A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

Costa, Marco (2013) A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Meccanica e scienze avanzate dell'ingegneria: progetto n. 2 "Ingegneria delle macchine e dei sistemi energetici", 25 Ciclo. DOI 10.6092/unibo/amsdottorato/5688.
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DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Tipologia del documento
Tesi di dottorato
Costa, Marco
Dottorato di ricerca
Scuola di dottorato
Ingegneria industriale
Settore disciplinare
Settore concorsuale
Parole chiave
multi objective optimization, genetic algorithm, neural network, Diesel
Data di discussione
25 Giugno 2013

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