Valutazione del contributo emissivo in condizioni non-stazionarie alle emissioni totali di una stufa a pellet domestica

The aim of this work is to evaluate the emissions of the main pollutants of a pellet stove, by trying to simulate the real use in domestic operations. All the operating phases of this system were considered: ignition, partial load, increase in power, and nominal load. In each phase, quantity and type of some pollutants in emissions were determined: the main pollutant gases (CO, NOx, SO2, H2S and volatile organic compounds (VOCs)), total dust (PM) and its content of polycyclic aromatic hydrocarbons (PAHs), regulated heavy metals (Ni, Cd, As and Pb), main soluble ions and Total Carbon (TC). Results show that emission factors of TSP, CO, and of the main determined pollutants (TC, Cd and PAHs) are higher during ignition phase. In particular, this phase prevalently contributes to PAHs emissions. During increase in power phase, gas and particulate emissions do not appreciably differ from nominal load ones; nevertheless, PAH emission factors are higher than steady state ones, but lower than ignition phase. Moreover, during not-steady state phases, PAH mixture is more toxic than during steady state phases. In conclusion, this study allowed to go deeper in pellet stove environmental impact, by pointing out how the different operating conditions can modify the emissions. These are different from certificated data, which are based exclusively on measurements in steady state conditions.

Studio e ottimizzazione di un sistema automatico per il cambiamento del setup in un motoveicolo sportivo

In the racing field the possibility to change the suspension settings can improve the overall performance of motorcycles, adapting to any type of circuit, any driving style and any weather condition, increasing the feeling of the rider with the vehicle. The present study investigated the pressure and forces related to changes in the oil level inside of the front fork. Seeing the importance of the change of the oil level have been developed an automated device, to be installed in the forks of original sports motorcycles, with the function to vary the level of oil in an automatic way. This system, having the possibility to continuously change the partial setup, could allow the optimization of the forks in each sector of the track, through a unit that automates the change. The project of the system has been presented to teams and riders of national championships and they showed interest on it.

Analisi di morfologie carsiche o crio-carsiche in relazione alla mobilizzazione di fluidi nella regione di Arabia Terra, Marte.

This thesis tries to interpret the origin and evolution of karst-like forms present in Arabia Terra, a region of Mars that develops in the equatorial zone of the planet. The work has been carried out specifically in the craters Crommelin (4o 91’ N-10o 51’ E), 12000088 (3o 48’ N-1o 30’ E), NE 12000088 (4° 20’ N-2° 50’ E), C "2" (3° 54’ N-1° W), and in their surrounding areas. These craters contain layered deposits characterized by a high albedo and on which erosion is very pronounced. The area containing the craters is a plateau that has the same characteristics of albedo and texture. The preliminary morphological study has made use of instrumentation such as the Mars Reconnaissance Orbiter (MRO), in particular HiRISE images (High Resolution Imaging Science Experiment), CTX (Context Camera) and CRISM (Compact Reconnaissance Imaging Spectrometers for Mars). A regional geomorphological map has been drawn up containing the main morphotypes, and detailed geomorphological maps were prepared for different karst-like morphologies. The analysis of spectral data collected from CRISM instrumentation has allowed to identify the footprint of sulphate minerals in the external area. Data were collected for morphometric negative forms (karst-like) and positive forms (mud volcanoes, dikes and pingos). For the analysis of the relief forms DTMs (Digital Terrain Models) produced by the union of stereographic CTX couples or HiRISE were used. From the analysis of high-resolution images morphological footprints similar to periglacial environments have been identified, including the presence of patterned ground and polygonal cracks found all over the area of investigation, and relief structures similar to pingos present in the crater C "2". These observations allow us to imagine a geological past with a cold climate at the equator able to freeze the few fluids present in the Martian arid terrain. The development of karst-like landforms, on the other hand, can be attributed to a subsequent improval of the weather conditions that led to a normal climate regime for the equatorial areas, resulting in the degradation of the permafrost. The melt waters have thus allowed the partial dissolution of the sulphate layers. The karst-like forms look rather fresh suggesting them to be not that old.

Applications of elliptic functions to solve differential equations

In questa tesi si mostrano alcune applicazioni degli integrali ellittici nella meccanica Hamiltoniana, allo scopo di risolvere i sistemi integrabili. Vengono descritte le funzioni ellittiche, in particolare la funzione ellittica di Weierstrass, ed elenchiamo i tipi di integrali ellittici costruendoli dalle funzioni di Weierstrass. Dopo aver considerato le basi della meccanica Hamiltoniana ed il teorema di Arnold Liouville, studiamo un esempio preso dal libro di Moser-Integrable Hamiltonian Systems and Spectral Theory, dove si prendono in considerazione i sistemi integrabili lungo la geodetica di un'ellissoide, e il sistema di Von Neumann. In particolare vediamo che nel caso n=2 abbiamo un integrale ellittico.

Radiotherapy with scanning carbon ion beams: biological dose analysis for partial treatment delivery

L’uso di particelle cariche pesanti in radioterapia prende il nome di adroterapia. L’adroterapia permette l’irraggiamento di un volume bersaglio minimizzando il danno ai tessuti sani circostanti rispetto alla radioterapia tradizionale a raggi X. Le proprietà radiobiologiche degli ioni carbonio rappresentano un problema per i modelli radiobiologici a causa della non linearità della loro efficacia biologica. In questa tesi presenteremo gli algoritmi che possono essere usati per calcolare la dose fisica e biologica per un piano di trattamento del CNAO (Centro Nazionale Adroterapia Oncologica). Un caso di particolare interesse è l’eventualità che un piano di trattamento venga interrotto prima del dovuto. A causa della non linearità della sopravvivenza cellulare al variare della quantità di dose ricevuta giornalmente, è necessario studiare gli effetti degli irraggiamenti parziali utilizzando algoritmi che tengano conto delle tante variabili che caratterizzano sia i fasci di ioni che i tessuti irraggiati. Nell'ambito di questa tesi, appositi algoritmi in MATLAB sono stati sviluppati e implementati per confrontare la dose biologica e fisica assorbita nei casi di trattamento parziale.

