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The use of oxygenated and renewable fuels is nowadays a widespread means to reduce regulated pollutant emissions produced by internal combustion engines, as well as to reduce the greenhouse impact of transportation. Besides PM, NOx and HC emissions, also the size distribution of particles emitted at the engine exhaust represent meaningful information, considering its adverse effects on the environment and human health. In this work, the results of a comprehensive investigation on the combustion characteristics and the exhaust emissions of a GDI high performance engine, fuelled with pure bio-ethanol and European gasoline, are shown. The engine is a 4-cylinder, 4-stroke, 1750 cm₃ displacement, and turbocharged. The engine was operated at different speed/load conditions and two fuel injection strategies were investigated: homogeneous charge mode and stratified charge mode.

Unregulated emissions of polycyclic aromatic hydrocarbons (PAHs), carbonylic compounds, benzene and particulate matter (PM) were quantified in exhausts of a vehicle fleet representative of in use gasoline cars. Emission factors were obtained during both cold and hot start driving cycles (from urban to motorway driving conditions). Carbonylic compounds were sampled by DNPH cartridges and analyzed by HPLC. Benzene and other light hydrocarbons were collected in bags and then analyzed by GC-FID. PAHs were trapped in XAD-2 cartridges and then analyzed by GC-MS. PM was sampled by using the gravimetric procedure required for diesel cars. The effect of technology is significant with respect to regulated and unregulated emissions but different emissive behavior was found by varying the driving cycles. Cold start has a major influence on hydrocarbon emissions (included unregulated ones). This experimental work was carried out within the framework of the EU Artemis project.

In this work, optical diagnostics were applied in a transparent DI diesel engine equipped with the head of Euro5 commercial engine and the last generation CR injection system. In order to realize the PCCI combustion the injection of neat bio-ethanol was performed in the intake manifold and European commercial diesel fuel was injected into the cylinder. Different amounts of bio-ethanol were injected in order to create PCCI combustion with high levels of pre-combustion mixing, and to ensure low equivalence ratio and low flame temperatures too. UV-Visible imaging and spectroscopic measurements were performed in the engine in order to investigate the autoignition of the charge and the combustion process, respectively. In particular, the detection of the species involved in the combustion, like OH, HCO, and CH, was performed. The relevance of the radicals and species on PCCI were evaluated and compared with the data from thermodynamic analysis.

Premixed charge compression ignition (PCCI) has been shown to be a promising strategy to simultaneously reduce emissions while realizing improved fuel economy. PCCI combustion uses high levels of pre-combustion mixing to lower both NOx and soot emissions by ensuring low equivalence ratio and low flame temperatures. The high level of pre-combustion mixing results in a primarily kinetics controlled combustion process. In this work, optical diagnostics have been applied in a transparent DI diesel engine equipped with the head of Euro5 commercial engine and the last generation CR injection system. In order to realize the PCCI combustion the injection of neat ethanol has been performed in the intake manifold. The engine run in continuous way at 1500 rpm engine speed and commercial diesel fuel has been injected into the cylinder. The PCCI combustion has been analyzed by means of UV- Visible digital imaging and the mixing process, the autoignition of the charge have been investigated.

Spectral flame emissivity and absorption measurements with high temporal and spatial resolution were performed in an optically accessible high-swirl divided-chamber Diesel system. Simultaneous determination of soot temperature, soot volume fraction and the OH radical concentration were made from the start to the end of the combustion in 153 locations equally distributed in the chamber. The engine was run at 2000 rpm and at fixed air-fuel ratio realizing 200 consecutive combustion cycles. To visualize the spatial and temporal spray and flame evolution, direct high-speed photographic sequences were taken at 8000 frames/s. The photographic sequences showed that the spray is strongly distorted and mixed by very high swirl resulting in a well premixed region where the combustion starts. The OH radicals were detected in the fuel reaction zone. Moreover OH concentration and soot volume fraction are well correlated with soot temperature.

This study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.

