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There is a growing need for low-emissions concepts due to stricter emission regulations, more stringent homologation cycles, and the possibility of a ban on new engines by 2035. Of particular concern are the conditions during a cold start, when the Three-Way Catalyst is not yet heated to its light-off temperature. During this period, the catalyst remains inactive, thereby failing to convert pollutants. Reducing the time needed to reach this temperature is crucial to comply with the more stringent emissions standards. The post oxidation by means of secondary air injection, illustrated in this work, is a possible solution to reduce the time needed to reach the above-mentioned temperature. The strategy consists of injecting air into the exhaust manifold via secondary air injectors to oxidize unburned fuel that comes from a rich combustion within the cylinder.

Test of autonomous systems is mostly brute force and ad-hoc thus being neither efficient nor transparent. Though requirements invite for a situational transparency, a framework is missing to judge quality of requirements and derived test-cases. Practical challenges are state explosion, difficulty to derive corner cases, no systematic safety of the intended functionality as specified, lack of accepted KPI, etc. Maintaining a valid safety case is hardly possible with such adaptive systems and continuous software updates. To achieve trusted autonomous vehicles, test cases must be generated automatically while at same time providing coverage (e.g., indicating progress with KPI), efficiency (e.g., limiting the amount of regression testing) and transparency (e.g., showing how specific corner cases are tested in case of accidents). This paper provides a method for automatically generating test cases for AI-based autonomous systems and compares it with existing testing methods.

Urban air mobility (UAM) is a fast-growing industry that utilizes electric vertical take-off and landing (eVTOL) technologies to operate in densely populated urban areas with limited space. However, atmospheric icing serves as a limitation to its operational envelope as in-flight icing can happen all year round anywhere around the globe. Since icing in smaller aviation systems is still an emerging topic, there is a necessity to study icing of eVTOL rotors specifically. Two rotor geometries were chosen for this study. A small 15-inch rotor was selected to illustrate a multirotor UAV drone, while a large 80-inch rotor was chosen to represent a UAM passenger aircraft. The ice accretion experiments were conducted in an icing wind tunnel on the small 15-inch rotor. The icing simulations were performed using FENSAP-ICE. The ice accretion simulations of the 15-inch rotor sections at –5 °C show a large, rather streamlined ice shape instead of the expected glaze ice characteristics.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

Air conditioning acoustics have become of paramount importance in electric vehicles, where noise from electromechanical components is no longer masked by the presence of the internal combustion engine. In a car HVAC systems, the coolant compressor is one of the most important sources in terms of vibration and noise generation. The paper, the generated structural noise is studied in detail on a prototype installation, and the noise transmission and propagation mechanisms are analyzed and discussed. Through ”in situ” measurements and virtual point transformation, the rotor unbalance forces and torque acting within the component are identified. The dynamic properties of the rubber mounts, installed between the compressor and its support, are identified thanks to matrix inversion methods. To assess the quality of the proposed procedure, the synthesized sound pressure level is compared with experimental SPL measurements in different operational conditions.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

Knocking is one of today’s main limitations in the ongoing efforts to increase efficiency and reduce emissions of spark-ignition engines. Especially for synthetic fuels or any alternative fuel type in general with a much steeper increase of the knock frequency at the KLSA, such as hydrogen, precise knock prediction is crucial to exploit their full potential. This paper therefore proposes a post-processing tool enabling further investigations to continuously gain better understanding of the knocking phenomenon. In this context, evaluation of local auto-ignitions preceding knock is crucial to improve knowledge about the stochastic occurrence of knock but also identify critical engine design to further optimize the geometry. In contrast to 0D simulations, 3D CFD simulations provide the possibility to investigate local parameters in the cylinder during the combustion.

Predictive physical modeling is an established method used in the development process for automotive components and systems. While accurate predictions can be issued after tuning model parameters, long computation times are expected depending on the complexity of the model. As requirements for components and systems continuously increase, new optimization approaches are constantly being applied to solve multidimensional objectives and resulting conflicts optimally. Some of those approaches are deemed not feasible, as the computational times for required single predictions using conventional simulation models are too high. To address this issue it is proposed to use data-driven model such as neural networks. Previous efforts have failed due to sparse data sets and resulting poor predictive ability. This paper introduces an AI Toolchain used for data-driven modeling of combustion engine components. Two methods for generating scalable and fully variable datasets will be shown.

