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Growing concerns about the emissions of internal combustion engines have forced the adoption of aftertreatment devices to reduce the adverse impact of diesel engines on health and environment. Diesel particulate filters are considered as an effective means to reduce the particle emissions and comply with the regulations. Research activity in this field focuses on filter configuration, materials and aging, on understanding the variation of soot layer properties during time, on defining of the optimal strategy of DPF management for on-board control applications. A model was implemented in order to simulate the filtration and regeneration processes of a wall-flow particulate filter, taking into account the emission characteristic of the engine, whose architecture and operating conditions deeply affect the size distribution of soot particles.

The management of multiple injections in compression ignition (CI) engines is one of the most common ways to increase engine performance by avoiding hardware modifications and after-treatment systems. Great attention is given to the profile of the injection rate since it controls the fuel delivery in the cylinder. The Injection Rate Shaping (IRS) is a technique that aims to manage the quantity of injected fuel during the injection process via a proper definition of the injection timing (injection duration and dwell time). In particular, it consists in closer and centered injection events and in a split main injection with a very small dwell time. From the experimental point of view, the performance of an IRS strategy has been studied in an optical CI engine. In particular, liquid and vapor phases of the injected fuel have been acquired via visible and infrared imaging, respectively. Injection parameters, like penetration and cone angle have been determined and analyzed.

The interest of the vehicle producers in fulfillment emission legislations without adopting after treatment systems is driving to the use of non-conventional energy sources for modern engines. A previous test campaign dealing with the use of blends of diesel and propane in a CI engine has pointed out the potential of this non-conventional fuel for diesel engines. The soft adaptation of the common rail injection system and the potential benefits, in terms of engine performances and pollutant emissions, encourage the use of propane-diesel blends if an optimization of the injection strategies is performed. In this work, the performances of a propane-diesel mixture in a research diesel engine have been investigated. The injection strategies of Euro 5 calibration have been used as reference for the development of optimized strategies. The aim of the optimization process was to ensure the same engine power output and reduce the pollutant emissions.

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.

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.

This study was conducted to determine the effects of the fouling process on the piezoelectric injectors in a transparent common-rail diesel engine. Piezoelectric injectors are characterized by a ceramic actuator that can dilate or retract when it receives a pulse of current. The piezo element controls a valve, which creates an imbalance in the pressure that is exerted at each end of the needle, causing the needle rising or closing. Two same model injectors were tested; one was new and the other one was fouled on a vehicle. The aim of the experimental investigation was to evaluate the performance of a new and a fouled piezoelectric injector in terms of injection and flame evolution. It was evaluated how the nozzle carbon deposits affect the injection quantity and combustion. The experimental apparatus was a single-cylinder research engine equipped with a Euro 5 multi-cylinder head. A second-generation common rail injection system and 6-hole piezoelectric injectors were used too.

Blends of propane-diesel fuel can be used in direct injection diesel engines to improve the air-fuel mixing and the premixed combustion phase, and to reduce pollutant emissions. The potential benefits of usinf propane in diesel engines are both environmental and economic; furthermore, its use does not require changes to the compression ratio of conventional diesel engines. The present paper describes an experimental investigation of the injection process for different liquid preformed blends of propane-diesel fuel in an optically accessible Common Rail diesel engine. Slight modifications of the injection system were required in order to operate with a blend of propane-diesel fuel. Pure diesel fuel and two propane-diesel mixtures at different mass ratios were tested (20% and 40% in mass of propane named P20 and P40). First, injection in air at ambient temperature and atmospheric pressure were performed to verify the functionality of the modified Common Rail injection system.

The present paper describes the results of an experimental activity performed on a small diesel engine for quadricycles, a category of vehicles that is spreading in Europe and is recently spreading over Indian countries. The engine is a prototype three-cylinder with 1000 cc of displacement and it is equipped with a direct common-rail injection system that reaches a maximum pressure of 1400 bar. The engine was designed to comply with Euro 4 emission standard that is a future regulation for quadricycles. It is worth underlining that the engine can meet emission limits just with EGR system and a DOC, without DPF. Various diesel/RME blends were tested; pure diesel and biodiesel fuels were also used. The investigation was carried out at the engine speeds of 1400, 2000 and 3400 rpm and full load. Combustion characteristics of both blended and pure RME were analyzed by means of in-cylinder pressure and heat released histories.

In recent years, several studies on the efficiency of modern diesel engines have focused on the modeling of combustion process in its different phases. Here, analytical equations are used to describe the physical phenomena that occur in the cylinder. The good agreement between the experimental and simulated data could improve the predictive capabilities of the computational code and reduce the cost of experimental activities. For the modeling of a diesel spray, the first step has been to investigate its behavior in a non-combusting environment; in particular, Musculus and Kattke proposed a model for the simulation of the injection of fuel in non-reacting still environment. Starting from that knowledge, the authors apply the injection model to a compression ignition research engine. By means of an optical engine, injection phase has been investigated via 2D digital imaging. The main jet characteristics like penetration and dispersion angle have been measured.

This paper deals with the combustion characteristics and exhaust emissions of a diesel engine fuelled with conventional diesel fuel and a biodiesel blend, in particular a 20% v/v concentration of rapeseed methyl ester (RME) mixed with diesel fuel. The investigation was carried out on a prototype three-cylinder engine with 1000 cc of displacement for quadricycle applications. The engine is equipped with a direct common-rail injection system that reaches a maximum pressure of 1400 bar. The engine was designed to comply with Euro 4 and BS IV exhaust emission regulations without a diesel particulate filter. Both in-cylinder pressure and rate of heat release traces were analyzed at different engine speeds and loads. Gaseous emissions were measured at the exhaust. A smoke meter was used to measure the particulate matter concentration. The sizing and the counting of the particles were performed by means of an engine exhaust particle sizer spectrometer.