In this paper the investigation with X-ray Tomography on the structure of a gasoline spray from a GDI injector for automotive applications based on polycapillary optics is reported. Table-top experiment using a microfocus Cu Kα X-ray source for radiography and tomography has been used in combination with a polycapillary halflens and a CCD detector. The GDI injector is inserted in a high-pressure rotating device actuated with angular steps Δθ = 1° at the injection pressure of 8.0 MPa. The sinogram reconstruction of the jets by slices permits a 360° spray access to the fuel downstream the nozzle tip. A spatial distribution of the fuel is reported along the direction of six jets giving a measure of the droplet concentration in a circle of 16 mm2 below the nozzle tip at atmospheric backpressure and ambient temperature.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the capability of GM Combustion Closed-Loop Control (CLCC) in enabling seamless operation with high biodiesel blending levels in a modern diesel engine for passenger car applications. As a matter of fact, fuelling modern electronically-controlled diesel engines with high blends of biodiesel leads to a performance reduction of about 12-15% at rated power and up to 30% in the low-end torque, while increasing significantly the engine-out NOx emissions. These effects are both due to the interaction of the biodiesel properties with the control logic of the electronic control unit, which is calibrated for diesel operation. However, as the authors previously demonstrated, if engine calibration is re-tuned for biodiesel fuelling, the above mentioned drawbacks can be compensated and the biodiesel environmental inner qualities can be fully deployed.

The effect of different fuel parameters on emissions is difficult to understand, the response depending upon different engine technologies. In addition the isolation of some of the fuel variables is often very hard. The present paper discusses the main results obtained testing a matrix of 14 fuels designed for obtain large variations of cetane number, sulphur and aromatic contents of Diesel oil. The aromatic structure of fuels and its effect on particulate emissions was also investigated. A linear regression analysis was performed in order to isolate the main controlling factors on particulate emissions. Finally the influence of aromatic contents of fuel on unregulated emissions was also assessed.

With the advent of the KIVA-4 code which employs an unstructured mesh to represent the engine geometry, the gap in flexibility between commercial and research modeling software becomes more narrow. In this study, we tried to perform a full cycle simulation of a 4-stroke HD diesel engine represented by a highly boosted research IF (Isotta Fraschini) engine using the KIVA-4 code. The engine mesh including the combustion chamber, intake and exhaust valves and helical manifolds was constructed using optional O-Grids catching a complex geometry of the engine parts with the help of the ANSYS ICEM CFD software. The KIVA-4 mesh input was obtained by a homemade mesh converter which can read STAR-CD and CFX outputs. The simulations were performed on a full 360 deg mesh consisting of 300,000 unstructured hexahedral cells at BDC. The physical properties of the liquid fuel were taken corresponding to those of real diesel #2 oil.

Nowadays, the regulation regards only the particles larger than 23 nm. The attention is shifting towards the sub-23 nm particles because of their large presence at the exhaust of the modern engines and their negative impact on human health. The main challenge of the regulation of these particles is the definition of a proper procedure for their measure. The nature of the sub-23 nm particles is not well understood, and their measure is strongly affected by the sampling conditions leading to not reliable measure. The aim of this paper is to provide information on the emissions of sub-23 nm particles from GDI vehicles/engines. At the same time, the presence of volatiles, which mainly contribute to the formation of sub-23 nm particles, was evaluated and the effect of sampling conditions was investigated. The analysis was performed on a 1.8L GDI powered vehicle, widely used both in North America and Europe, and a 4-cylinder GDI engine, whose features are similar to those of the vehicle.

In the global scenario of encouraging the use of renewable sources, the bioethanol as fuel supply in the automotive sector is receiving increasing interest. In the present paper the results of a research activity aimed to study the impact of a bioethanol/biodiesel/mineral diesel blend on performance and emissions of an automotive diesel engine are reassumed. An experimental campaign has been devoted to characterize the engine fuelled by the ethanol based blend highlighting the advantages and issues related to the bioethanol use. Moreover, the effects of the most important injection settings on the engine performance have been detailed, applying a Design of Experiment (DoE) method, to identify the potentiality offered by a proper engine calibration to optimize the ethanol blend use.

