Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world.

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.

Study of the genes involved in Naphthenic acids (NAs) degradation by Rhodococcus spp.

Naphthenic acids (NAs) are an important group of organic pollutants mainly found in hydrocarbon deposits. Although these compounds are toxic, recalcitrant, and persistent in the environment, we are just learning the diversity of microbial communities involved in NAs- degradation and the mechanisms by which NAs are biodegraded. Studies have shown that naphthenic acids are susceptible to biodegradation, which decreases their concentration and reduces toxicity. Nevertheless, little is still known about their biodegradability. The present PhD Thesis’s work is aimed to study the biodegradation of simple model NAs using bacteria strains belonging to the Rhodococcus genus. In particular, Rh. sp. BCP1 and Rh. opacus R7 were able to utilize NAs such as cyclohexane carboxylic acid and cyclopentane carboxylic acid as the sole carbon and energy sources, even at concentrations up to 1000 mg/L. The presence of either substituents or longer carboxylic acid chains attached to the cyclohexane ring negatively affected the growth by pure bacterial cultures. Moreover, BCP1 and R7 cells incubated in the presence of CHCA or CPCA show a general increase of saturated and methyl-substituted fatty acids in their membrane, while the cis-mono-unsaturated ones decrease, as compared to glucose-grown cells. The observed lipid molecules modification during the growth in the presence of NAs is suggested as a possible mechanism to decrease the fluidity of the cell membrane to counteract NAs toxicity. In order to further evaluate this toxic effect on cell features, the morphological changes of BCP1 and R7 cells were also assessed through Transmission Electron Microscopy (TEM), revealing interesting ultrastructural changes. The induction of putative genes, and the construction of a random transposon mutagenesis library were also carried out to reveal the mechanisms by which these Rhodococcus strains can degrade toxic compounds such as NAs.