Sviluppo di algoritmi avanzati di analisi e diagnosi combustione in tempo reale per motori endotermici alternativi

Increasingly stringent exhaust emission limits and higher fuel economy are the main drivers of the engine development process. As a consequence, the complexity of the propulsion units and its subsystems increase, due to the extensive use of sensors and actuators needed to obtain a precise control over the combustion phase. Since engine calibration process consumes most of the development time, new tools and methodologies are needed to shorten the development time and increase the performance attainable. Real time combustion analysis, based on the in-cylinder pressure signal, can significantly improve the calibration of the engine control strategies and the development of new algorithms, giving instantaneous feedback on the engine behavior. A complete combustion analysis and diagnosis system has been developed, capable of evaluating the most important indicators about the combustion process, such as indicated mean effective pressure, heat release, mass fraction burned and knock indexes. Such a tool is built on top of a flexible, modular and affordable hardware platform, capable of satisfying the requirements needed for accuracy and precision, but also enabling the use directly on-board the vehicle, due to its small form factor.

Application of innovative methods of source apportionment in air contamination assessment

In this work, new tools in atmospheric pollutant sampling and analysis were applied in order to go deeper in source apportionment study. The project was developed mainly by the study of atmospheric emission sources in a suburban area influenced by a municipal solid waste incinerator (MSWI), a medium-sized coastal tourist town and a motorway. Two main research lines were followed. For what concerns the first line, the potentiality of the use of PM samplers coupled with a wind select sensor was assessed. Results showed that they may be a valid support in source apportionment studies. However, meteorological and territorial conditions could strongly affect the results. Moreover, new markers were investigated, particularly focusing on the processes of biomass burning. OC revealed a good biomass combustion process indicator, as well as all determined organic compounds. Among metals, lead and aluminium are well related to the biomass combustion. Surprisingly PM was not enriched of potassium during bonfire event. The second research line consists on the application of Positive Matrix factorization (PMF), a new statistical tool in data analysis. This new technique was applied to datasets which refer to different time resolution data. PMF application to atmospheric deposition fluxes identified six main sources affecting the area. The incinerator’s relative contribution seemed to be negligible. PMF analysis was then applied to PM2.5 collected with samplers coupled with a wind select sensor. The higher number of determined environmental indicators allowed to obtain more detailed results on the sources affecting the area. Vehicular traffic revealed the source of greatest concern for the study area. Also in this case, incinerator’s relative contribution seemed to be negligible. Finally, the application of PMF analysis to hourly aerosol data demonstrated that the higher the temporal resolution of the data was, the more the source profiles were close to the real one.

Modellazione Tridimensionale del Processo di Combustione di un Razzo a Propellente Solido

La regolazione dei sistemi di propulsione a razzo a propellente solido (Solid Rocket Motors) ha da sempre rappresentato una delle principali problematiche legate a questa tipologia di motori. L’assenza di un qualsiasi genere di controllo diretto del processo di combustione del grano solido, fa si che la previsione della balistica interna rappresenti da sempre il principale strumento utilizzato sia per definire in fase di progetto la configurazione ottimale del motore, sia per analizzare le eventuali anomalie riscontrate in ambito sperimentale. Variazioni locali nella struttura del propellente, difettosità interne o eterogeneità nelle condizioni di camera posso dare origine ad alterazioni del rateo locale di combustione del propellente e conseguentemente a profili di pressione e di spinta sperimentali differenti da quelli previsti per via teorica. Molti dei codici attualmente in uso offrono un approccio piuttosto semplificato al problema, facendo per lo più ricorso a fattori correttivi (fattori HUMP) semi-empirici, senza tuttavia andare a ricostruire in maniera più realistica le eterogeneità di prestazione del propellente. Questo lavoro di tesi vuole dunque proporre un nuovo approccio alla previsione numerica delle prestazioni dei sistemi a propellente solido, attraverso la realizzazione di un nuovo codice di simulazione, denominato ROBOOST (ROcket BOOst Simulation Tool). Richiamando concetti e techiche proprie della Computer Grafica, questo nuovo codice è in grado di ricostruire in processo di regressione superficiale del grano in maniera puntuale, attraverso l’utilizzo di una mesh triangolare mobile. Variazioni locali del rateo di combustione posso quindi essere facilmente riprodotte ed il calcolo della balistica interna avviene mediante l’accoppiamento di un modello 0D non-stazionario e di uno 1D quasi-stazionario. L’attività è stata svolta in collaborazione con l’azienda Avio Space Division e il nuovo codice è stato implementato con successo sul motore Zefiro 9.

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

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.