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In the process of conducting a reliability analysis of a system, quite often the population of interest is not homogenous; consisting of sub-populations which arise as production operations are adjusted, component suppliers are changed, etc. While these sub-populations are each unique in many ways, they also have much in common. It is also common for data to be available from a variety of different test regimes, e.g. environmental testing and fleet maintenance observations. Hierarchical Bayesian methods provide an organized, objective means of estimating the reliability of the individual systems, the sub-population reliability as well as the reliability of the entire population. This paper provides an introduction to a Bayesian approach that can be extended for more complicated situations.

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.

With the huge improvements made during the last years in the area of integrated safety systems, they are one of the main contributors to the massively rising complexity within automotive systems. However, this enormous complexity stimulates the demand for methodologies supporting the efficient development of such systems, both in terms of cost and development time. Within this work, we propose a co-simulation-based approach for the validation of integrated safety systems. Based on data measurements gained from a test bed, models for the sensors and the distributed safety system are established. They are integrated into a co-simulation environment containing models of the ambience, driving dynamics, and the crash-behavior of the vehicle. Hence, the complete heterogeneous system including all relevant effects and dependencies is modeled within the co-simulation.

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.

Driving cycles are valuable tools for emissions calibration at engine and powertrain test beds. While generic velocity profiles were sufficient in the past, legislative changes and increasing complexity of powertrain and exhaust aftertreatment systems require a new approach: Realistically transient cycles - which include critical driving maneuvers and can be tailored to a specific powertrain configuration - are needed to optimize the emission behavior of the said powertrain. For the generation of realistic velocity profiles, the Markov chain approach has been widely used and described in literature. However, this approach, so far, has only been used to generate cycles that are statistically representative of a large database of real driving trips, which is typically not available during the early stages of development of a new powertrain.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

This paper discusses the treatment of uncertainties corresponding to relatively few samples of random-variable quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse samples it is not practical to have a goal of accurately estimating the underlying variability distribution (probability density function, PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a desired percentage of the actual PDF, say 95% included probability, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the random-variable range corresponding to the desired percentage of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem an interesting and difficult one.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

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.

Raising demands towards lightweight design paired with a loss of originally predominant engine noise pose significant challenges for NVH engineers in the automotive industry. From an aeroacoustic point of view, low frequency buffeting ranks among the most frequently encountered issues. The phenomenon typically arises due to structural transmission of aerodynamic wall pressure fluctuations and/or, as indicated in this work, through rear vent excitation. A possible workflow to simulate structure-excited buffeting contains a strongly coupled vibro-acoustic model for structure and interior cavity excited by a spatial pressure distribution obtained from a CFD simulation. In the case of rear vent buffeting no validated workflow has been published yet. While approaches have been made to simulate the problem for a real-car geometry such attempts suffer from tremendous computation costs, meshing effort and lack of flexibility.

A study of n-dodecane atomization, following the prescribed unseating of the needle tip, is presented for a high-pressure, non-cavitating Bosch Diesel injector (“Spray A”, in the Engine Combustion Network denomination). In the two simulations discussed here, the internal and external multiphase flows are seamlessly calculated across the injection orifice using an interface-capturing approach (for the liquid fuel surface) together with an embedded boundary formulation (for the injector's walls). This setting makes it possible to directly relate the liquid jet spray characteristics (under the assumption of sub-critical flow and with a grid resolution of 3 µm, or 1/30 of the orifice diameter) to the moving internal geometry of the injector. Another novelty is the capability of modeling the compressibility of the liquid and the gas phase while maintaining a sharp interface between the two.

This paper presents and validates a model of fatigue crack propagation in resistance spot welded joints; this model is called the “Structural Stress Model” (SSM). Important features of the SSM are that it is based on the physical evidence of fatigue in spot welded joints and on well-accepted descriptors of fatigue more generally. Furthermore, it is usable by designers evaluating fatigue response of structures containing multiple welds. Underlying assumptions of the SSM are also reviewed.

By using a radio frequency (RF) audio distortion measurement test setup, communication devices can be evaluated for degradation caused by electromagnetic interference (EMI) from active vehicle components. This measurement technique can be used to determine the performance of a radio receiver under a variety of conditions. The test setup consists of making measurements on a baseband audio signal that is sent to the device under test (receiver) via over-the-air RF transmissions. Once a baseline is established, active components on the vehicle can be powered on to determine their contribution to the receiver's degradation. The degradation measured is a result of distortion caused by conducted, radiated, and/or coupled EMI from active components into the receiver's passband.

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.

A general model framework for investigating various injection strategies in compression ignition engines with both mixture and thermal inhomogeneities is presented using an extended representative interactive flamelet model. The equations describing evolution of chemistry are written for a scalar phase space of either one or two dimensions and an approach for modeling multiple injections is given. The combustion model is solved interactively with the turbulent flow field by coupling with a Reynolds-Averaged Navier-Stokes (RANS) solver. The model is applied in the simulation of a split-injection diesel engine and results are compared to experimental data obtained from a single cylinder research engine.

This paper presents a new approach to analyzing and developing low-drag cooling systems. A relation is derived which describes cooling drag by a number of contributions. Interference drag clearly can be identified as one of them. Cooling system parameters can be assigned to different terms of the relation, so that differences due to parameter variations of the individual drag contributions can be estimated. In order to predict the interference-drag dependency on the outlet location and the local outlet mass flow, an extensive study on a standard BMW sedan has been carried out, both experimentally and by CFD. The results show the importance of providing consistent outflow conditions which take into account the outlet location and flow direction, in order to minimize cooling drag.

It is shown in how far modal correction techniques can contribute to reduce the CPU effort in the scope of numerical structural-acoustic investigations. Due to this technique a quasi-online interactive acoustic optimization of a basic system configuration can be achieved. In this context special focus is pointed on structural optimization aspects. Finally, the limits of validity as to the accuracy of the results obtained by the modal correction approach in the case of larger modifications are determined.

The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions.

Despite recent advances towards powertrain electrification as a solution to mitigate pollutant emissions from road transport, synthetic fuels (especially e- fuels) still have a major role to play in applications where electrification will not be viable in short-medium term. Among e-fuels, oxymethylene ethers are getting serious interest within the scientific community and industry. Dimethoxy methane (OME1) is the smaller molecule among this group, which is of special interest due to its low soot formation. However, its application is still limited mainly due to its low lower heating value. In contrast, other fuel alternatives like hydrogenated vegetable oil (HVO) are considered as drop-in solutions thanks to their very similar properties and molecular composition to that of fossil diesel. However, their pollutant emission improvement is limited.

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.