Search Results

Multicore processor are well established in classical and tablet personal computers for some year. Such processors use more then one central core for computation and allow to integrate more computational power with smaller costs. However more than 90% of all processors worldwide are not placed in classical IT but are empedded in bigger systems like in modern vehicles or airplanes. Such systems face a very high demand in terms of safety, security an reliability which hinders the use of multicores in such systems. The funded project ARAMiS faces these demands and has the goal to enable the usability of multicore systems in the domains automotive and avionics, as well as later also railway. ARAMiS is the basis for higher traffic safety, traffic efficiency and comfort.

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.

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.

Today, body vibration energy of passenger cars gets dissipated by linear working shock absorbers. A new approach substitutes the damper of a passenger car by a cardanic gimbaled flywheel mass. The constructive design leads to a rotary damper in which the vertical movement of the wheel carrier leads to revolution of the rotational axis of the flywheel. In this arrangement, the occurring precession moments are used to control damping moments and to store vibrational energy. Different damper characteristics are achieved by different induced precession. From almost zero torque output to high torque output, this damper has a huge spread. Next to the basic principal, in this paper an integration in the chassis, including a constructive proposal is shown. A conflict with high torque and high angular velocity leads to a special design. Moreover concepts to deal with all vehicle situations like yawing, rolling and pitching are shown.

In ride comfort as well as driving dynamics, the behavior of the vehicle is affected by several subsystems and their properties. When analyzing the suspension, especially the characteristics of the main spring and damper but also rubber bushings are of main importance. Still, the properties of the different components are dependent on the present operating conditions. Concerning rubber bushings, several effects have already been investigated, e.g. dependencies of the transfer function of frequency, amplitude or load history. In this context influences of changes in temperature are often neglected. However, in the following research, the focus specifically lies on determination and analysis of the temperature dependency of rubber bushings. For this purpose, initially the relationship between properties of pure rubber and rubber bushings is described, which serves as a basis for correlating respective temperature dependencies.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

This paper proposes end-to-end protection mechanisms to be added to a generic FlexRay network in order to achieve fault detection and integrity levels sufficient for a SIL3 fail safe communication system. The mechanisms are derived from the random hardware failure modes to be considered for communication controllers according to IEC 61508. Mechanisms provided by the FlexRay protocol are pointed out. Additional features necessary to fulfil the requirements are discussed. It is shown how to calculate the failure rate probabilities of the CRC used as a safety code with respect to EN 50159.

Helmholtz-resonators are discussed in technical acoustics normally in conjunction with attenuation of sound, not with amplification or even production of sound. On the other hand everybody knows the sound produced by a bottle, when someone blows over the orifice. During the investigation of the sound produced in body gaps it was found that the underlying flow physics are closely related to the Helmholtz-resonator. But different from the typical Helmholtz-resonator generated noise – as for example the blown bottle or, from the automotive world, the sun roof buffeting – there is no fluid resonance involved in the process. For body gaps the random pressure fluctuation of the turbulent boundary layer is sufficient to excite the acoustic resonance in the cavity. The sound generation is characterized by a continuous rise in sound pressure level with increasing velocity, the rise is proportional to U with varying exponents.

In order to suppress the well-documented low frequency pressure fluctuations in open jet wind tunnels, termed ‘wind tunnel buffeting’, an Active Resonance Control (ARC) System was implemented in the Audi aero-acoustic wind tunnel several years ago. This ARC-Sys-tem reduces the periodic pressure fluctuations by up to 23 dB and completely eliminates the periodic velocity fluctuations using a simple feedback control scheme. To set up the ARC system in practice, the system's parameters are optimised once for each critical flow velocity, when the vortex shedding frequency coincides with an acoustic resonance mode of the wind tunnel. Due to the fact that both frequency and amplitude of the excited resonances not only depend on flow velocity but also on other parameters such as collector position and test-car geometry, the system has to be adjusted with regard to each of these cases.

Optimization of ride comfort is becoming increasingly important in chassis development. Constantly rising traffic density and comfort-orientated customer preferences are mainly responsible for this. Comfort and its improvement are important, not only on bad road surfaces, but also on even surfaces. The complexity of analysis leads to a strong link between car testing and simulation. The testing itself is divided in to roadtests and test stands. It is of outstanding importance to analyze the vehicles vibrational behavior from road tests as it is a real life situation. In order to get meaningful results from the roadtest the vehicle has to be seen as a complete vibrational system. The vibrational behavior of a system is clearly defined by input and output of the system. Road tests are chosen in relation to the predicted car. The roads surface is the input of the system exciting the vibrational subsystems of a car. The ride tests are used for the evaluation of drive response behavior.

The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.

A joint study was conducted between BMW and Goodyear with the objective of analysing the cause and identifying methods to reduce the structure-borne interior noise in a vehicle driving on rough road surfaces. A vibro-acoustic characterization of the car was performed by measuring the car vibro-acoustic transfer functions and by using a transfer path analysis technique to identify the main suspension parts affecting the interior noise at target frequencies. The vibration transmissibility characteristics of the tire were measured and also simulated by Finite Element in [1-200Hz] frequency range. The vibro-acoustic interaction between the tire and car sub-systems was examined. A Finite Element sensitivity analysis was used to define and build new prototype tires. A 3dB(A) interior noise improvement was obtained with these new tires at target frequencies.

In this contribution functional safety is discussed from a car manufacturer's point of view. Typical elements of a safety standard concerning safety activities during the product development process are described as well as management and other supporting processes. Emphasis is laid on the aspect of risk assessment and the determination of safety classes. Experiences with methods for safety analysis like FTA or FMEA are discussed and pros and cons of quantitative safety assessment are argued.

In the early stages of conceptual design the available geometric data are very coarse and the lifespan of a design idea is very short. The structural evaluation and improvement of a design has to take both facts into account. Its focus is on the total vehicle and its performance. This can be estimated by a modeling technique, which is adequate for the lack of geometric details. Static and dynamic global stiffness as well as some aspects of crash and NVH have to be considered. Optimization will lead to the proper sizing and some indication of the potential of the structure. In order to maintain high quality standards this approach has to be supported by specialized CAE tools and extensive rules on modeling techniques and analysis procedures.

With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

Driven by the growing internet and remote connectivity of automobiles, combined with the emerging trend to automated driving, the importance of security for automotive systems is massively increasing. Although cyber security is a common part of daily routines in the traditional IT domain, necessary security mechanisms are not yet widely applied in the vehicles. At first glance, this may not appear to be a problem as there are lots of solutions from other domains, which potentially could be re-used. But substantial differences compared to an automotive environment have to be taken into account, drastically reducing the possibilities for simple reuse. Our contribution is to address automotive electronics engineers who are confronted with security requirements. Therefore, it will firstly provide some basic knowledge about IT security and subsequently present a selection of automotive specific security use cases.

Over the past decades, interior noise from wind noise or engine noise have been significantly reduced by leveraging improvements of both the overall vehicle design and of sound package. Consequently, noise sources originating from HVAC systems (Heat Ventilation and Air Conditioning), fans or exhaust systems are becoming more relevant for perceived quality and passenger comfort. This study focuses on HVAC systems and discusses a Flow-Induced Noise Detection Contributions (FIND Contributions) numerical method enabling the identification of the flow-induced noise sources inside and around HVAC systems. This methodology is based on the post-processing of unsteady flow results obtained using Lattice Boltzmann based Method (LBM) Computational Fluid Dynamics (CFD) simulations combined with LBM-simulated Acoustic Transfer Functions (ATF) between the position of the sources inside the system and the passenger’s ears.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.