Stochastic dynamics on infinite and finite transport capacity networks: an operatorial approach

Nella tesi viene studiata la dinamica stocastica di particelle non interagenti su network con capacita di trasporto finita. L'argomento viene affrontato introducendo un formalismo operatoriale per il sistema. Dopo averne verificato la consistenza su modelli risolvibili analiticamente, tale formalismo viene impiegato per dimostrare l'emergere di una forza entropica agente sulle particelle, dovuta alle limitazioni dinamiche del network. Inoltre viene proposta una spiegazione qualitativa dell'effetto di attrazione reciproca tra nodi vuoti nel caso di processi sincroni.

Eluding oblivion with smart stochastic selection of synaptic updates

The variables involved in the equations that describe realistic synaptic dynamics always vary in a limited range. Their boundedness makes the synapses forgetful, not for the mere passage of time, but because new experiences overwrite old memories. The forgetting rate depends on how many synapses are modified by each new experience: many changes means fast learning and fast forgetting, whereas few changes means slow learning and long memory retention. Reducing the average number of modified synapses can extend the memory span at the price of a reduced amount of information stored when a new experience is memorized. Every trick which allows to slow down the learning process in a smart way can improve the memory performance. We review some of the tricks that allow to elude fast forgetting (oblivion). They are based on the stochastic selection of the synapses whose modifications are actually consolidated following each new experience. In practice only a randomly selected, small fraction of the synapses eligible for an update are actually modified. This allows to acquire the amount of information necessary to retrieve the memory without compromising the retention of old experiences. The fraction of modified synapses can be further reduced in a smart way by changing synapses only when it is really necessary, i.e. when the post-synaptic neuron does not respond as desired. Finally we show that such a stochastic selection emerges naturally from spike driven synaptic dynamics which read noisy pre and post-synaptic neural activities. These activities can actually be generated by a chaotic system.

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

Roy-Steiner equations for piN scattering

In this talk, we present a coupled system of integral equations for the πN → πN (s-channel) and ππ → N̅N (t-channel) lowest partial waves, derived from Roy–Steiner equations for pion–nucleon scattering. After giving a brief overview of this system of equations, we present the solution of the t-channel sub-problem by means of Muskhelishvili–Omnès techniques, and solve the s-channel sub-problem after finding a set of phase shifts and subthreshold parameters which satisfy the Roy–Steiner equations.

Functional equations and the Cauchy mean value theorem

The aim of this note is to characterize all pairs of sufficiently smooth functions for which the mean value in the Cauchy mean value theorem is taken at a point which has a well-determined position in the interval. As an application of this result, a partial answer is given to a question posed by Sahoo and Riedel.

Explicit and Implicit Methods In Solving Differential Equations

Differential equations are equations that involve an unknown function and derivatives. Euler's method are efficient methods to yield fairly accurate approximations of the actual solutions. By manipulating such methods, one can find ways to provide good approximations compared to the exact solution of parabolic partial differential equations and nonlinear parabolic differential equations.

Numerical simulation of group combustion of pulverized coal

A mathematical model for the group combustion of pulverized coal particles was developed in a previous work. It includes the Lagrangian description of the dehumidification, devolatilization and char gasification reactions of the coal particles in the homogenized gaseous environment resulting from the three fuels, CO, H2 and volatiles, supplied by the gasification of the particles and their simultaneous group combustion by the gas phase oxidation reactions, which are considered to be very fast. This model is complemented here with an analysis of the particle dynamics, determined principally by the effects of aerodynamic drag and gravity, and its dispersion based on a stochastic model. It is also extended to include two other simpler models for the gasification of the particles: the first one for particles small enough to extinguish the surrounding diffusion flames, and a second one for particles with small ash content when the porous shell of ashes remaining after gasification of the char, non structurally stable, is disrupted. As an example of the applicability of the models, they are used in the numerical simulation of an experiment of a non-swirling pulverized coal jet with a nearly stagnant air at ambient temperature, with an initial region of interaction with a small annular methane flame. Computational algorithms for solving the different stages undergone by a coal particle during its combustion are proposed. For the partial differential equations modeling the gas phase, a second order finite element method combined with a semi-Lagrangian characteristics method are used. The results obtained with the three versions of the model are compared among them and show how the first of the simpler models fits better the experimental results.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Models for stochastic climate prediction

There has been a recent burst of activity in the atmosphere/ocean sciences community in utilizing stable linear Langevin stochastic models for the unresolved degree of freedom in stochastic climate prediction. Here several idealized models for stochastic climate modeling are introduced and analyzed through unambiguous mathematical theory. This analysis demonstrates the potential need for more sophisticated models beyond stable linear Langevin equations. The new phenomena include the emergence of both unstable linear Langevin stochastic models for the climate mean and the need to incorporate both suitable nonlinear effects and multiplicative noise in stochastic models under appropriate circumstances. The strategy for stochastic climate modeling that emerges from this analysis is illustrated on an idealized example involving truncated barotropic flow on a beta-plane with topography and a mean flow. In this example, the effect of the original 57 degrees of freedom is well represented by a theoretically predicted stochastic model with only 3 degrees of freedom.