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Journal Article

Experimental and Numerical Evaluation of Diesel Spray Momentum Flux

2009-11-02
2009-01-2772
In the present work, an experimental and numerical analysis of high pressure Diesel spray evolution is carried out in terms of spray momentum flux time history and instantaneous injection rate. The final goal of spray momentum and of injection rate analyses is the evaluation of the nozzle outlet flow characteristics and of the nozzle internal geometry possible influences on cavitation phenomena, which are of primary importance for the spray evolution. Further, the evaluation of the flow characteristics at the nozzle exit is fundamental in order to obtain reliable boundary conditions for injection process 3D simulation. In this paper, spray momentum data obtained in ambient temperature, high counter-pressure conditions at the Perugia University Spray Laboratory are presented and compared with the results of 3D simulations of the momentum rig itself.
Journal Article

A Parametric Optimization Study of a Hydraulic Valve Actuation System

2008-04-14
2008-01-1356
A new camless system (referred to as Hydraulic Valve Control - HVC - system) is in an advanced state of prototyping and development. The present paper aims to support the new incoming activities concerning the possible modifications to the geometrical and mechanical characteristics of the system. The optimization of the new HVC system prototype is done using a multi-objective tool that integrates the hydraulic/mechanical simulator reproducing the physical model, with an optimization software. The latter tool can be used choosing a specific approach among different probabilistic mathematical models; the Genetic Algorithm approach was chosen to achieve the goal of the present study. The paper describes design optimization of the pilot stage of the actuator for given characteristics of the power stage and of the poppet valve.
Technical Paper

Engine Knock Evaluation Using a Machine Learning Approach

2020-09-27
2020-24-0005
Artificial Intelligence is becoming very important and useful in several scientific fields. Machine learning methods, such as neural networks and decision trees, are often proposed in applications for internal combustion engines as virtual sensors, faults diagnosis systems and engine performance optimization. The high pressure of the intake air coupled with the demand of lean conditions, in order to reduce emissions, have often close relationship with the knock events. Fuels autoignition characteristics and flame front speed have a significant impact on knock phenomenon, producing high internal cylinder pressures and engine faults. The limitations in using pressure sensors in the racing field and the challenge to reduce the costs of commercial cars, push the replacement of a hardware redundancy with a software redundancy.
Technical Paper

Artificial Intelligence Methodologies for Oxygen Virtual Sensing at Diesel Engine Intake

2012-04-16
2012-01-1153
In the last decades, worldwide automotive regulations induced the industry to dramatically increase the application of electronics in the control of the engine and of the pollutant emissions reduction systems. Besides the need of engine control, suitable fault diagnosis tools had also to be developed, in order to fulfil OBD-II and E-OBD requirements. At present, one of the problems in the development of Diesel engines is represented by the achievement of an ever more sharp control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but an ever higher efficiency in terms of emissions abatement, requires to determine as better as possible the actual oxygen content in the charge at the engine intake manifold, also in dynamic conditions, i.e. in transient engine operation.
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