The aim of the present work is the study of the combustion process in Gasoline Direct Injection (GDI) engine fuelled with ethanol mixed with gasoline at percentages of 10 and 85. The characterization has been made in terms of performance and emission for different injection pressure conditions and the results correlated to the unperturbed non-evaporating evolution of the fuel injected in a pressurized quiescent vessel. Measurements were performed in the optically accessible combustion chamber made by modifying a real 4-stroke, 4-cylinder, high performance GDI engine. The cylinder head was instrumented by using an endoscopic system coupled to high spatial and temporal resolution camera in order to allow the visualization of the fuel injection and the combustion process. The engine is equipped with solenoid-actuated six-hole GDI injectors, 0.14 mm hole diameter, 9.0 g/s @ 10 MPa static flow.

In the context of a transport sustainability, some solutions could be proposed from the integration of many disciplines, architects, environmentalists, policy makers, and consequently it may be addressed with different approaches. These solutions would be applied at different geographical levels, i.e. national, regional or urban scale. Moreover, the assessment of cars emissions in real use plays a fundamental role for their reductions. This is also the direction of the new harmonized test procedures (WLTP). Furthermore, it is fundamental to keep in mind that the new WLTC cycle will reproduce a situation closer to the reality comparing to the EUDC/NEDC driving cycle. In this paper, we will be focused on vehicle kinematic evaluation aimed at valuation of traffic situation and emissions.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the impact of both fresh and highly oxidized RME at two levels of blending on spray formation and combustion in modern automotive diesel engines. The tests were performed on an optical single-cylinder engine sharing combustion system configuration with the 2.0L Euro5 GM diesel engine for passenger car application. Two blends (B50 and B100) blending were tested for both fresh and aged RME and compared with commercial diesel fuel in two different operating points typical of NEDC (1500rpm/2bar BMEP and 2000rpm/5bar BMEP). The experimental activity was devoted to an in-depth investigation of the spray density, breakup and penetration, mixture formation, combustion and soot formation, by means of optical techniques.

In this paper, a high temporal resolution optical technique, based on the multi-wavelength UV-visible-near IR extinction spectroscopy, was applied at the exhaust of an automotive diesel engine to investigate the post-injection strategy impact on the fuel vapor. Experimental investigations were carried out using three fuels: commercial diesel (B5), a blend of 80% diesel with 20% by vol. of gasoline (G20) and a blend of 80% diesel with 20% by vol. of n-butanol (BU20). Experiments were performed at the engine speed of 2500rpm and 0.8MPa of brake mean effective pressure exploring two post-injection timings and two EGR rates. The optical diagnostic allowed evaluating, during the post-injection activation, the evolution of the fuel vapor in the engine exhaust line. The investigation was focused on the impact of post-injection strategy and fuel properties on the aptitude to produce hydrocarbon rich gaseous exhaust for the regeneration of diesel particulate trap (DPF).

Three dimensional computations of Diesel combustion were performed using a modified version of Kiva II code. The autoignition and combustion model were tuned on a set of experimental conditions, changing the engine design, the operating conditions and the fuel characteristics. The sensitivity of the model to the different test cases is acceptable and the experimental trends are well reproduced. In addition the peak of pressure and temperature computed by the code are quite close to the experimental values, as well as the pressure derivatives. Once tuned the combustion model constants, different but simple formulations for the soot formation and oxidation processes were implemented in the code and compared with the experimental measurements obtained both with fast sampling technique and two colors method. These formulations were found unable to give good prediction in a large range of engine operating conditions, even if the model tuning may be very good for each test point.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine.

Gasoline direct injection (GDI) combustion with un-throttled lean stratified operation allows to reduce engine toxic emissions and achieve significant benefits in terms of fuel consumption. However, use of gasoline stratified charges can lead to several problems, such as a high cycle-to-cycle variability and increased particle emissions. Use of multiple injection strategies allows to mitigate these problems, but it requires the injection of small fuel amounts forcing the traditional solenoid injectors to work in their “ballistic” region, where the correlation between coil energizing time and injected fuel amount becomes highly not linear. In the present work a closed-loop control system able to manage the delivery of small quantities of fuel has been introduced. The control system is based on a particular feature found on the coil voltage command signal during the de-energizing phase.