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An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

Correct simulations of rotating wheels are essential for accurate aerodynamic investigations of passenger vehicles. Therefore, modern automotive wind tunnels are equipped with five-belt moving ground systems with wheel drive units (WDUs) connected to the underfloor balance. The pressure distribution on the exposed areas of the WDU belts results in undesired lift forces being measured which must be considered to obtain accurate lift values for the vehicle. This work investigates the parasitic WDU lift for various configurations of a crossover SUV using numerical simulations that have been correlated to wind tunnel data. Several parameters were considered in the investigation, such as WDU size, WDU placement, tyre variants and vehicle configurations. The results show that the parasitic lift is more sensitive to the width than the length of the WDU. However, the belt length is also important to consider, especially if the wheel cannot be placed centred.

In view of BS-VI emission norms implementation in Commercial Vehicle (CV) application, the emissions are not only confirmed in standard condition but also in non-standard condition including different combinations of ambient temperature and pressure especially for checking the emission in WNTE cycle. However, achieving the emissions in different environmental conditions require physical emission calibration to be performed in those conditions. Hence, engine must be calibrated in climatic test chambers to ensure emission in different climatic conditions leading to multifold increase in the calibration effort. With addition of BS-VI emission regulation, After Treatment System (ATS) is a mandatory requirement to reduce the tail pipe emissions. Efficient functioning of ATS requires enough heating to convert the engine out emissions. Vehicle level Real Drive Emission (RDE) measurement without Conformity Factor (CF) limitation are added as an important legislative requirement.

The major objective of this paper is to develop thermal management strategy targeting optimum performance of Selective Catalytic Reduction (SCR) catalyst in a Medium Duty Diesel Engine performing in BS6 emission cycles. In the current scenario, the Emissions Norms are becoming more stringent and with the introduction of Real Drive Emission Test (RDE) and WHTC test comprising of both cold and hot phase, there is a need to develop techniques and strategies which are quick to respond in real time to cope with emission limit especially NOx. SCR seems to be suitable solution in reducing NOx in real time. However, there are limitations to SCR operating conditions, the major being the dosing release conditions which defines the gas temperature at which DEF (Diesel Exhaust Fluid) can be injected as DEF injection at lower gas temperatures than dosing release will lead to Urea deposit formation and will significantly hamper the SCR performance.

This paper investigates how particulates number PN is influenced by fuel wall-film, liner wetting, and the mixing quality for different start of injection timings (SOI). Both experimental data with PN measurements, endoscope images from a high-speed camera from a single-cylinder engine, and CFD simulations were used for the analysis. Engine geometry was a spray-guided system with 300 bar fuel pressure and with single injections. Data was captured for 2000 rpm / 9 bar IMEPn. The results show that fuel film on the piston was only found to significantly increase PN for over-advanced SOI (in our engine geometry, earlier than -310 CAD). This results in luminescence from diffusion burn on the piston surface, which strongly contributes to PN. For an SOI timing of -310 CAD, fuel film on piston reaches a maximum of 3% of the injected fuel, vaporizes, and no remaining fuel film is found at the time of ignition. Approximately 0.5-1% of the fuel ends up on the liner.

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

Gasoline direct injection (GDI) engines usually emit higher levels of particulates in warm-up conditions of a driving cycle. Thus, sources of soot formation in these conditions were investigated by measuring particulate numbers (PN) emitted from a single-cylinder GDI engine and their sizes. The combustion was also visualized using an endoscope connected to a high-speed camera. Engine coolant and oil temperatures were varied between 15 and 90oC to mimic warm-up conditions. In addition, effects of delaying the start of ignition (SOI) on the emissions in these conditions were examined. Coolant and oil temperatures were varied individually to identify which factor has most effect on PN emissions. While coolant temperature strongly influenced PN with cold oil, the oil temperature insignificantly affected PN at low coolant temperature. These findings indicate that PN emissions are heavily dependent on the engine block’s temperature, which is dominated by the coolant.

Fuel injection is a key process influencing the performance of Gasoline Direct Injection (GDI) Engines. Injecting fuel at elevated temperature can initiate flash boiling which can lead to faster breakup, reduced penetration, and increased spray-cone angle. Thus, it impacts engine efficiency in terms of combustion quality, CO2, NOx and soot emission levels. This research deals with modelling of flash boiling processes occurring in gasoline fuel injectors. The flashing mass transfer rate is modelled by the advanced Hertz-Knudsen model considering the deviation from the thermodynamic-equilibrium conditions. The effect of nucleation-site density and its variation with degree of superheat is studied. The model is validated against benchmark test cases and a substantiated comparison with experiment is achieved.

The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks.

A method for measuring apparent soot particle size and concentration in turbulent combusting diesel jets with elevated and inhomogeneous optical density is presented and discussed. The method is based on the combination of quasi-simultaneous Laser Induced Incandescence (LII), Elastic Scattering (ELS) and Light Extinction (LE) measurements exhibiting a high potential for spatially resolved measurements of carbonaceous particles in flames and residual gases at a given instant. The method evaluates the LII signal by calculating the laser fluence across the flame and compensating for signal trapping, allowing measurements where laser extinction between the flame borders reaches values up to 90%. The method was implemented by measuring particle size and concentration in the middle sagittal axis of optically dense, combusting diesel jets at a certain time after the start of combustion.

