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The continuous pursuit of higher combustion efficiencies, as well as the possible usage of synthetic fuels with different properties than fossil-ones, require reliable and low-cost numerical approaches to support and speed-up engines industrial design. In this context, SI engines operated with homogeneous ultra-lean mixtures both characterized by a classical ignition configuration or equipped with an active prechamber represent the most promising solutions. In this work, for the classical ignition arrangement, a 3DCFD strategy to model the impact of the ignition system type on the CCV is developed using the RANS approach for turbulence modelling. The spark-discharge is modelled through a set of Lagrangian particles, whose velocity is modified with a zero-divergence perturbation at each discharge event, then evolved according to the Simplified Langevin Model (SLM) to simulate stochastic interactions with the surrounding gas flow.

Active prechamber combustion systems for SI engines represent a feasible and effective solution in reducing fuel consumption and pollutant emissions for both marine and ground heavy-duty engines. However, reliable and low-cost numerical approaches need to be developed to support and speed-up their industrial design considering their geometry complexity and the involved multiple flow length scales. This work presents a CFD methodology based on the RANS approach for the simulation of active prechamber spark-ignition engines. To reduce the computational time, the gas exchange process is computed only in the prechamber region to correctly describe the flow and mixture distributions, while the whole cylinder geometry is considered only for the power-cycle (compression, combustion and expansion). Outside the prechamber the in-cylinder flow field at IVC is estimated from the measured swirl ratio.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

A correct prediction of the initial stages of the combustion process in SI engines is of great importance to understand how local flow conditions, fuel properties, mixture stratification and ignition affect the in-cylinder pressure development and pollutant formation. However, flame kernel growth is governed by many interacting processes including energy transfer from the electrical circuit to the gas phase, interaction between the plasma channel and the flow field, transition between different combustion regimes and gas expansion at very high temperatures. In this work, the authors intend to present a comprehensive, multi-dimensional model that can be used to predict the initial combustion stages in SI engines. In particular, the spark channel is represented by a set of Lagrangian particles where each one of them acts as a single flame kernel.

Emissions and in-cylinder pressure traces are used to compare the relative importance of soot formation and soot oxidation in a heavy-duty diesel engine. The equivalence ratio at the lift-off length is estimated with an empirical correlation and an idealized model of diesel spray. No correlation is found between the equivalence ratio at lift-off and the soot emissions. This confirms that trends in soot emissions cannot be directly understood by the soot formation process. The coupling between soot emission levels and late heat release after end of injection is also studied. A regression model describing soot emissions as function of global engine parameters influencing soot oxidation is proposed. Overall, the results of this analysis indicate that soot emissions can be understood in terms of the efficiency of the oxidation process.

Increased research is being driven by the automotive industry facing challenges, requiring to comply with both current and future emissions legislation, and to lower the fuel consumption. The reason for this legislation is to restrict the harmful pollution which every year causes 3.3 million premature deaths worldwide [1]. One factor that causes this pollution is NOx emissions. NOx emission legislation has been reduced from 8 g/kWh (Euro I) down to 0.4 g/kWh (Euro VI) and recently new legislation for ammonia slip which increase the challenge of exhaust aftertreatment with a SCR system. In order to achieve a good NOx conversion together with a low slip of ammonia, small droplets of Urea solution needs to be injected which can be rapidly evaporated and mixed into the flow of exhaust gases.

In the automotive industry, the piezo-based in-cylinder pressure sensor is getting commercialized and used in production vehicles. For example, the pressure sensor offers the opportunity to design algorithms for estimation of engine emissions, such as soot and NO , during a combustion cycle. In this paper a zero-dimensional NO model for a diesel engine is implemented that will be used in real time. The model is based on the thermal NO formation and the Zeldovich mechanism using two non-geometrical zones: burned and unburned zone. The influence of EGR on combustion temperature was modeled using a well-known thermodynamic identity where specific heat at constant pressure is included. Specific heat will vary with temperature and the gas composition. The model was implemented in LabVIEW using tools specific for an FPGA (Field-Programmable Gate Array).

