Development of numerical methodologies to predict the liquid fuel sprays - wall interaction to optimize the mixing process of direct injection spark ignition engines

Nowadays the development of new Internal Combustion Engines is mainly driven by the need to reduce tailpipe emissions of pollutants, Green-House Gases and avoid the fossil fuels wasting. The design of dimension and shape of the combustion chamber together with the implementation of different injection strategies e.g., injection timing, spray targeting, higher injection pressure, play a key role in the accomplishment of the aforementioned targets. As far as the match between the fuel injection and evaporation and the combustion chamber shape is concerned, the assessment of the interaction between the liquid fuel spray and the engine walls in gasoline direct injection engines is crucial. The use of numerical simulations is an acknowledged technique to support the study of new technological solutions such as the design of new gasoline blends and of tailored injection strategies to pursue the target mixture formation. The current simulation framework lacks a well-defined best practice for the liquid fuel spray interaction simulation, which is a complex multi-physics problem. This thesis deals with the development of robust methodologies to approach the numerical simulation of the liquid fuel spray interaction with walls and lubricants. The accomplishment of this task was divided into three tasks: i) setup and validation of spray-wall impingement three-dimensional CFD spray simulations; ii) development of a one-dimensional model describing the liquid fuel – lubricant oil interaction; iii) development of a machine learning based algorithm aimed to define which mixture of known pure components mimics the physical behaviour of the real gasoline for the simulation of the liquid fuel spray interaction.

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.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

The rupture process of recent tsunamigenic earthquakes by geophysical data inversion

Subduction zones are the favorite places to generate tsunamigenic earthquakes, where friction between oceanic and continental plates causes the occurrence of a strong seismicity. The topics and the methodologies discussed in this thesis are focussed to the understanding of the rupture process of the seismic sources of great earthquakes that generate tsunamis. The tsunamigenesis is controlled by several kinematical characteristic of the parent earthquake, as the focal mechanism, the depth of the rupture, the slip distribution along the fault area and by the mechanical properties of the source zone. Each of these factors plays a fundamental role in the tsunami generation. Therefore, inferring the source parameters of tsunamigenic earthquakes is crucial to understand the generation of the consequent tsunami and so to mitigate the risk along the coasts. The typical way to proceed when we want to gather information regarding the source process is to have recourse to the inversion of geophysical data that are available. Tsunami data, moreover, are useful to constrain the portion of the fault area that extends offshore, generally close to the trench that, on the contrary, other kinds of data are not able to constrain. In this thesis I have discussed the rupture process of some recent tsunamigenic events, as inferred by means of an inverse method. I have presented the 2003 Tokachi-Oki (Japan) earthquake (Mw 8.1). In this study the slip distribution on the fault has been inferred by inverting tsunami waveform, GPS, and bottom-pressure data. The joint inversion of tsunami and geodetic data has revealed a much better constrain for the slip distribution on the fault rather than the separate inversions of single datasets. Then we have studied the earthquake occurred on 2007 in southern Sumatra (Mw 8.4). By inverting several tsunami waveforms, both in the near and in the far field, we have determined the slip distribution and the mean rupture velocity along the causative fault. Since the largest patch of slip was concentrated on the deepest part of the fault, this is the likely reason for the small tsunami waves that followed the earthquake, pointing out how much the depth of the rupture plays a crucial role in controlling the tsunamigenesis. Finally, we have presented a new rupture model for the great 2004 Sumatra earthquake (Mw 9.2). We have performed the joint inversion of tsunami waveform, GPS and satellite altimetry data, to infer the slip distribution, the slip direction, and the rupture velocity on the fault. Furthermore, in this work we have presented a novel method to estimate, in a self-consistent way, the average rigidity of the source zone. The estimation of the source zone rigidity is important since it may play a significant role in the tsunami generation and, particularly for slow earthquakes, a low rigidity value is sometimes necessary to explain how a relatively low seismic moment earthquake may generate significant tsunamis; this latter point may be relevant for explaining the mechanics of the tsunami earthquakes, one of the open issues in present day seismology. The investigation of these tsunamigenic earthquakes has underlined the importance to use a joint inversion of different geophysical data to determine the rupture characteristics. The results shown here have important implications for the implementation of new tsunami warning systems – particularly in the near-field – the improvement of the current ones, and furthermore for the planning of the inundation maps for tsunami-hazard assessment along the coastal area.