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To promote advanced combustion strategies complying with stringent emission regulations of CI engines, computational models have to accurately predict the injector inner flow and cavitation development in the nozzle. This paper describes a coupled 1D/2D/3D modeling technique for the simulation of fuel flow and nozzle cavitation in diesel injection systems. The new technique comprises 1D fuel flow, 2D multi-body dynamics and 3D modeling of nozzle inner flow using a multi-fluid method. The 1D/2D model of the common rail injector is created with AVL software Boost-Hydsim. The computational mesh including the nozzle sac with spray holes is generated with AVL meshing tool Fame. 3D multi-phase calculations are performed with AVL software FIRE. The co-simulation procedure is controlled by Boost-Hydsim. Initially Hydsim performs a standalone 1D simulation until the needle lift reaches a prescribed tolerance (typically 2 to 5 μm).

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

The present work introduces an extended particulate filter model focusing on capabilities to cover catalytic and surface storage reactions and to serve as a virtual multi-functional reactor/separator. The model can be classified as a transient, non-isothermal 1D+1D two-channel model. The applied modeling framework offers the required modeling depth to investigate arbitrary catalytic reaction schemes and it follows the computational requirement of running in real-time. The trade-off between model complexity and computational speed is scalable. The model is validated with the help of an analytically solved reference and the model parametrization is demonstrated by simulating experimentally given temperatures of a heat-up measurement. The detailed 1D+1D model is demonstrated in a concept study comparing the impact of different spatial washcoat distributions.

Today's powertrains are becoming more and more complex due to the increasing number of gear box types requiring gearshift patterns like conventional (equipped with GSI) and automatic-manual transmissions (AT, AMT), double clutch and continuous variable transmissions (DCT, CVT). This increasing variety of gear boxes requires a higher effort for the overall optimization of the powertrain. At the same time, it is necessary to assess the impact of different powertrains and control strategies on CO₂ emissions very early in the development process. The optimization of Gear Shift Patterns (G.S.P.) has to fulfill multiple constraints in terms of objective customers' requirements, like driveability, NVH, performance, emissions and fuel consumption. For these reasons, RENAULT and AVL entered an engineering collaboration in order to develop a dedicated simulation tool: CRUISE GSP.

The Selective Catalytic Reduction (SCR) is a promising approach to meet future legislation regarding the nitric oxide emissions of diesel engines. In automotive applications a liquid urea-water solution (UWS) is injected into the hot exhaust gas. It evaporates and decomposes to ammonia vapor acting as the reducing agent. Significant criteria for an efficient SCR system are a fast mixture preparation of the UWS and a high ammonia uniformity at the SCR catalyst. Multiphase CFD simulation is capable to support the development of this process. However, major challenges are the correct description of the liquid phase behavior and the simulation of the ammonia vapor mixing in the turbulent exhaust gas upstream of the SCR catalyst. This paper presents a systematic study of the impact of the turbulence model and the numerical spatial discretization scheme on the prediction of the turbulent mixing process of the gaseous ammonia.

Long-term reliability is one of the major requirements for the operation of automotive exhaust aftertreatment systems based on selective catalytic reduction (SCR). For an efficient reduction of nitrogen oxides in the SCR catalyst it is desirable that the thermolysis of the injected urea water solution (UWS) is completed within the mixing section of the exhaust system. Urea might undergo a number of secondary reactions leading to the formation of solid deposits on system walls. A deeper understanding of the mechanisms and influence factors is a basic requirement to prevent and predict undesired decomposition products. This paper outlines the mechanisms of UWS transport and deposition on a typical mixing element geometry. The conditions leading to deposit formation were investigated based on optical and temperature measurements in a box with optical access. A good correlation with the deposit location observed at the close-to-series exhaust system was found.

The present work introduces a fully integrated real-time (RT) capable engine and vehicle model. The gas path and drive line are described in the time domain of seconds whereas the reciprocating characteristics of an IC engine are reflected by a crank angle resolved cylinder model. The RT engine model is derived from a high fidelity 1D cycle simulation and gas exchange model to support an efficient and consistent transfer of model data like geometries, heat transfer or combustion. The workflow of model calibration and application is outlined and base ECU functionalities for boost pressure, EGR, smoke and idle speed control are applied for transient engine operation. Steady state results of the RT engine model are compared to experimental data and 1D high fidelity simulations for 19 different engine load points. In addition an NEDC (New European Drive Cycle) is simulated and results are evaluated with data from chassis dynamometer measurements.

Turbocharged diesel engines are popular propulsion systems for automotive applications like trucks or passenger cars because of their high efficiency and advantageous torque characteristic. The high NOx emissions due to their combustion process and missing three-way catalyst are, however, a disadvantage. Consequently, to satisfy future emission legislations, NOx emissions must be reduced. In addition, growing environmental awareness requires reduction of CO2 emissions, respectively, consumption. Hybridization is an effective method to achieve these multiple goals. The general tendency in direction of electrification of the powertrain leads to a diversity of drive concepts. In this context, the study of the entire system is as important as the analysis and evaluation of the interaction of the system components.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values. This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).

Experimental studies have shown that knitted wiremesh mixers reduce the formation of solid deposits and improve ammonia homogenization in automotive SCR systems. However, their implementation in CFD models remains a major challenge due to the complex WM geometry. It was the aim of the current study to investigate droplet WM interaction. Essential processes, such as secondary droplet generation, wall film formation, and heat exchange, were analyzed in detail and a numerical model was set up. A box with heat resisting glass was used to study urea-water solution spray impingement on a WM under a wide range of operating conditions. High speed videography was used to identify the impingement regimes. Infrared thermography was applied to investigate WM cooling. In order to determine the impact of the WM on the spray characteristics, the droplet spectrum was measured both upstream and downstream of the WM using the laser diffraction method.

