Search Results

The common realization of the necessity to reduce the use of mineral sources is promoting the use of alternative fuels. Big efforts are being made to replace petroleum derivatives in the internal combustion engines (ICEs). For this purpose it is mandatory to evaluate the behavior of non-conventional fuels in the ICEs. The optical diagnostics have proven to be a powerful tool to analyze the processes that take place inside the engine. In particular, 2d imaging in the infrared range can reveal new details about the effect of the fuel properties since this technique is still not very common. In this work, a comparison between commercial diesel fuel and two non-conventional fuels has been made in an optically accessible diesel engine. The non-conventional fuels are: the first generation biofuel Rapeseed Methyl Ester (RME) and an experimental blend of diesel and a fuel with high glycerol content (HG).

An innovative 0D predictive combustion model for the simulation of the HRR (heat release rate) in DI diesel engines was assessed and implemented in a 1D fluid-dynamic commercial code for the simulation of a Fiat heavy duty diesel engine equipped with a Variable Geometry Turbocharger system, in the frame of the CORE (CO2 reduction for long distance transport) Collaborative Project of the European Community, VII FP. The 0D combustion approach starts from the calculation of the injection rate profile on the basis of the injected fuel quantities and on the injection parameters, such as the start of injection and the energizing time, taking the injector opening and closure delays into account. The injection rate profile in turn allows the released chemical energy to be estimated. The approach assumes that HRR is proportional to the energy associated with the accumulated fuel mass in the combustion chamber.

In order to meet the stricter and stricter emission regulations, cleaner combustion concepts for Diesel engines are being progressively introduced. These new combustion approaches often requires closed loop control systems with real time information about combustion quality. The most important parameter for the evaluation of combustion quality in internal combustion engines is the in-cylinder pressure, but its direct measurement is very expensive and involves an intrusive approach to the cylinder. Previous researches demonstrated the direct relationship existing between in-cylinder pressure and engine block vibration signal and several authors tried to reconstruct the pressure cycle on the basis of information coming from accelerometers mounted on engine block. This paper proposes a method, based on the analysis of the engine vibration signal, for the diagnosis of combustion process in a Diesel engine.

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.

The frequency spectral structure of turbulence spatial components was investigated in the cylinder of an automotive diesel engine with a high-squish reentrant in-piston bowl of the conical type and a helical inlet port. A sophisticated HWA technique using single- and dual-sensor probes was applied for instantaneous air velocity measurements along the injector axis at practical engine speeds, up to 3000 rpm, under motored conditions. The investigation was carried out for both cycle-resolved and conventional turbulence components, as were determined by different wire orientations, throughout the induction, the compression and the early stage of the expansion stroke. The anisotropy of turbulence spectral structure and its temporal evolution during the engine cycle were examined by evaluating the autospectral density functions and the time scales of each turbulence component in consecutive correlation crank-angle intervals.

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.

Internal combustion engines are characterized by high pressure and thermal loads on pistons and in cylinders. The heat generated by the combustion process is dissipated by means of water and oil cooling systems. For the best design and optimization of the engine components it is necessary to know the components temperature in order to estimate the thermal flows. The purpose of this work is to measure the piston sapphire window temperature in a research optically accessible engine by combining two different techniques: measurements with templugs and with thermography. The method is very simple and can provide a reliable value of temperature within a small interval. It fits well for applications inside the engine because of its low technical level requirements. It consists of application of temperature sensitive stickers on the target component that makes it a very robust method, not affected by piston movement.

The analysis of heat losses in internal combustion engines (ICEs) is fundamental to evaluate and to improve engine efficiency. Detailed and reliable heat transfer models are required for more complex 1d-3d combustion models. At the same time, the thermal status of engine components, like pistons, is needed for an efficient design. Measurements of piston temperature during ICEs operation represent an important and challenging result to get for the aforementioned purposes. In the present work, temperature measurements collected at different engine speeds and loads, both in motored and fired modes, have been performed and used to set-up a theoretical correlation and 1d model of heat transfer through the optical window of the piston. The in-cylinder gas and external ambient temperature, together with the thermodynamic and material properties are given. The model has been first calibrated in some selected operating conditions and then validated in the remaining.

The aim of this study is to investigate how the fouling that injectors undergo after several operating hours on a vehicle can affect the injection and combustion phases. The impact of the injector fouling on the pollutant formation has been also investigated. Moreover, the effects of the injector cleaning by deposits through the top quality diesel fuel commercialized by eni that is FAME free and contains multi performance additives have been investigated. The experimentation has been carried out on transparent compression ignition engine. It is a single cylinder equipped with a Euro 5 multi-cylinder head and a second-generation common rail injection system. Three indirect-acting piezoelectric injectors have been tested. The first one has been fouled with European commercial diesel fuel through the CEC DW10 injector-coking test. The second one has been fouled in the same way and, then, it has been cleaned with eni top quality diesel fuel. This fuel has fed the third injector too.