For the NOx removal from diesel exhaust, the selective catalytic reduction (SCR) and lean NOx traps are established technologies. However, these procedures lack efficiency below 200 °C, which is of importance for city driving and cold start phases. Thus, the present paper deals with the development of a novel low-temperature deNOx strategy implying the catalytic NOx reduction by hydrogen. For the investigations, a highly active H2-deNOx catalyst, originally engineered for lean H2 combustion engines, was employed. This Pt-based catalyst reached peak NOx conversion of 95 % in synthetic diesel exhaust with N2 selectivities up to 80 %. Additionally, driving cycle tests on a diesel engine test bench were also performed to evaluate the H2-deNOx performance under practical conditions. For this purpose, a diesel oxidation catalyst, a diesel particulate filter and a H2 injection nozzle with mixing unit were placed upstream to the full size H2-deNOx catalyst.

It is particularly easy to get tunnel vision as a domain expert, and focus only on the improvements one could provide in their area of expertise. To make matters worse, many Original Equipment Manufacturers (OEMs) are silo-ed by domain of expertise, unconsciously promoting this single mindedness in design. Unfortunately, the successful and profitable development of a vehicle is dependent on the delicate balance of performance across many domains, involving multiple physics and departments. Taking for instance the design of a Heating, Ventilation & Air Conditioning (HVAC) system, the device’s primary function is to control the climate system in vehicle cabins, and more importantly to make sure that critical areas on the windshield can be defrosted in cold weather conditions within regulation time. With the advent of electric and autonomous vehicles, further importance is now also placed on the energy efficiency of the HVAC, and its noise.

In this paper, the differences between a production car of the 2018 A-class and an early stage vehicle model with a mostly similar outer skin are examined experimentally and numerically. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models, as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as a motor compartment with a cooling module, deflecting axles with rotatable wheels and underbody covers are represented.

The automotive industry uses supercharging in combination with various EGR strategies to meet the increasing demand for Diesel engines with high efficiency and low engine emissions. The charge air is heated by the EGR and the compression in the turbocharger to such an extent that high NOx emissions and a reduction in engine performance occurs. For this reason, the charge air cooler cools down the charge air before it enters the air intake manifold. In case of low pressure EGR, the charge air possesses a high moisture content and under certain operating conditions an accumulation of condensate takes place within the charge air cooler. During demanding engine loads, the condensate is entrained from the charge air cooler into the combustion chamber, resulting in misfiring or severe engine damage.

This paper investigates the pressure drop with and without condensation inside a charge air cooler. The background to this investigation is the fact that the stored condensate in charge air coolers can be torn into the combustion chamber during different driving states. This may result in misfiring or in the worst-case lead to an engine failure. In order to prevent or reduce the accumulated condensate inside charge air coolers, a better understanding of the detailed physics of this process is required. To this end, one single channel of the charge air side is investigated in detail by using an experimental setup that was built to reproduce the operating conditions leading to condensation. First, measurements of the pressure drop without condensation are conducted and a good agreement with experimental data of a comparable heat exchanger reported in Kays and London [1] is shown.

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.

Further improvements of internal combustion engines to reduce fuel consumption and to face future legislation constraints are strictly related to the study of mixture formation. The reason for that is the desire to supply the engine with homogeneous charge, towards the direction of a global stoichiometric blend in the combustion chamber. Fuel evaporation and thus mixture quality mostly depend on injector atomization features and charge motion within the cylinder. 3D-CFD simulations offer great potential to study not only injector atomization quality but also the evaporation behavior. Nevertheless coupling optical measurements and simulations for injector analysis is an open discussion because of the large number of influencing parameters and interactions affecting the fuel injection’s reproducibility. For this purpose, detailed numerical investigations are used to describe the injection phenomena.

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.

The industry is working intensively on the precision of thermal management. By using complex thermal management strategies, it is possible to make engine heat distribution more accurate and dynamic, thereby increasing efficiency. Significant efforts are made to improve the cooling efficiency of the engine water jacket by using 3D CFD. As well, 1D simulation plays a significant role in the design and analysis of the cooling system, especially for considering transient behaviour of the engine. In this work, a practice-oriented universal method for creating a 1D water jacket model is presented. The focus is on the discretization strategy of 3D geometry and the calculation of heat transfer using Nusselt correlations. The basis and reference are 3D CFD simulations of the water jacket. Guidelines for the water jacket discretization are proposed. The heat transfer calculation in the 1D-templates is based on Nusselt-correlations (Nu = Nu(Re, Pr)), which are derived from 3D CFD simulations.

For the gasoline engine, the isochoric process is the ideal limit of the ideal processes. During the project, a combustion engine with real isochoric boundary conditions is built. A “resting time” of the piston for several degrees crank angle in the top dead center (TDC) can be realized with a special crank drive. This crank drive consists of two crankshafts with different strokes, which are combined. The two crankshafts rotate with a ratio of two to one in opposite directions. The total stroke corresponds to the amount of the first crankshaft, so it is possible to investigate different strokes of the second crankshaft in the same crankcase. Different “resting times” can be achieved by different strokes of the second crankshaft. A specific combination of both crankshafts make a stroke possible which corresponds to that of a conventional combustion engine.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.