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The BMW Group has introduced electric cars to the market with the MINI E already in 2009. The next step will be the launch of the BMW ActiveE in 2011, followed by the revolutionary Mega City Vehicle in 2013. The presentation will explain the BMW Group strategy for implementing sustainable mobility. A focus will be emobility, the use of carbon fiber and the holistic sustainability approach of BMW Group?s project i. Reference will be made to the research results of the MINI E projects in the US and in Europe. Presenter Andreas Klugescheid, BMW AG

The introduction of alternative fuels is crucial to limit greenhouse gases. CNG is regarded as one of the most promising clean fuels given its worldwide availability, its low price and its intrinsic properties (high knocking resistance, low carbon content...). One way to optimize dedicated natural gas engines is to improve the CNG slow burning velocity compared to gasoline fuel and allow lean burn combustion mode. Besides optimization of the combustion chamber design, hydrogen addition to CNG is a promising solution to boost the combustion thanks to its fast burning rate, its wide flammability limits and its low quenching gap. This paper presents an investigation of different methane/hydrogen blends between 0% and 40 vol. % hydrogen ratio for three different combustion modes: stoichiometric, lean-burn and stoichiometric with EGR.

This paper presents an innovative methodology and the corresponding results of a study whose goal is to identify the main links between in-cylinder charge motion and the development of combustion without taking into consideration how to create this charge motion (shape of the intake ducts, valve timing, etc …). During this study a specific methodology was developed and used. It is based on the calculation of a “3D numerical test bench” matrix planned following the Design Of Experiments method. Many aerodynamic configurations obtained by combining the three main aerodynamic motions with several different intensities (tumble, cross-tumble or swirl) at the intake valve closing were calculated.

In order to address the CO₂ emissions issue and to diversify the energy for transportation, CNG (Compressed Natural Gas) is considered as one of the most promising alternative fuels given its high octane number. However, gaseous injection decreases volumetric efficiency, impacting directly the maximal torque through a reduction of the cylinder fill-up. To overcome this drawback, both independent natural gas and gasoline indirect injection systems with dedicated engine control were fitted on a RENAULT 2.0L turbocharged SI (Spark Ignition) engine and were adapted for simultaneous operation. The main objective of this innovative combination of gas and liquid fuel injections is to increase the volumetric efficiency without losing the high knocking resistance of methane.

This study deals with a coupled experimental and modeling approach of Diesel Particulate Filter cracking. A coupled model (heat transfer, mass transfer, chemical reactions) is used to predict the temperature field inside the filter during the regeneration steps. This model consists of assembled 1D models and is calibrated using a set of laboratory bench tests. In this set of experiments, laboratory scale filters are tested in different conditions (variation of the oxygen rate and gas flow) and axial/radial thermal gradient are recorded with the use of thermocouples. This model is used to build a second set of laboratory bench tests, which is dedicated to the understanding of the phenomena of Diesel Particulate Filter cracking.

The target of substantial CO₂ reductions in the spirit of the Kyoto Protocol as well as higher engine efficiency requirements has increased research efforts into hybridization of passenger cars. In the frame of this hybridization, there is a real need to develop small Internal Combustion Engines (ICE) with high power density. The two-stroke cycle can be a solution to reach these goals, allowing reductions of engine displacement, size and weight while maintaining good NVH, power and consumption levels. Reducing the number of cylinders, could also help reduce engine cost. Taking advantage of a strong interaction between the design office, 0D system simulations and 3D CFD computations, a specific methodology was set up in order to define a first optimized version of a two-stroke uniflow diesel engine. The main geometrical specifications (displacement, architecture) were chosen at the beginning of the study based on a bibliographic pre-study and the power target in terms.

This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

Shock waves have been recently observed in high-pressure diesel sprays. In this paper, three-dimensional numerical simulations of supersonic diesel spray injection have been performed to investigate the underlying dynamics of the induced shock waves and their interactions with the spray. A Volume-of-Fluid based method in the CFD software (CONVERGE) is used to model this multiphase phenomena. An adaptive Mesh Refinement (AMR) scheme is employed to capture the front of the spray and the shock waves with high fidelity. Simulation results are compared to the available experimental observations to validate the numerical procedure. Parametric studies with different injection and ambient conditions are conducted to examine the effect of these factors on the generation of shock waves and their dynamics.

