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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.

This paper presents calculations of engine flows by using the Partially-Averaged Navier Stokes (PANS) method (Girimaji [1]; [2]). The PANS is a scale-resolving turbulence computational approach designed to resolve large scale fluctuations and model the remainder with appropriate closures. Depending upon the prescribed cut-off length (filter width) the method adjusts seamlessly from the Reynolds-Averaged Navier-Stokes (RANS) to the Direct Numerical Solution (DNS) of the Navier-Stokes equations. The PANS method was successfully used for many applications but mainly on static geometries, e.g. Basara et al. [3]; [4]. This is due to the calculation of the cut-off control parameter which requires that the resolved kinetic energy is known and this is usually obtained by suitably averaging of the resolved field. Such averaging process is expensive and impractical for engines as it would require averaging per cycles.

Reynolds-averaged Navier-Stokes (RANS) computations of heat transfer involving wall bounded flows at elevated Prandtl numbers typically suffer from a lack of accuracy and/or increased mesh dependency. This can be often attributed to an improper near-wall turbulence modeling and the deficiency of the wall heat transfer models (based on the so called P-functions) that do not properly account for the variation of the turbulent Prandtl number in the wall proximity (y+< 5). As the conductive sub-layer gets significantly thinner than the viscous velocity sub-layer (for Pr >1), treatment of the thermal buffer layer gains importance as well. Various hybrid strategies utilize blending functions dependent on the molecular Prandtl number, which do not necessarily provide a smooth transition from the viscous/conductive sub-layer to the logarithmic region.

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 presented paper deals with a methodology to model cycle-to-cycle variations (CCV) in 0-D/1-D simulation tools. This is achieved by introducing perturbations of combustion model parameters. To enable that, crank angle resolved data of individual cycles (pressure traces) have to be available for a reasonable number of engine cycles. Either experimental data or 3-D CFD results can be applied. In the presented work, experimental data of a single-cylinder research engine were considered while predicted LES 3-D CFD results will be tested in the future. Different engine operating points were selected - both stable ones (low CCV) and unstable ones (high CCV). The proposed methodology consists of two major steps. First, individual cycle data have to be matched with the 0-D/1-D model, i.e., combustion model parameters are varied to achieve the best possible match of pressure traces - an automated optimization approach is applied to achieve that.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

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.

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 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.

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.

Wet clutches drag loss simulation is essentially linked to the clutch friction surface patterns in addition to the main geometry and conditions of the interface (relative speed, separation, inner and outer radius, viscosity and boundary pressures). The clutch patterns promote cooling flow and micro-hydrodynamic effects to aid clutch separation but greatly complicate the simulation of drag loss during separation. These drag losses are important in understanding the system losses as well as finding the most effective clutch cooling strategy. Typical clutch models either only consider simple patterns, such as radial grooves, or require significant simulation efforts to evaluate. Additionally, many simple models require calibration to measurement of the actual clutch they try to model before they provide a useful model.

Aerodynamic properties of a BMW car model taking over a truck model are studied computationally by applying the scale-resolving PANS (Partially-averaged Navier-Stokes) approach. Both vehicles represent down-scaled (1:2.5), geometrically-similar models of realistic vehicle configurations for which on-road measurements have been performed by Schrefl (2008). The operating conditions of the modelled ‘on-road’ overtaking maneuver are determined by applying the dynamic similarity concept in terms of Reynolds number consistency. The simulated vehicle configuration constitutes of a non-moving truck model and a car model moving against the air flow, the velocity of which corresponds to the car velocity.

Two thread forming processes, rolling and cutting, were studied for their effects on fatigue in cast aluminum 319-T7. Material was excised from cylinder blocks and tested in rotating-bending fatigue in the form of unnotched and notched specimens. The notched specimens were prepared by either rolling or cutting to replicate threads in production-intent parts. Cut threads exhibited conventional notch behavior for notch sensitive materials. In contrast, plastic deformation induced by rolling created residual compressive stresses in the notch root and significantly improved fatigue strength to the point that most of the rolled specimens broke outside the notch. Fractographic and metallographic investigation showed that cracks at the root of rolled notches were deflected upon initiation. This lengthened their incubation period, which effectively increased fatigue resistance.

