Sea level measurement using single GNSS antenna SNR signals

This thesis presents a possible method to calculate sea level variation using geodetic-quality Global Navigate Satellite System (GNSS) receivers. Three antennas are used: two small antennas and a choke ring one, analyzing only Global Positioning System signals. The main goal of the thesis is to test a modified configuration for antenna set up. In particular, measurements obtained tilting one antenna to face the horizon are compared to measurements obtained from antennas looking upward. The location of the experiment is a coastal environment nearby the Onsala Space Observatory in Sweden. Sea level variations are obtained using periodogram analysis of the SNR signal and compared to synthetic gauge generated from two independent tide gauges. The choke ring antenna provides poor result, with an RMS around 6 cm and a correlation coefficients of 0.89. The smaller antennas provide correlation coefficients around 0.93. The antenna pointing upward present an RMS of 4.3 cm and the one pointing the horizon an RMS of 6.7 cm. Notable variation in the statistical parameters is found when modifying the length of the interval analyzed. In particular, doubts are risen on the reliability of certain scattered data. No relation is found between the accuracy of the method and weather conditions. Possible methods to enhance the available data are investigated, and correlation coefficient above 0.97 can be obtained with small antennas when sacrificing data points. Hence, the results provide evidence of the suitability of SNR signal analysis for sea level variation in coastal environment even in the case of adverse weather conditions. In particular, tilted configurations provides comparable result with upward looking geodetic antennas. A SNR signal simulator is also tested to investigate its performance and usability. Various configuration are analyzed in combination with the periodogram procedure used to calculate the height of reflectors. Consistency between the data calculated and those received is found, and the overall accuracy of the height calculation program is found to be around 5 mm for input height below 5 m. The procedure is thus found to be suitable to analyze the data provided by the GNSS antennas at Onsala.

Measurement of LSP-induced residual stresses and fatigue life on thin-walled aluminium alloys specimens

This thesis work encloses activities carried out in the Laser Center of the Polytechnic University of Madrid and the laboratories of the University of Bologna in Forlì. This thesis focuses on the superficial mechanical treatment for metallic materials called Laser Shock Peening (LSP). This process is a surface enhancement treatment which induces a significant layer of beneficial compressive residual stresses underneath the surface of metal components in order to improve the detrimental effects of the crack growth behavior rate in it. The innovation aspect of this work is the LSP application to specimens with extremely low thickness. In particular, after a bibliographic study and comparison with the main treatments used for the same purposes, this work analyzes the physics of the operation of a laser, its interaction with the surface of the material and the generation of the surface residual stresses which are fundamentals to obtain the LSP benefits. In particular this thesis work regards the application of this treatment to some Al2024-T351 specimens with low thickness. Among the improvements that can be obtained performing this operation, the most important in the aeronautic field is the fatigue life improvement of the treated components. As demonstrated in this work, a well-done LSP treatment can slow down the progress of the defects in the material that could lead to sudden failure of the structure. A part of this thesis is the simulation of this phenomenon using the program AFGROW, with which have been analyzed different geometric configurations of the treatment, verifying which was better for large panels of typical aeronautical interest. The core of the LSP process are the residual stresses that are induced on the material by the interaction with the laser light, these can be simulated with the finite elements but it is essential to verify and measure them experimentally. In the thesis are introduced the main methods for the detection of those stresses, they can be mechanical or by diffraction. In particular, will be described the principles and the detailed realization method of the Hole Drilling measure and an introduction of the X-ray Diffraction; then will be presented the results I obtained with both techniques. In addition to these two measurement techniques will also be introduced Neutron Diffraction method. The last part refers to the experimental tests of the fatigue life of the specimens, with a detailed description of the apparatus and the procedure used from the initial specimen preparation to the fatigue test with the press. Then the obtained results are exposed and discussed.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Validation of an intermediate-complexity flood risk model for climate change adaptation

In questo lavoro di tesi viene presentato e validato un modello di rischio di alluvione a complessità intermedia per scenari climatici futuri. Questo modello appartiene a quella categoria di strumenti che mirano a soddisfare le esigenze identificate dal World Climate Research Program (WRCP) per affrontare gli effetti del cambiamento climatico. L'obiettivo perseguito è quello di sviluppare, seguendo un approccio ``bottom-up" al rischio climatico regionale, strumenti che possano aiutare i decisori a realizzare l'adattamento ai cambiamenti climatici. Il modello qui presentato è interamente basato su dati open-source forniti dai servizi Copernicus. Il contributo di questo lavoro di tesi riguarda lo sviluppo di un modello, formulato da (Ruggieri et al.), per stimare i danni di eventi alluvionali fluviali per specifici i livelli di riscaldamento globale (GWL). Il modello è stato testato su tre bacini idrografici di medie dimensioni in Emilia-Romagna, Panaro, Reno e Secchia. In questo lavoro, il modello viene sottoposto a test di sensibilità rispetto a un'ipotesi enunciata nella formulazione del modello, poi vengono effettuate analisi relative all'ensemble multi-modello utilizzato per le proiezioni. Il modello viene quindi validato, confrontando i danni stimati nel clima attuale per i tre fiumi con i danni osservati e confrontando le portate simulate con quelle osservate. Infine, vengono stimati i danni associati agli eventi alluvionali in tre scenari climatici futuri caratterizzati da GWL di 1.5° C, 2.0° C e 3.0°C.