In the context of today’s and future legislative requirements for NOx and soot particle emissions as well as today’s market trends for further efficiency gains in gasoline engines, computational fluid dynamics (CFD) models need to further improve their intrinsic predictive capability to fulfill OEM needs towards the future. Improving fuel chemistry modelling, knock predictions and the modelling of the interaction between the chemistry and turbulent flow are three key challenges to improve the predictivity of CFD simulations of Spark-Ignited (SI) engines. The Flamelet Generated Manifold (FGM) combustion modelling approach addresses these challenges. By using chemistry pre-tabulation technologies, today’s most detailed fuel chemistry models can be included in the CFD simulation. This allows a much more refined description of auto-ignition delays for knock as well as radical concentrations which feed into emission models, at comparable or even reduced overall CFD run-time.

The effects of using n-butanol, n-octanol, fossil Diesel, hydrotreated vegetable oil (HVO), and blends of these fuels on spray penetration, flame and soot characteristics were investigated in a high-pressure high-temperature constant volume combustion chamber designed to mimic a heavy duty Diesel engine. Backlight illumination was used to capture liquid and vapor phase spray images with a high-speed camera. The flame lift-off length (LOL) and ignition delay were determined by analyzing OH* chemiluminescence images. Laser extinction diagnostics were used to measure the spatially and temporally resolved soot volume fraction. The spray experiments were performed by injecting fuels under non-combusting (623 K) and combusting (823 K) conditions at a fixed ambient air density of 26 kg/m3. A Scania 0.19 mm single straight hole injector and Scania XPI common rail fuel supply system were used to produce injection pressures of 120 MPa and 180 MPa.

Maximum fuel injection pressures for GDI engines is expected to increase due to positive effects on emissions and engine-efficiency. Current GDI injectors have maximum operating pressures of 35 MPa, but higher injection pressures have yielded promising reductions in particle number (PN) and improved combustion stability. However, the mechanisms responsible for these effects are poorly understood, and there have been few studies on fuel sprays formed at high injection pressures. This paper summarizes experimental studies on the properties of sprays formed at high injection pressures. The results of these experiments can be used as inputs for CFD simulations and studies on combustion behavior, emissions formation, and combustion system design. The experiments were conducted using an injection rate meter and optical methods in a constant volume spray chamber. Injection rate measurements were performed to determine the injectors’ flow characteristics.

Look ahead information can be used to improve the powertrain’s fuel consumption while efficiently controlling exhaust emissions. A passenger car propelled by a Euro 6d capable diesel engine is studied. In the conventional approach, the diesel powertrain subsystem control is rule based. It uses no information of future load requests but is operated with the objective of low engine out exhaust emission species until the Exhaust After-Treatment System (EATS) light off has occurred, even if fuel economy is compromised greatly. Upon EATS light off, the engine is operated more fuel efficiently since the EATS system is able to treat emissions effectively. This paper presents a supervisory control structure with the intended purpose to operate the complete powertrain using a minimum of fuel while improving the robustness of exhaust emissions.

The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.

With upcoming emission regulations particle emissions for GDI engines are challenging engine and injector developers. Despite the introduction of GPFs, engine-out emission should be optimized to avoid extra cost and exhaust backpressure. Engine tests with a state of the art Miller GDI engine showed up to 200% increased particle emissions over the test duration due to injector deposit related diffusion flames. No spray altering deposits have been found inside the injector nozzle. To optimize this tip sooting behavior a tool chain is presented which involves injector multiphase simulations, a spray simulation coupled with a wallfilm model and testing. First the flow inside the injector is analyzed based on a 3D-XRay model. The next step is a Lagrangian spray simulation coupled with a wallfilm module which is used to simulate the fuel impingement on the injector tip and counter-bores.

Due to an ongoing trend of high injection pressures in the realm of internal combustion engines, the role of cavitation that typically happens inside the injector nozzle has become increasingly important. In this work, a large Eddy Simulation (LES) with cavitation modeled on the basis of an Eulerian Stochastic Field (ESF) method and a homogeneous mixture model is performed to investigate the role of cavitation on the Engine Combustion Network (ECN) spray G2. The Eulerian stochastic field cavitation model is coupled to a pressure based solver for the flow, which lowers the computational cost, thereby making the methodology highly applicable to realistic injector geometries. Moreover, the nature of the Eulerian stochastic field method makes it more convenient to achieve a high scalability when applied to parallel cases, which gives the method the edge over cavitation models that are based on Lagrangian tracking.

In engine research and development there are often different engine parameters that produce similar effects on the end-point results. When calibrating modern engines, a huge number of parameters needs to be set, which also includes compensation parameters for model imperfections. In this context, simpler, more robust, and physically based models should be beneficial both for calibration work load and powertrain performance. In this study, we present an experimental methodology that uses intermediate (“intrinsic”) variables instead of engine parameters. By using simple thermodynamic models, the engine parameters EGR, IVC, and PBoost could be translated into oxygen concentration, temperature and gas density at the start of injection. The reason for this transformation of data is to “move” the Design of Experiment (DoE) closer to the situation of interest (i.e. the combustion) and to be able to construct simpler and more physically based models.

Thermodynamic power cycles have been shown to provide an excellent method for waste heat recovery (WHR) in internal combustion engines. By capturing and reusing heat that would otherwise be lost to the environment, the efficiency of engines can be increased. This study evaluates the maximum power output of different cycles used for WHR in a heavy duty Diesel engine with a focus on working fluid selection. Typically, only high temperature heat sources are evaluated for WHR in engines, whereas this study also considers the potential of WHR from the coolant. To recover the heat, four types of power cycles were evaluated: the organic Rankine cycle (ORC), transcritical Rankine cycle, trilateral flash cycle, and organic flash cycle. This paper allows for a direct comparison of these cycles by simulating all cycles using the same boundary conditions and working fluids.