Numerical methods for the dispersion analysis of Guided Waves

The use of guided ultrasonic waves (GUW) has increased considerably in the fields of non-destructive (NDE) testing and structural health monitoring (SHM) due to their ability to perform long range inspections, to probe hidden areas as well as to provide a complete monitoring of the entire waveguide. Guided waves can be fully exploited only once their dispersive properties are known for the given waveguide. In this context, well stated analytical and numerical methods are represented by the Matrix family methods and the Semi Analytical Finite Element (SAFE) methods. However, while the former are limited to simple geometries of finite or infinite extent, the latter can model arbitrary cross-section waveguides of finite domain only. This thesis is aimed at developing three different numerical methods for modelling wave propagation in complex translational invariant systems. First, a classical SAFE formulation for viscoelastic waveguides is extended to account for a three dimensional translational invariant static prestress state. The effect of prestress, residual stress and applied loads on the dispersion properties of the guided waves is shown. Next, a two-and-a-half Boundary Element Method (2.5D BEM) for the dispersion analysis of damped guided waves in waveguides and cavities of arbitrary cross-section is proposed. The attenuation dispersive spectrum due to material damping and geometrical spreading of cavities with arbitrary shape is shown for the first time. Finally, a coupled SAFE-2.5D BEM framework is developed to study the dispersion characteristics of waves in viscoelastic waveguides of arbitrary geometry embedded in infinite solid or liquid media. Dispersion of leaky and non-leaky guided waves in terms of speed and attenuation, as well as the radiated wavefields, can be computed. The results obtained in this thesis can be helpful for the design of both actuation and sensing systems in practical application, as well as to tune experimental setup.

Simulation Guided Navigation in cranio-maxillo-facial surgery: a new approach to improve intraoperative three-dimensional accuracy and reproducibility during surgery.

The aim of this PhD thesis " Simulation Guided Navigation in cranio- maxillo- facial surgery : a new approach to Improve intraoperative three-dimensional accuracy and reproducibility during surgery ." was at the center of its attention the various applications of a method introduced by our School in 2010 and has as its theme the increase of interest of reproducibility of surgical programs through methods that in whole or in part are using intraoperative navigation. It was introduced in Orthognathic Surgery Validation a new method for the interventions carried out according to the method Simulation Guided Navigation in facial deformities ; was then analyzed the method of three-dimensional control of the osteotomies through the use of templates and cutting of plates using the method precontoured CAD -CAM and laser sintering . It was finally proceeded to introduce the method of piezonavigated surgery in the various branches of maxillofacial surgery . These studies have been subjected to validation processes and the results are presented .

Azacitidine and Lenalidomide Combination Therapy in Myelodysplastic Syndromes: Topography and Translational Relevance of Nuclear Phospholipase C β - dependent Signalling

Nuclear inositide signalling pathways, and particularly those regulated by PI-PLCβ1, are associated with cell proliferation and differentiation. Myelodysplastic syndromes (MDS) are a heterogeneous spectrum of chronic myeloid hemopathies with associated symptomatic cytopenias and substantial potential for evolution to acute myeloid leukemia (AML). MDS patients are currently treated with two main approaches, epigenetic (Azacitidine) and immunomodulatory (Lenalidomide: above all in cell clones bearing a deletion of the long arm of the chromosome 5 [del(5q)]). As Azacitidine and Lenalidomide alone can show adverse effects or patients can be refractory, an experimental current approach is the combination of the two drugs. Clinically, this combination therapy is promising, while its molecular effect has to be clarified. Stemming from these data, in this study the effect of an Azacitidine-Lenalidomide combination therapy was studied, in both MDS patients and hematopoietic cell lines. The specific aims of this study were to evaluate the effect of Azacitidine and Lenalidomide MDS therapy on: cell cycle regulation, hematopoietic differentiation, gene mutation and miR expression. Lenalidomide alone, via PI-PLCβ1/PKC pathway, was able to induce a selective G0/G1 arrest of the cell cycle in del(5q) cells, slowing down their rate proliferation and favouring erythropoiesis activation. In addition, although the mutation profile at baseline was not entirely capable of predicting the clinical effect of Azacitidine and Lenalidomide therapy, the presence of specific point mutations affecting three inositide genes (PI3KCD, AKT3, PLCG2) was correlated to and anticipated a negative clinical outcome. Moreover, the differential miR expression was detectable even from the 4th cycle of therapy in responder patients, as compared to non-responders. In MDS, this is the first evidence that the molecular mutation profiling of inositide genes or a specific mini-cluster of differentially expressed miRs, targeting inositide signaling molecules, can be associated with the clinical response, thus possibly predicting the effect of the therapy.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.