SI engines fueled with hydrogen represent a promising powertrain solution to meet the ambitious target of carbon-free emissions at the tailpipe. Therefore, fast and reliable numerical tools can significantly support the automotive industry in the optimization of such technology. In this work, a 1D-3D methodology is presented to simulate in detail the combustion process with minimal computational effort. First, a 1D analysis of the complete engine cycle is carried out on the user-defined powertrain configuration. The purpose is to achieve reliable boundary conditions for the combustion chamber, based on realistic engine parameters. Then, a 3D simulation of the power-cycle is performed to mimic the combustion process. The flow velocity and turbulence distributions are initialized without the need of simulating the gas exchange process, according to a validated technique.

As the number of actuators and sensors increases in modern combustion engines, the task of optimizing engine performance becomes increasingly complex. Efficient information processing techniques are therefore important, both for off-line calibration of engine maps, and on-line adjustments based on sensor data. In-cylinder pressure sensors are slowly spreading from laboratory use to production engines, thus making data with high temporal resolution of the combustion process available. The standard way of using the cylinder pressure data for control and diagnostics is to focus on a few important physical features extracted from the pressure trace, such as the combustion phasing CA50, the indicated mean effective pressure IMEP, and the ignition delay. These features give important information on the combustion process, but much information is lost as the information from the high-resolution pressure trace is condensed into a few key parameters.

The combustion timing in internal combustion engines affects the fuel consumption, in-cylinder peak pressure, engine noise and emission levels. The combination of an in-cylinder pressure sensor together with a direct injection fuel system lends itself well for cycle-to-cycle control of the combustion timing. This paper presents a method of controlling the combustion timing by the use of a cycle-to-cycle injection-timing algorithm. At each cycle the currently estimated heat-release rate is used to predict the in-cylinder pressure change due to a combustion-timing shift. The prediction is then used to obtain a cycle-to-cycle model that relates combustion timing to gross indicated mean effective pressure, max pressure and max pressure derivative. Then the injection timing that controls the combustion timing is decided by solving an optimization problem involving the model obtained.

The paper describes a novel method for calculating the residual gas fraction and the trapping efficiency in a 2 stroke engine. Assuming one dimensional compressible flow through the inlet and exhaust ports, the method estimates the instantaneous mass flowing in and out from the combustion chamber; later the residual gas fraction and trapping efficiency are estimated combining together the perfect displacement and perfect mixing scavenging models. It is assumed that when the intake port opens, the fresh mixture is pushing out the burned charge without any mixing and after a multiple of the time needed for the largest eddy to perform one rotation, the two gasses are instantly mixed up together and expelled. The result is a very simple algorithm that does not require much computational time and is able to estimate with high level of precision the trapping efficiency and the residual gas fraction in 2 stroke engines.

In this paper, numerical simulations of an automotive-size optical diesel engine have been conducted employing the Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model and a reduced n-heptane chemical mechanism implemented in OpenFOAM. The current paper builds on a previous work where the model has been validated for the same engine using optical diagnostic data. The present study investigates numerically the influence of different operating conditions - relevant for modern diesel engines - on the mixture formation development under non-reactive conditions as well as low- and high-temperature ignition behaviour and flame evolution in the presence of strong jet-wall interactions typically encountered in automotive-size diesel engines. Also, emissions of CO and unburned hydrocarbons (UHC) are considered.

In this paper, computation fluid dynamics (CFD) simulations are employed to describe the effect of flow parameters on the formation of soot and NOx in a heavy duty engine under low load and high load. The complexity of diesel combustion, specially when soot, NOx and other emissions are of interest, requires using a detailed chemical mechanism to have a correct estimation of temperature and species distribution. In this work, Multiple Representative Interactive Flamelets (MRIF) method is employed to describe the chemical reactions, ignition, flame propagation and emissions in the engine. A phenomenological model for soot formation, including soot nucleation, coagulation and oxidation with O2 and OH is incorporated into the flamelet combustion model. Different strategies for modelling NOx are chosen to take into account the longer time scale for NOx formation. The numerical results are compared with experimental data to show the validity of the model for the cases under study.

