Un modello spazialmente distribuito per la modellazione fisicamente basata del contributo locale al trasporto solido in sospensione in alvei fluviali

La presente tesi di dottorato si propone lo sviluppo di un modello spazialmente distribuito per produrre una stima dell'erosione superficiale in bacini appenninici. Il modello è stato progettato per simulare in maniera fisicamente basata il distacco di suolo e di sedimento depositato ad opera delle precipitazioni e del deflusso superficiale, e si propone come utile strumento per lo studio della vulnerabilità del territorio collinare e montano. Si è scelto un bacino collinare dell'Appennino bolognese per testare le capacità del modello e verificarne la robustezza. Dopo una breve introduzione per esporre il contesto in cui si opera, nel primo capitolo sono presentate le principali forme di erosione e una loro descrizione fisico-matematica, nel secondo capitolo verranno introdotti i principali prodotti della modellistica di erosione del suolo, spiegando quale interpretazione dei fenomeni fisici è stata data. Nel terzo capitolo verrà descritto il modello oggetto della tesi di dottorando, con una prima breve descrizione della componente afflussi-deflussi ed una seconda descrizione della componente di erosione del suolo. Nel quarto capitolo verrà descritto il bacino di applicazione del modello, i risultati della calibrazione ed un'analisi di sensitività. Infine si presenteranno le conclusioni sullo studio.

Ricostruzione degli sforzi in elementi finiti di piastra

Nell’ambito dell’analisi computazionale delle strutture il metodo degli elementi finiti è probabilmente uno dei metodi numerici più efficaci ed impiegati. La semplicità dell’idea di base del metodo e la relativa facilità con cui può essere implementato in codici di calcolo hanno reso possibile l’applicazione di questa tecnica computazionale in diversi settori, non solo dell’ingegneria strutturale, ma in generale della matematica applicata. Ma, nonostante il livello raggiunto dalle tecnologie ad elementi finiti sia già abbastanza elevato, per alcune applicazioni tipiche dell’ingegneria strutturale (problemi bidimensionali, analisi di lastre inflesse) le prestazioni fornite dagli elementi usualmente utilizzati, ovvero gli elementi di tipo compatibile, sono in effetti poco soddisfacenti. Vengono in aiuto perciò gli elementi finiti basati su formulazioni miste che da un lato presentano una più complessa formulazione, ma dall’altro consentono di prevenire alcuni problemi ricorrenti quali per esempio il fenomeno dello shear locking. Indipendentemente dai tipi di elementi finiti utilizzati, le quantità di interesse nell’ambito dell’ingegneria non sono gli spostamenti ma gli sforzi o più in generale le quantità derivate dagli spostamenti. Mentre i primi sono molto accurati, i secondi risultano discontinui e di qualità scadente. A valle di un calcolo FEM, negli ultimi anni, hanno preso piede procedure di post-processing in grado, partendo dalla soluzione agli elementi finiti, di ricostruire lo sforzo all’interno di patch di elementi rendendo quest’ultimo più accurato. Tali procedure prendono il nome di Procedure di Ricostruzione (Recovery Based Approaches). Le procedure di ricostruzione qui utilizzate risultano essere la REP (Recovery by Equilibrium in Patches) e la RCP (Recovery by Compatibility in Patches). L’obbiettivo che ci si prefigge in questo lavoro è quello di applicare le procedure di ricostruzione ad un esempio di piastra, discretizzato con vari tipi di elementi finiti, mettendone in luce i vantaggi in termini di migliore accurattezza e di maggiore convergenza.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

Multiresolution spherical wavelet analysis in global seismic tomography

Every seismic event produces seismic waves which travel throughout the Earth. Seismology is the science of interpreting measurements to derive information about the structure of the Earth. Seismic tomography is the most powerful tool for determination of 3D structure of deep Earth's interiors. Tomographic models obtained at the global and regional scales are an underlying tool for determination of geodynamical state of the Earth, showing evident correlation with other geophysical and geological characteristics. The global tomographic images of the Earth can be written as a linear combinations of basis functions from a specifically chosen set, defining the model parameterization. A number of different parameterizations are commonly seen in literature: seismic velocities in the Earth have been expressed, for example, as combinations of spherical harmonics or by means of the simpler characteristic functions of discrete cells. With this work we are interested to focus our attention on this aspect, evaluating a new type of parameterization, performed by means of wavelet functions. It is known from the classical Fourier theory that a signal can be expressed as the sum of a, possibly infinite, series of sines and cosines. This sum is often referred as a Fourier expansion. The big disadvantage of a Fourier expansion is that it has only frequency resolution and no time resolution. The Wavelet Analysis (or Wavelet Transform) is probably the most recent solution to overcome the shortcomings of Fourier analysis. The fundamental idea behind this innovative analysis is to study signal according to scale. Wavelets, in fact, are mathematical functions that cut up data into different frequency components, and then study each component with resolution matched to its scale, so they are especially useful in the analysis of non stationary process that contains multi-scale features, discontinuities and sharp strike. Wavelets are essentially used in two ways when they are applied in geophysical process or signals studies: 1) as a basis for representation or characterization of process; 2) as an integration kernel for analysis to extract information about the process. These two types of applications of wavelets in geophysical field, are object of study of this work. At the beginning we use the wavelets as basis to represent and resolve the Tomographic Inverse Problem. After a briefly introduction to seismic tomography theory, we assess the power of wavelet analysis in the representation of two different type of synthetic models; then we apply it to real data, obtaining surface wave phase velocity maps and evaluating its abilities by means of comparison with an other type of parametrization (i.e., block parametrization). For the second type of wavelet application we analyze the ability of Continuous Wavelet Transform in the spectral analysis, starting again with some synthetic tests to evaluate its sensibility and capability and then apply the same analysis to real data to obtain Local Correlation Maps between different model at same depth or between different profiles of the same model.