This paper presents calculations of external car aerodynamics by using the finite volume (FV) immersed-boundary method. The FV numerical codes primarily employ Reynolds-Averaged Navier-Stokes (RANS) models. In recent years, and due to possibility to run very large computational meshes, these models are usually used in conjunction with the advanced near-wall models. Moreover, it has been often demonstrated that the accuracy of RANS near-wall models relies on the mesh quality near the wall so by the rule, larger number of wall body-fitted cell-layers are employed. An immersed boundary (IB) method becomes an attractive alternative to the ‘standard’ FV approaches especially when applied to low quality CAD data. In general, the IB method is less investigated and validated for the car aerodynamics, particularly in conjunction with advanced near-wall turbulence models and an adaptive mesh refinement (AMR).

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Global vehicle simulation is already a well-established tool to support the vehicle development process. In contrast to conventional vehicles, electrified powertrains offer an additional challenge to the thermal conditioning: the durability of E-components is not only influenced by temperature peaks but also by the duration and amplitude of temperature swings as well as temperature gradients within the components during their lifetime. Keeping all components always at the preferred lowest temperature level to avoid ageing under any conditions (driving, parking, etc.) will result in very high energy consumption which is in contradiction to the efficiency targets.

In the present work, a scalable simulation methodology is presented that enables the assessment of the impact of SI-engine cycle-to-cycle combustion variations on fuel consumption and hence CO2 emissions on three different levels of modeling depth: in-cylinder, steady-state engine and transient engine and vehicle simulation. On the detailed engine combustion chamber level, a 3D-CFD approach is used to study the impact of the turbulent in-cylinder flow on the cycle-resolved flame propagation characteristics. On engine level, cycle-to-cycle combustion variations are assessed regarding their impact on indicated mean effective pressure, aiming at estimating the possible fuel consumption savings when cyclic variations are minimized. Finally, on the vehicle system level, a combined real-time engine approach with crank-angle resolved cylinder is used to assess the potential fuel consumption savings for different vehicle drivecycle conditions.

The combustion efficiency of large gas engines is limited by knocking combustion. Due to fact that the quality of the fuel gas has a high impact on the self-ignition of the mixture, it is the aim of this work to model the knocking combustion for fuel gases with different composition using detailed chemistry. A cycle-resolved knock simulation of the fast burning cycles was carried out in order to assume realistic temperatures and pressures in the unburned mixture Therefore, an empirical model that predicts the cyclic variations on the basis of turbulent and chemical time scales was derived from measured burn rates and implemented in a 1D simulation model. Based on the simulation of the fast burning engine cycles the self-ignition process of the unburned zone was calculated with a stochastic reactor model and correlated to measurements from the engines test bench. A good agreement of the knock onset could be achieved with this approach.

The aim of this paper is to present the adjoint equations for shape optimization derived from steady incompressible Navier-Stokes (N-S) equations and an objective functional. These adjoint Navier-Stokes equations have a similar form as the N-S equations, while the source terms and the boundary conditions depend on the chosen objective. Additionally, the gradient of the targeted objective with respect to the design variables is calculated. Based on this, a modification of the geometry is computed to arrive at an improved objective value. In order to find out, whether a more sophisticated approach is needed, the adjoint equations are derived by using two different approaches. The first approach is based on the frozen turbulence assumption and the second approach, which is advanced in this paper, is derived from the near wall k − ζ − f turbulence model.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Furthermore the increasing number of vehicle configurations leads to a high variability and degrees of freedom in possible system designs and the control thereof, which can only be handled by a comprehensive tool chain of vehicle system simulation and a generic control system architecture. The required model must comprise all relevant systems of the vehicle (control functionality, cooling system, lubrication system, engine, drive train, HV components etc.). For proper prediction with respect to energy consumption all interactions and interdependencies of those systems have to be taken into consideration, i.e. all energy fluxes (mechanical, hydraulically, electrical, thermal) have to be exchanged among the system boundaries accordingly.

The correct information about legal demands of the On-Board-Diagnostic (OBD) system in a vehicle project is required throughout the entire development process. Usually, the main obstacle in succeeding is to provide the company's expertise of some few experts for all employees who work in OBD related projects. The paper describes the AVL solution for knowledge management and tool supported control system design and calibration: OBD System Development Database. The software enables the user to access the regulatory requirements for a specific application and legislation from past, present and future (proposed rule-making) point of view. Information concerning already available and stored monitoring concepts is linked to the requirements in order to re-use potentially suitable concepts and to enable an efficient knowledge exchange within the company.