) aspects of consumer electronics in the car. We describe the requirements concerning HMI integration of consumer electronics and offer several possible solutions. One of the requirements is minimal driver distraction. A desired property in this context concerns the mental model that the user builds of the service that is to be operated: ideally, this model (i.e., appearance and interaction logic) need not change when integrating the service into the automobile, even though the operating elements differ considerably (e.g., touch screen vs. iDrive commander). A further requirement is posed by the dynamic nature of CE services: often, they are not known at design/deploy time of the HMI software of the automobile.

Comfort and well-being have always been connected with a flawless interior acoustic, free of any background noise or BSR, (buzz, squeak and rattle). BSR noises dominate the interior acoustic and represent one of the main sources for discomfort often causing considerable warranty costs. Traditionally BSR issues have been identified and rectified through extensive hardware testing, which by its nature intensifies toward the end of the car development process. In the following paper the integration of a virtual BSR validation technique in a modern development process by the use of appropriate CAE methods is presented. The goal is to shift, in compliance with the front loading concept, the development activities into the early phase. The approach is illustrated through the example of an instrument panel, from the early concept draft for single components to an assessment of the complete assembly.

In order to meet the severe emission restrictions imposed by SULEV and EURO V standards the catalytic converter must reach light-off temperature during the first 20 seconds after engine cold start. Thermal losses in the exhaust manifold are driven by the heat transfer of the pulsating and turbulent exhaust flow and affect significantly the warm-up time of the catalyst. In the present paper an investigation concerning the gas-side heat transfer in the exhaust system of a spark ignited (SI) combustion engine with retarded ignition timing and secondary air injection into the exhaust port is reported. Based on this analysis, the warm-up simulation of a one-dimensional flow simulation tool is improved for an evaluation of different exhaust system configurations.

The new inline six cylinder Twin-Turbo gasoline engine forms the pinnacle of BMW's wide range of straight-six power units, developing maximum output of 300hp and a peak torque of 300 lb-ft with a displacement of 3.0 litre. Using two turbochargers in combination with the new BMW High Precision Fuel Injection leads to a responsive build-up of torque and to an impressive development of power over a wide engine speed range. This paper gives a detailed overview of the turbocharger-and the injection system and describes the effect of both systems on power and torque, as well as on fuel consumption and emission. The big advantage of using two small turbochargers is their low moment of inertia, even the slightest movement of the accelerator pedal by the driver's foot serving to immediately build up superior pressure and power. This puts an end to the turbo “gap” previously typical of a turbocharged power unit.