Search Results

The thermal operational safety (TOS) of a vehicle ensures that no component exceeds its critical temperature during vehicle operation. To enhance the current TOS validation process, a data-driven approach is proposed to predict maximum component temperatures of a new vehicle project by leveraging the historical thermal wind tunnel data from previous vehicle projects. The approach intends to support engineers with temperature predictions in the early phase and reduce the number of wind tunnel tests in the late phase of the TOS validation process. In the early phase, all measurements of the new vehicle project are predicted. In the late phase, a percentage of measurements with the test vehicle used for the model training and the remaining tests are predicted with the trained ML model. In a first step, data from all wind tunnel tests is extracted into a joint dataset together with metadata about the vehicle and the executed load case.

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.

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.

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.

Efforts in aerodynamic optimization of road vehicles have been steadily increasing in recent years, mainly focusing on the reduction of aerodynamic drag. Of a car's total drag, wheels and wheel houses account for approx. 25 percent. Consequently, the flow around automotive wheels has lately been investigated intensively. Previously, the authors studied a treaded, deformable, isolated full-scale tire rotating in contact with the ground in the wind tunnel and using the Lattice-Boltzmann solver Exa PowerFLOW. It was shown that applying a common numerical setup, with velocity boundary condition prescribed on the tread, significant errors were introduced in the simulation. The contact patch separation was exaggerated and the flow field from wind tunnel measurements could not be reproduced. This investigation carries on the work by examining sensitivities and new approaches in the setup.

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.

At the rear of the vehicle an end acoustic silencer is attached to the exhaust system. This is primarily to reduce noise emissions for the benefit of passengers and bystanders. Due to the location of the end acoustic silencer conventional thermal protection methods (heat shields) through experimental means can not only be difficult to incorporate but also can be an inefficient and costly experience. Hence simulation methods may improve the development process by introducing methods of optimization in early phase vehicle design. A previous publication (Part 1) described a methodology of improving the surface temperatures prediction of general exhaust configurations. It was found in this initial study that simulation results for silencer configurations exhibited significant discrepancies in comparison to experimental data.

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.

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.

Unsteady aerodynamic flow phenomena are investigated in the wind tunnel by oscillating a realistic 50% scale model around its vertical axis. Thus the model is exposed to time-dependent flow conditions at realistic Reynolds and Strouhal numbers. Using this setup unsteady aerodynamic loads are observed to differ significantly from quasi-steady loads. In particular, the unsteady yaw moment exceeds the quasi-steady approximation by 80%. On the other hand, side force and roll moment are over predicted by quasi-steady approximation but exhibit a significant time delay. Using hotwire anemometry, a delayed reaction of the wake flow of Δt/T = 0.15 is observed, which is thought to be the principal cause for the differences between unsteady and quasi-steady aerodynamic loads. A schematic mechanism explaining these differences due to the delayed reaction of the wake flow is proposed.

Unsteady aerodynamic flow phenomena are investigated in a wind tunnel by oscillating a realistic 50% scale model around the vertical axis. Thus the model is exposed to time-dependent flow conditions at realistic Reynolds and Strouhal numbers. Using this setup unsteady aerodynamic loads are observed to differ significantly from quasi steady loads. In particular, the unsteady yaw moment exceeds the quasi steady approximation significantly. On the other hand, side force and roll moment are over predicted by quasi steady approximation but exhibit a significant time delay. Part 2 of this study proves that a delayed and enhanced response of the surface pressures at the rear side of the vehicle is responsible for the differences between unsteady and quasi steady loads. The pressure changes at the vehicle front, however, are shown to have similar amplitudes and almost no phase shift compared to quasi steady flow conditions.

Evaluation of safety benefits is an essential task during design and development of pedestrian protection systems. Comparative evaluation of different safety concepts is facilitated by a common metric taking into account the expected human benefits. Translation of physical characteristics of a collision, such as impact speed, into human benefits requires reliable and preferably evidence-based injury models. To this end, the dependence of injury severity of body regions on explanatory factors is quantified here using the US Pedestrian Crash Data Study (PCDS) for pedestrians in frontal vehicle collisions. The explanatory and causal factors include vehicle component characteristics, physiological and biomechanical variables, and crash parameters. Severe to serious injuries most often involve the head, thorax and lower extremities.

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.

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.

The trend in the previous years showed that an ideal product is not obtained as a sum of development results of several separated disciplines but rather as a result of a holistic multidisciplinary CAE approach. In the course of the whole component development process it is necessary to consider all functions of an individual component equivalent to their importance in the system as a whole, in order to achieve both a technical and a financial optimum. The predictability and the accuracy of an individual computational method have to be regarded against the background of the entire simulation process. A continuative CAE-standard and a harmonious interaction between the different computational disciplines promise more success than focusing specifically on individual topics and thereby neglecting the “bigger picture”. This awareness provided the basis for a decision to change the entire crash simulation software to ABAQUS.

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.