Applicability of URANS and DES simulations of flow past rectangular cylinders and bridge sections

This paper discusses the results of computational fluid dynamics simulations carried out for rectangular cylinders with various side ratios of interest for many civil engineering structures. A bridge deck of common cross-section geometry was also considered. Unsteady Reynolds-averaged Navier–Stokes (URANS) equations were solved in conjunction with either an eddy viscosity or a linearized explicit algebraic Reynolds stress model. The analysis showed that for the case studies considered, the 2D URANS approach was able to give reasonable results if coupled with an advanced turbulence model and a suitable computational mesh. The simulations even reproduced, at least qualitatively, complex phenomena observed in the wind tunnel, such as Reynolds number effects for a sharp-edged geometry. The study focused both on stationary and harmonically oscillating bodies. For the latter, self-excited forces and flutter derivatives were calculated and compared to experimental data. In the particular case of a benchmark rectangular 5:1 cylinder, 3D detached eddy simulations were also carried out, highlighting the improvement in the accuracy of the results with respect to both 2D and 3D URANS calculations. All of the computations were performed with the Tau code, a non-commercial unstructured solver developed by the German Aerospace Center

Molecular Magnetic Materials on Solid Surfaces

This PhD thesis summarises a study of the nanostructuration of single molecule magnets and organic radicals on metallic surfaces, carried out by the author in collaboration with a number of research groups in Italy, France, Germany and Israel. A tailored approach was followed to graft individual molecules to the surface, to characterise the morphology of the functionalised surfaces with standard scanning probe microscopy and to investigate their magnetic properties using X-Ray circular dichroism. The aim of this project was to develop the initial basis for the organisation and addressing of magnetic molecules with a view to the development of single molecule devices for data storage and molecular-spintronic applications

Studio e implementazione di metodi per la classificazione automatica di movimenti umani basata su dati accelerometrici

Questo lavoro si pone come obiettivo lo studio di algoritmi per la classificazione automatica di posture e movimenti eseguiti da un soggetto, mediante elaborazione dei segnali provenienti da cinque accelerometri biassiali posti in corrispondenza di determinati punti anatomici. Un sistema di classificazione automatica del movimento è di grande interesse in applicazioni di pervasive computing che richiedano la conoscenza del contesto per facilitare l’interazione uomo-macchina, e in biomedicina, per la realizzazione di sistemi wearable per la valutazione a lungo-termine di parametri fisiologici e biomeccanici. In questo lavoro ci proponiamo in primo luogo di studiare algoritmi di classificazione one-shot, in cui l’esito della classificazione a un certo istante non dipende dalla storia delle classificazioni precedenti, e algoritmi di classificazione sequenziale basati sugli Hidden Markov Model (HMM), per sfruttare la conoscenza delle statistiche di un task risultante dal concatenamento di singole primitive di movimento. All’algoritmo di classificazione automatica delle sequenze di movimenti e posture è stato inoltre introdotto un sistema di rimozione automatica dei dati non classificabili, relativi alle transizioni posturali o ai movimenti non noti al sistema. The aim of this study is the development of an algorithm for automatic classification of human postures and movements, starting from accelerometer data. The acceleration data can be measured by a few sensors affixed to selected points of the human body. Movement classifiers can be interesting in applications of pervasive computing, whereas contextual awareness may ease the human-machine interaction, or in biomedicine, whereas wearable systems are developed for long-term monitoring of physiological and biomechanical parameters. In this paper we intend to study one-shot and sequential classifiers. One-shot classifiers deliver their actual outcome, without any regard to previous outcomes. Conversely, sequential classifiers, i.e. Hidden Markov Model (HMM), incorporate the statistical information acquired about the movement dynamics into the classification process. An automatic spurious data removing algorithm has been added to this kind of classifier, to make possible the automatic detection and removal of data relative to unknown movements or postural transitions

Patterning molecular scale paramagnets at Au Surface: A root to Magneto-Molecular-Electronics

Few examples of the exploitation of molecular magnetic properties in molecular electronics are known to date. Here we propose the realization of Self assembled monolayers (SAM) of a particular stable organic radical. This radical is meant to be used as a standard molecule on which to prove the validity of a single spin reading procedure known as ESR-STM. We also discuss a range of possible applications, further than ESR-STM, of magnetic monolayers of simple purely organic magnetic molecule.Comment: This preprint is currently partially under revisio

Accelerometry-Based Classification of Human Activities Using Markov Modeling

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series