Partially Premixed Combustion (PPC) of fuels in the gasoline octane range has proven its potential to achieve simultaneous reduction in soot and NOX emissions, combined with high indicated efficiencies; while still retaining proper control over combustion phasing with the injection event, contrary to fully premixed strategies. However, gasoline fuels with high octane number as the commonly available for the public provide a challenge to ensure reliable ignition especially in the low load range, while fuel blends with lower octane numbers present problems for extending the ignition delay in the high load range and avoid the onset of knocking-like combustion. Thus, choosing an appropriate fuel and injection strategy is critical to solve these issues, assuring successful PPC operation in the full engine map.

One of the most important safety issues for automotive engineering is to avoid any fire due to the ignition of flammable liquids, which may result from leaks. Fire risk is a combination of hot temperature, fast vaporisation and accumulation of vapor in a cavity. In IC engines, potentially flammable liquids are fuel and oil. To guarantee safety, flammable liquids must not come into contact with hot parts of the engine. Consequently, shields are designed to guide the flow path of possible leakages and to take any flammable liquid out of the hot areas. Simulation is a great help to optimize the shape of the shield by investigating a large number of possible leakages rapidly. Recent breakthroughs in numerical methods make it possible to apply simulations to industrial design concepts. The employed approach is based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) method.

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.

Reducing NOx tailpipe emissions is one of the major challenges when developing automotive Diesel engines which must simultaneously face stricter emission norms and reduce their fuel consumption/CO2 emission. In fact, the engine control system has to manage at the same time the multiple advanced combustion technologies such as high EGR rates, new injection strategies, complex after-treatment devices and sophisticated turbocharging systems implemented in recent diesel engines. In order to limit both the cost and duration of engine control system development, a virtual engine simulator has been developed in the last few years. The platform of this simulator is based on a 0D/1D approach, chosen for its low computational time. The existing simulation tools lead to satisfactory results concerning the combustion phase as well as the air supply system. In this context, the current paper describes the development of a new NOx emission model which is coupled with the combustion model.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

This work involves modeling internal and near-nozzle flows of a gasoline direct injection (GDI) nozzle. The Engine Combustion Network (ECN) Spray G condition has been considered for these simulations using the nominal geometry of the Spray G injector. First, best practices for numerical simulation of the two-phase flow evolution inside and the near-nozzle regions of the Spray G injector are presented for the peak needle lift. The mass flow rate prediction for peak needle lift was in reasonable agreement with experimental data available in the ECN database. Liquid plume targeting angle and liquid penetration estimates showed promising agreement with experimental observations. The capability to assess the influence of different thermodynamic conditions on the two-phase flow nature was established by predicting non-flashing and flashing phenomena.

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 touches on the mass transport phenomenon in the exhaust gas recirculation (EGR) of a gasoline engine air path. It presents the control-oriented model and control design of the burned gas ratio (BGR) transport phenomenon, witnessed in the intake path of an internal combustion engine (ICE), due to the redirection of burned gases to the intake path by the low-pressure EGR (LP-EGR). Based on a nonlinear AMESim® model of the engine, the BGR in the intake manifold is modeled as a state-space (SS) output time-delay model, or alternatively as an ODE-PDE coupled system, that take into account the time delay between the moment at which the combusted gases leave the exhaust manifold and that at which they are readmitted in the intake manifold. In addition to their mass transport delay, the BGRs in the intake path are also subject to state and input inequality constraints.

The Reynolds stress turbulence model (RSM) has been developed to go beyond the Boussinesq hypothesis and to improve turbulence modeling of flows with significant mean streamline curvature and secondary flow. In this paper the RSM in commercial CFD software CONVERGE is tested for its performance and robustness when applying to complex flows. Several validation cases including flow over flat plate, vortex combustor, diesel engine spray and combustion were selected to test the RSM. The swirling flow in vortex combustor, non-reacting but vaporizing ECN Spray A (free jet) and Sandia small bore diesel engine case are used to demonstrate the benefits of the RSM over the widely used RNG k-epsilon model without model tuning. The vortex combustor case shows the RSM can provide good prediction for strong swirling flow. ECN spray A case was used to demonstrate that the RSM can accurately predict the liquid and vapor penetration lengths of a free jet under diesel engine conditions.