Search Results

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.

Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations.

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.

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 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.

Among the issues affecting the design process of a vehicle, there is the lack of multidisciplinary knowledge among the different teams involved. This often leads to the risk of loosing important key points from the initial concept idea of designers to the final vehicle package definition made by engineers. Therefore, this study builds up a method based on parameters defining exterior aesthetic priorities according to car segment to support engineers involved in the automotive design process. In particular, during the early design-engineering phase, this method should help them to understand better vehicle proportions defined by designers. This work is currently used in the course of design and simulation of road vehicles (Chair of Automotive Technology, TUM) to explain fundamentals of automotive design.

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.

Aerodynamic design and thermal management are some of the most important tasks when developing new concepts for the flow around tractor-trailers. Today, both experimental and numerical studies are an integral part of the aerodynamic and thermal design processes. A variety of studies have been conducted how the aerodynamic design reduces the drag coefficient for fuel efficiency as well as for the construction of radiators to provide cooling on tractor-trailers. However, only a few studies cover the combined effect of the aerodynamic and thermal design on the air temperature of the under hood flow [8, 13, 16, 17, 20]. The objective of this study is to analyze the heat transfer through forced convection for a scaled Cab-over-Engine (CoE) tractor-trailer model with under hood flow. Different design concepts are compared to provide low under hood air temperature and efficient cooling of the sub components.

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.

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.

Software-intensive systems and functions drive innovations in cars today. OEMs and suppliers face multiple challenges to take advantage of possibilities in this area. The rapidly developing field of software-intensive systems and software-based features in the automotive domain asks for dedicated engineering approaches, models, and processes. This paper defines the characteristics of software engineering for automotive systems and discusses methodological, technological, and organizational implications. These are used to pinpoint promising research areas as well as educational ramifications.

In order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.