In the present work, different combustion control strategies have been experimentally tested in a heavy-duty 3.0 L Euro VI diesel engine. In particular, closed-loop pressure-based and open-loop model-based techniques, able to perform a real-time control of the center of combustion (MFB50), have been compared with the standard map-based engine calibration in order to highlight their potentialities. In the pressure-based technique, the instantaneous measurement of in-cylinder pressure signal is performed by a pressure transducer, from which the MFB50 can be directly calculated and the start of the injection of the main pulse (SOImain) is set in a closed-loop control to reach the MFB50 target, while the model-based approach exploits a heat release rate predictive model to estimate the MFB50 value and sets the corresponding SOImain in an open-loop control. The experimental campaign involved both steady-state and transient tests.

An investigation has been carried out on the spray penetration and soot formation processes in a research diesel engine by means of a quasi-dimensional multizone combustion model. The model integrates a predictive non stationary 1D spray model developed by the Sandia National Laboratory, with a diagnostic multizone thermodynamic model, and is capable of predicting the spray formation, combustion and soot formation processes in the combustion chamber. The multizone model was used to analyze three operating conditions, i.e., a zero load point (BMEP = 0 bar at 1000 rpm), a medium load point (BMEP = 5 bar at 2000 rpm) and a medium-high load point (BMEP = 10 bar at 2000 rpm). These conditions were experimentally tested in an optical single cylinder engine with the combustion system configuration of a 2.0L Euro4 GM diesel engine for passenger car applications.

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.

Diesel combustion process was studied and characterized by digital imaging and ultraviolet-visible flame emission, extinction and scattering spectroscopy. Optical measurements were applied to a transparent diesel engine, realized by modifying a single cylinder, air-cooled, 4-stroke diesel engine by means of an external combustion chamber on the top of the engine, connected to the main chamber by a tangential passage. Diesel engine was equipped with a fully flexible electronic controlled ‘Common Rail’ injection system. Measurements were performed at 1000 rpm engine speed for two typical injection strategies. The first one consisted of a main injection in order to compare the results with those ones obtained by conventional injection system operating at low pressure. The other one was based on a pilot and main injection that is typical of current direct injection diesel engines.

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.

The present work has the aim of developing a fast approach for the predictive calculation of in-cylinder combustion temperatures and NOx formation in diesel engines, under steady state and transient conditions. The model has been tested on a PC, and found to require very little computational time, thus suggesting it could be implemented in the ECU (Engine Control Unit) of engines for model-based control tasks. The method starts with the low-throughput predictive combustion model that was previously developed by the authors, which allows the predictive estimation of the heat-release rate and of the in-cylinder pressure trace to be made on the basis of the injection parameters and of a few quantities measured by the ECU, such as the intake manifold pressure and temperature.

A new procedure for the real-time estimation of the EGR rate and charge oxygen concentration has been developed, assessed and applied to a low-compression ratio GMPT-E EURO V diesel engine. High EGR rates are usually employed in modern diesel engines to reduce combustion temperatures and NOx emissions, especially at medium-low load and speed conditions. The EGR rate is usually calibrated in steady-state conditions, but, under transient conditions, it can be responsible for misfire occurrence or non optimal combustion cycles, if not properly controlled. In other words, combustion instabilities can occur, especially during tip-in maneuvers, which imply transition from high EGR (low load) to low EGR (high load) rates. Misfire is determined by a temporary reduction in the intake charge oxygen concentration during the closure of the EGR valve.

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.

This paper deals with the combustion behavior and exhaust emissions of a small compression ignition engine modified to operate in diesel/methane dual fuel mode. The engine is a three-cylinder, 1028 cm3 of displacement, equipped with a common rail injection system. The engine is provided with the production diesel oxidation catalyst. Intake manifold was modified in order to set up a gas injector managed by an external control unit. Experiments were carried out at different engine speeds and loads. For each engine operating condition, the majority of the total load was supplied by methane while a small percentage of the load was realized using diesel fuel; the latter was necessary to ignite the premixed charge of gaseous fuel. Thermodynamical analysis of the combustion phase was performed by in-cylinder pressure signal. Gas emissions and particulate matter were measured at the exhaust by commercial instruments.

This paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power. First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range. A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time.