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Technical Paper

Development and Application of S.I. Combustion Models for Emissions Prediction

The s.i. combustion process and its corresponding pollutant formation are investigated by means of a quasiD approach and a CFD model. This work has been motivated by the need to better understand the reliability of such models and to assess their accuracies with respect to the prediction of engine performances and emissions. An extended dissertation about the fundamental mechanisms governing the pollutant formation in the turbulent premixed combustion which characterizes the s.i. engines is given. The conclusion of such analysis is the definition of a new reduced chemical scheme, based on the application of partial-equilibrium and steady-state assumptions for the radicals and the solution of a transport equation for each specie which is kinetically controlled. For this purpose the CFD code OpenFOAM [1, 2, 3] and the thermo-fluid dynamic code GASDYN [4, 5] have been applied and enhanced.
Technical Paper

Application of Derivative-Free Search Algorithms for Performance Optimization of Spark Ignition Engines

This paper exploits the possibilities of achieving an efficient performance optimization methodology to be applied to different spark ignition engine configurations. The objective of the task described here is to determine the combination of parameters which provides the highest volumetric efficiency and effective torque. The definition of general strategy requires first the identification and grouping of the geometric and operating variables to be optimized (duct diameters and lengths, valve timing, spark advance, etc…). The high number of possibilities entails critical choices to reduce, from an engineering design point of view before than from a mathematical point of view, the required computational time. Once proper thermo-fluid dynamic decisions are taken, the most efficient optimization methodology is required. The application of Design of Experiments techniques allows to screen the design space and give a first estimation of the optimal point.