Thermal structure and dynamical modeling of a Mediterranean tropical-like cyclone

In the present work, a detailed analysis of a Mediterranean TLC occurred in January 2014 has been conducted. The author is not aware of other studies regarding this particular event at the publication of this thesis. In order to outline the cyclone evolution, observational data, including weather-stations data, satellite data, radar data and photographic evidence, were collected at first. After having identified the cyclone path and its general features, the GLOBO, BOLAM and MOLOCH NWP models, developed at ISAC-CNR (Bologna), were used to simulate the phenomenon. Particular attention was paid on the Mediterranean phase as well as on the Atlantic phase, since the cyclone showed a well defined precursor up to 3 days before the minimum formation in the Alboran Sea. The Mediterranean phase has been studied using different combinations of GLOBO, BOLAM and MOLOCH models, so as to evaluate the best model chain to simulate this kind of phenomena. The BOLAM and MOLOCH models showed the best performance, by adjusting the path erroneously deviated in the National Centre for Environmental Prediction (NCEP) and ECMWF operational models. The analysis of the cyclone thermal phase shown the presence of a deep-warm core structure in many cases, thus confirming the tropical-like nature of the system. Furthermore, the results showed high sensitivity to initial conditions in the whole lifetime of the cyclone, while the Sea Surface Temperature (SST) modification leads only to small changes in the Adriatic phase. The Atlantic phase has been studied using GLOBO and BOLAM model and with the aid of the same methodology already developed. After tracing the precursor, in the form of a low-pressure system, from the American East Coast to Spain, the thermal phase analysis was conducted. The parameters obtained showed evidence of a deep-cold core asymmetric structure during the whole Atlantic phase, while the first contact with the Mediterranean Sea caused a sudden transition to a shallow-warm core structure. The examination of Potential Vorticity (PV) 3-dimensional structure revealed the presence of a PV streamer that individually formed over Greenland and eventually interacted with the low-pressure system over the Spanish coast, favouring the first phase of the cyclone baroclinic intensification. Finally, the development of an automated system that tracks and studies the thermal phase of Mediterranean cyclones has been encouraged. This could lead to the forecast of potential tropical transition, against with a minimum computational investment.

Physical characterization and numerical modeling of a WEST-like tokamak divertor plasma

Il presente elaborato è incentrato sulla modellizzazione del plasma di bordo nei dispositivi per la produzione di energia da fusione nucleare noti come tokamak. La tecnologia che nel corso di tutta la seconda metà del XX secolo fino ad oggi è stata sviluppata a questo fine deve necessariamente scontrarsi con alcuni limiti. Nei tokamak il confinamento del plasma è di tipo magnetico e vincola le particelle a muoversi di moto elicoidale all'interno del vessel, tuttavia il confinamento non risulta perfetto e parte dell'energia si scarica sulle pareti della camera, rischiando pertanto di fondere i materiali. Alcune strategie possono essere messe in atto per limitare questo problema, per esempio agendo sulla geometria del tokamak, oppure sulla fisica, inducendo nel plasma una data concentrazione di impurezze che ionizzino irraggiando parte dell'energia di plasma. Proprio tale meccanismo di perdita è stato simulato in un modello monodimensionale di plasma monofluido di bordo. I risultati del codice numerico relativo al modello dimostrano che per concentrazioni di impurezze crescenti è possibile diminuire in modo significativo flusso di calore e temperatura al divertore. Per di più risulta possibile controllare la posizione del fronte di irraggiamento per mezzo di parametri di controllo del plasma quali la pressione. Si osserva inoltre l'insorgere del cosiddetto fenomeno di biforcazione alle basse temperature di divertore, fenomeno in cui il plasma si comporta in modo instabile a causa di fenomeni fisici tipici delle basse energie ("detachment") e a seguito del quale può improvvisamente spegnersi (disruzione). Infine lo stesso modello è stato migliorato inserendo l'ipotesi di plasma bifluido. Anche per gli ioni viene osservato il fenomeno di biforcazione. I risultati numerici evidenziano le dinamiche dello scambio energetico fra le specie gettando le basi di una progettazione efficiente della chimica del plasma finalizzata al raffreddamento del divertore.

Theoretical and experimental studies of gold catalysts for glucose oxidation

The glucaric acid (GLA) has been identified as a “top value-added chemical from biomass” that can be employed for many uses; for instance, it could be a precursor of adipic acid, a monomer of Nylon-6,6. GLA can be synthetized by the oxidation of glucose (GLU), passing through the intermediate gluconic acid (GLO). In recent years, a new process has been sought to obtain GLA in an economic and environmental sustainable way, in order to replace the current use of HNO3 as a stoichiometric oxidant, or electrocatalysis and biochemical synthesis, which show several disadvantages. Thereby, this work is focused on the study of catalysts based on gold nanoparticles supported on activated carbon for the oxidation reaction of GLU to GLA using O2 as an oxidant agent and NaOH as base. The sol-immobilization method leads us to obtain small and well dispersed nanoparticles, characterized by UV-Vis, XRD and TEM techniques. Repeating the reaction on different batches of catalyst, both the synthesis and the reaction were confirmed to be reproducible. The effect of the reaction time feeding GLO as reagent was studied: the results show that the conversion of GLO increases as the reaction time increases; however, the yields of GLA and others increase up to 1 hour, and then they remain constant. In order to obtain information on the catalytic mechanism at the atomistic level, a computational study based on density functional theory and atomistic modeling of the gold nano-catalyst were performed. Highly symmetric (icosahedral and cubo-octahedral) and distorted Au55 nanoparticles have been optimized along with Au(111) and Au(100) surfaces. Distorted structures were found to be more stable than symmetrical ones due to relativistic effects. On these various models the adsorptions of various species involved in the catalysis have been studied, including OH- species, GLU and GLO. The study carried out aims to provide a method for approaching to the study of nanoparticellary catalytic systems.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.