The emission control in heavy-duty vehicles today is based on predefined injection strategies and after-treatment systems such as SCR (selective catalytic reduction) and DPF (diesel particulate filter). State-of-the-art engine control is presently based on cycle-to-cycle resolution. The introduction of the crank angle resolved pressure measurement, from a piezo-based pressure sensor, enables the possibility to control the fuel injection based on combustion feedback while the combustion is occurring. In this paper a study is presented on the possibility to control NOx (nitrogen oxides) formation with a crank angle resolved NOx estimator as feedback. The estimator and the injection control are implemented on an FPGA (Field-Programmable Gate Array) to manage the inherent time constraints. The FPGA is integrated with the rest of the engine control system for injection control and measurement.

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional liquid fuels to CNG. To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts. In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on.

A Scania 13 1 engine modified for single cylinder operations was run using nine fuels in the boiling point range of gasoline, but very different octane number, together with PRF20 and MK1-diesel. The eleven fuels were tested in a load sweep between 5 and 26 bar gross IMEP at 1250 rpm and also at idle (2.5 bar IMEP, 600 rpm). The boost level was proportional to the load while the inlet temperature was held constant at 303 K. For each fuel the load sweep was terminated if the ignitibility limit was reached. A lower load limit of 15 and 10 bar gross IMEP was found with fuels having an octane number range of 93-100 and 80-89 respectively, while fuels with an octane number below 70 were able to run through the whole load range including idle. A careful selection of boost pressure and EGR in the previously specified load range allowed achieving a gross indicated efficiency between 52 and 55% while NOx ranged between 0.1 and 0.5 g/kWh.

Partially Premixed Combustion (PPC) is a promising combustion concept with high thermodynamic efficiency and low emission level, and also with minimal modification of standard engine hardware. To use PPC in a production oriented engine, the optimal intake charge conditions for PPC should be included in the analysis. The experiments in this paper investigated and confirmed that the optimal intake conditions of net indicated efficiency for PPC are EGR between 50% and 55% as possible and the lambda close to 1.4. Heat-transfer energy and exhaust gas waste-energy contribute to the majority of the energy loss in the engine. The low EGR region has high heat-transfer and low exhaust gas enthalpy-waste, while the high EGR region has low heat-transfer and high exhaust gas waste-enthalpy. The optimal EGR condition is around 50% where the smallest energy loss is found as a trade-off between heat transfer and exhaust-gas enthalpy-waste.

This article deals with study of ionization current sensing technique's signal characteristics while operating with pre-chamber spark plug to achieve plasma jet ignition in a 6 cylinder 9 liter turbo-charged natural gas engine under EGR and excess air dilution. Unlike the signal with conventional spark plug which can be divided into distinct chemical and thermal ionization peaks, the signal with pre-chamber spark plug shows a much larger first peak and a negligible second peak thereafter. Many studies in past have found the time of second peak coinciding with the time of maximum cylinder pressure and this correlation has been used as an input to combustion control systems but the absence of second peak makes application of this concept difficult with pre-chamber spark plug.

Multiple injections and high EGR rates are now widely adopted for combustion and emissions control in passenger car diesel engines. In a wide range of operating conditions, fuel is provided through one to five separated injection events, and recirculated gas fractions between 0 to 30% are used. Within this context, fast and reliable multi-dimensional models are necessary to define suitable injection strategies for different operating points and reduce both the costs and time required for engine design and development. In this work, the authors have applied a modified version of the characteristic time-scale combustion model (CTC) to predict combustion and pollutant emissions in diesel engines using advanced injection strategies. The Shell auto-ignition model is used to predict auto-ignition, with a suitable set of coefficients that were tuned for diesel fuel.

Homogeneous Charge Compression Ignition (HCCI) holds great promises for good fuel economy and low emissions of NOX and soot. The concept of HCCI is premixed combustion of a highly diluted mixture. The dilution limits the combustion temperature and thus prevents extensive NOX production. Load is controlled by altering the quality of the charge, rather than the quantity. No throttling together with a high compression ratio to facilitate auto ignition and lean mixtures results in good brake thermal efficiency. However, HCCI also presents challenges like how to control the combustion and how to achieve an acceptable load range. This work is focused on solutions to the latter problem. The high dilution required to avoid NOX production limits the mass of fuel relative to the mass of air or EGR. For a given size of the engine the only way to recover the loss of power due to dilution is to force more mass through the engine.