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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.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

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.

High pressure EGR provides NOx emission reduction even at low exhaust temperatures. To maintain a safe EGR system operation over a required lifetime, the EGR cooler fouling must not exceed an allowable level, even if the engine is operated under worst-case conditions. A reliable fouling simulation model represents a valuable tool in the engine development process, which validates operating and calibration strategies regarding fouling tendency, helping to avoid fouling issues in a late development phase close to series production. Long-chained hydrocarbons in the exhaust gas essentially impact the fouling layer formation. Therefore, a simulation model requires reliable input data especially regarding mass flow of long-chained hydrocarbons transported into the cooler. There is a huge number of different hydrocarbon species in the exhaust gas, but their individual concentration typically is very low, close to the detection limit of standard in-situ measurement equipment like GC-MS.

Conventional methods of physicochemical models require various experts and a high measurement demand to achieve the required model accuracy. With an additional request for faster development time for diagnostic algorithms, this method has reached the limits of economic feasibility. Machine learning algorithms are getting more popular in order to achieve a high model accuracy with an appropriate economical effort and allow to describe complex problems using statistical methods. An important point is the independence from other modelled variables and the exclusive use of sensor data and actuator settings. The concept has already been successfully proven in the field of modelling for exhaust gas aftertreatment sensors. An engine-out nitrogen oxide (NOX) emission sensor model based on polynomial regression was developed, trained, and transferred onto a conventional automotive electronic control unit (ECU) and also proves real-time capability.

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 development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.

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.

The latest legislative tendencies for on-highway heavy duty vehicles in the United States such as the feasibility assessment of low NOX standards of CARB or EPA’s memorandum forecast further tightening of the NOX emissions limits. In addition, the GHG Phase 2 legislation and also phased-in regulations in the EU enforce a continuous reduction in CO2 emissions resp. fuel consumption. In order to meet such low NOX emission limits, a rapid heat-up of the exhaust after-treatment system (EATS) is inevitable. However, the required thermal management results in increased fuel consumption, i.e. CO2 emissions as shown in numerous previous works also by the authors. A NOX-CO2 trade-off for cumulative cycle emissions can be observed, which can be optimized by using more advance technologies on the engine and/or on the EATS side.

A practical Gasoline Compression Ignition (GCI) concept is presented that works on standard European 95 RON E10 gasoline over the whole speed/load range. A spark is employed to assist the gasoline autoignition at low loads; this avoids the requirement of a complex cam profile to control the local mixture temperature for reliable autoignition. The combustion phasing is controlled by the injection pattern and timing, and a sufficient degree of stratification is needed to control the maximum rate of pressure rise and prevent knock. With active control of the swirl level, the combustion system is found to be relatively robust against variability in charge motion, and subtle differences in fuel reactivity. Results show that the new concept can achieve very low fuel consumption over a significant portion of the speed/load map, equivalent to diesel efficiency. The efficiency is worse than an equivalent diesel engine only at low load where the combustion assistance operates.

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.

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.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

In order to reduce the negative health effects associated with engine pollutants, environmental problems caused by combustion engine emissions and satisfy the current strict emission standards, it is essential to better understand and simulate the emission formation process. Further development of emission model, improves the accuracy of the model-based optimization approach, which is used as a decisive tool for combustion system development and engine-out emission reduction. The numerical approaches for emission simulation are closely coupled to the combustion model. Using a detailed emission model, considering the 3D mixture preparation simulation including, chemical reactions, demands high computational effort. Phenomenological combustion models, used in 1D approaches for model-based system optimization can deliver heat release rate, while using a two-zone approach can estimate the NOx emissions.

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.

Engine valve flow coefficients are used to describe the flow throughput performance of engine valve/port designs, and to model gas exchange in 0D/1D engine simulation. Valve flow coefficients are normally determined at a stationary flow test bench, separately for intake and exhaust side, in the absence of the piston. However, engine operation differs from this setup; i. a. the piston might interact with valve flow around scavenging top dead center, and instead of steady boundary conditions, valve flow is nearly always subjected to pressure pulsations, due to pressure wave reflections within the gas exchange ports. In this work the influences of piston position and flow pulsation on valve flow coefficients are investigated for different SI engine geometries by means of 3D CFD and measurements at an enhanced flow test bench.

In the automotive industry, there is a continued need to improve the development process and handle the increasing complexity of the overall vehicle system. One major step in this process is a comprehensive and complementary approach to both simulation and testing. Knowledge of the overall dynamic vehicle behavior is becoming increasingly important for the development of new control concepts such as integrated vehicle dynamics control aiming to improve handling quality and ride comfort. However, with current well-established test systems, only separated and isolated aspects of vehicle dynamics can be evaluated. To address these challenges and further merge the link between simulation and testing, the Institute of Internal Combustion Engines and Automotive Engineering (IVK), University of Stuttgart is introducing a new Handling Roadway (HRW) Test System in cooperation with The Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS) and MTS Systems Corporation.

The low-NOx standard for heavy-duty trucks proposed by the California Air Resources Board will require rapid warm-up of the aftertreatment system. Several different engine technologies are being considered to meet this need. In this study, a 1-D engine model was first used to evaluate several individual control strategies capable of increasing the exhaust enthalpy and decreasing the engine-out NOX over the initial portion of the cold start FTP cycle. The additional fuel consumption resulting from these strategies was also quantified with the model. Next, several of those strategies were combined to create a hypothetical aftertreatment warm-up mode for the engine. The model was then used to evaluate potential benefits of an air gap manifold (AGM) and two different turbine by-pass architectures. The detailed geometry of the AGM model was taken into account, having been constructed from a real prototype design.

A wide variety of applications such as driver assistant and energy management systems are researched and developed in virtual test environments. The safe testing of the applications in early stages is based on parameterizable and reproducible simulations of different driving scenarios. One possibility is modeling the microscopic driving behavior to simulate the longitudinal vehicle dynamics of individual vehicles. The currently used driver models are characterized by a conflict regarding comprehensibility, accuracy and calibration effort. Due to the importance for further analyses this conflict of interests is addressed by the presentation of a new microscopic driver model in this paper. The proposed driver model stores measured driving behaviors with its statistical distributions in maps. Thereby, the driving task is divided into free flow, braking in front of stops and following vehicles ahead. This makes it possible to display the driving behavior in its entirety.