The present work introduces an innovative mechanistically based 0D spray model which is coupled to a combustion model on the basis of an advanced mixture controlled combustion approach. The model calculates the rate of heat release based on the injection rate profile and the in-cylinder state. The air/fuel distribution in the spray is predicted based on momentum conservation by applying first principles. On the basis of the 2-zone cylinder framework, NOx emissions are calculated by the Zeldovich mechanism. The combustion and emission models are calibrated and validated with a series of dedicated test bed data specifically revealing its capability of describing the impact of variations of EGR, injection timing, and injection pressure. A model based optimization is carried out, aiming at an optimum trade-off between fuel consumption and engine-out emissions. The findings serve to estimate an economic optimum point in the NOx/BSFC trade-off.

Modern Diesel engines require an integrated development of combustion strategies, air management and exhaust aftertreatment. This study presents a comprehensive simulation approach with the aim to support engine development activities in the virtual environment. A real-time capable engine, vehicle and control model is extended by three key features. First, a pollutant production model is embedded in a two-zone cylinder model. Second, a framework for catalytic pollutant conversion is built focusing on modern diesel exhaust aftertreatment systems. Third, an extended species transport model is introduced considering the transport of pollutants through the air path. The entire plant model is validated on the example of a passenger car Diesel engine. The predicted engine behavior is compared with steady-state measurements. The NO formation model is investigated for a series of steady-state and transient operating conditions.

A calibration and validation workflow will be presented in this paper, which utilizes common static global models for fuel consumption, NOx and soot. Due to the applicability for warm-up tests, e. g. New European Driving Cycle (NEDC), the models need to predict the temperature influence and will be fitted with measuring data from a conditioned engine test bed. The applied model structure - consisting of a number of global data-based sub-models - is configured especially for the requirements of multi-injection strategies of common rail systems. Additionally common global models for several constant coolant water temperature levels are generated and the workflow tool supports the combination and segmentation of global nominal map with temperature correction maps for seamless and direct ECU setting.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

Engine simulation can be performed using model approaches of different depths in capturing physical effects. The present paper presents a comprehensive comparison study on seven different engine models. The models range from transient 1D cycle resolved approaches to steady-state non-dimensional maps. The models are discussed in the light of key features, amount and kind of required input data, model calibration effort and predictability and application areas. The computational performance of the different models and their capabilities to capture different transient effects is investigated together with a vehicle model under real-life driving conditions. In the trade-off field of model predictability and computational performance an innovative approach on crank-angle resolved cylinder modeling turned out to be most beneficial.

In this paper, the multi-physical simulation workflow from electromagnetics to structural dynamics for a squirrel-cage induction machine is explored. In electromagnetic simulations, local forces and rotor torque are calculated for specific speed-torque operation points. In order to consider non-linearities and interaction with control system as well as transmission, time-domain simulations are carried out. For induction machines, the computational effort with full transient numerical methods like finite element analysis (FEA) is very high. A novel reduced order electro-mechanical model is presented. It still accounts for vibro-acoustically relevant harmonics due to pulse-width modulation (PWM), slotting, distributed winding and saturation effects, but is substantially faster (minutes to hours instead of days to weeks per operation point).

In exhaust systems with selective catalytic reduction (SCR) a fast conversion of liquid urea to gaseous ammonia and a uniform distribution of the ammonia vapor upstream of the SCR catalyst are essential to reduce the nitric oxides efficiently. For the prediction of the mixing process and the transport of ammonia vapor with the CFD method an accurate description of the turbulent flow field is a basic requirement. This paper presents the comparison of simulation results using three different turbulence models (high-Re kε-RNG model, low-Re kω-SST model, Reynolds stress model) with measurements of the turbulent velocity field using Laser Doppler Anemometry (LDA). The investigations were carried out for a SCR system with a swirl mixer on a cold flow test bench for two different volume flows. From the measured velocity signals different components of the Reynolds-tensor were derived.

For nearly twenty years, DiMethyl Ether has been known to be an outstanding fuel for combustion in diesel cycle engines. Not only does it have a high Cetane number, it burns absolutely soot free and produces lower NOx exhaust emissions than the equivalent diesel. However, the physical properties of DME such as its low viscosity, lubricity and bulk modulus have negative effects for the fuel injection system, which have both limited the achievable injection pressures to about 500 bar and DME's introduction into the market. To overcome some of these effects, a common rail fuel injection system was adapted to operate with DME and produce injection pressures of up to 1000 bar. To understand the effect of the high injection pressure, tests were carried out using 2D optically accessed nozzles. This allowed the impact of the high vapour pressure of DME on the onset of cavitation in the nozzle hole to be assessed and improve the flow characteristics.

The demand for high efficiency powertrains in automotive engineering is further increasing, with hybrid powertrains being a feasible option to cope with new legislations. So far hybridization has only played a minor role for L-category vehicles. Focusing on an exemplary high-power L-category on-road vehicle, this research aims to show a new development approach, which combines longitudinal dynamic simulation (LDS) with “Design of Experiments” (DoE) in course of hybrid electric powertrain development. Furthermore, addressing the technological aspect, this paper points out how such a vehicle can benefit from 48V-hybridization of its already existing internal combustion powertrain. A fully parametric LDS model is built in Matlab/Simulink, with exchangeable powertrain components and an adaptable hybrid operation strategy. Beforehand, characterizing decisions as to focus on 48V and on parallel hybrid architecture are made.