Radio relics and magnetic fields in galaxy clusters

Radio relics are diffuse synchrotron sources generally located in the peripheries of galaxy clusters in merging state. According to the current leading scenario, relics trace gigantic cosmological shock waves that cross the intra-cluster medium where particle acceleration occurs. The relic/shock connection is supported by several observational facts, including the spatial coincidence between relics and shocks found in the X-rays. Under the assumptions that particles are accelerated at the shock front and are subsequently deposited and then age downstream of the shock, Markevitch et al. (2005) proposed a method to constrain the magnetic field strength in radio relics. Measuring the thickness of radio relics at different frequencies allows to derive combined constraints on the velocity of the downstream flow and on the magnetic field, which in turns determines particle aging. We elaborate this idea to infer first constraints on magnetic fields in cluster outskirts. We consider three models of particle aging and develop a geometric model to take into account the contribution to the relic transverse size due to the projection of the shock-surface on the plane of the sky. We selected three well studied radio relics in the clusters A 521, CIZA J2242.8+5301 and 1RXS J0603.3+4214. These relics have been chosen primarily because they are almost seen edge-on and because the Mach number of the shock that is associated with these relics is measured by X-ray observations, thus allowing to break the degeneracy between magnetic field and downstream velocity in the method. For the first two clusters, our method is consistent with a pure radiative aging model allowing us to derive constraints on the relics magnetic field strength. In the case of 1RXS J0603.3+4214 we find that particle life-times are consistent with a pure radiative aging model under some conditions, however we also collect evidences for downstream particle re-acceleration in the relic W-region and for a magnetic field decaying downstream in its E-region. Our estimates of the magnetic field strength in the relics in A 521 and CIZA J2242.8+5301 provide unique information on the field properties in cluster outskirts. The constraints derived for these relics, together with the lower limits to the magnetic field that we derived from the lack of inverse Compton X-ray emission from the sources, have been combined with the constraints from Faraday rotation studies of the Coma cluster. Overall results suggest that the spatial profile of the magnetic field energy density is broader than that of the thermal gas, implying that the ε_th /ε_B ratio decreases with cluster radius. Alternatively, radio relics could trace dynamically active regions where the magnetic field strength is biased high with respect to the average value in the cluster volume.

Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields.

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

An Embedded In-Cabin Lightweight High-Performance 3D Gaze Estimation System

Gaze estimation has gained interest in recent years for being an important cue to obtain information about the internal cognitive state of humans. Regardless of whether it is the 3D gaze vector or the point of gaze (PoG), gaze estimation has been applied in various fields, such as: human robot interaction, augmented reality, medicine, aviation and automotive. In the latter field, as part of Advanced Driver-Assistance Systems (ADAS), it allows the development of cutting-edge systems capable of mitigating road accidents by monitoring driver distraction. Gaze estimation can be also used to enhance the driving experience, for instance, autonomous driving. It also can improve comfort with augmented reality components capable of being commanded by the driver's eyes. Although, several high-performance real-time inference works already exist, just a few are capable of working with only a RGB camera on computationally constrained devices, such as a microcontroller. This work aims to develop a low-cost, efficient and high-performance embedded system capable of estimating the driver's gaze using deep learning and a RGB camera. The proposed system has achieved near-SOTA performances with about 90% less memory footprint. The capabilities to generalize in unseen environments have been evaluated through a live demonstration, where high performance and near real-time inference were obtained using a webcam and a Raspberry Pi4.

Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

Study of high-quality ALD gate dielectrics in radiation sensitive oxide field effect transistors

Radiation dosimetry is crucial in many fields, where the exposure of ionizing radiation must be precisely controlled to avoid health and environmental safety issues. Radiotherapy and radioprotection are two examples in which fast and reliable detectors are needed. Compact and large area wearable detectors are being developed to address real-life radiation dosimetry applications, their ideal properties include flexibility, lightness, and low-cost. This thesis contributed to the development of Radiation sensitive OXide Field Effect Transistors (ROXFETs), which are detectors able to provide fast and real-time radiation read out. ROXFETs are based on thin film transistors fabricated with high-mobility amorphous oxide semiconductor, making them compatible with large area, flexible, and low cost production over plastic substrates. The gate dielectric material has high dielectric constant and high atomic number, which results in high performances and high radiation sensitivity, respectively. The aim of this work was to establish a stable and reliable fabrication process for ROXFETs made with atomic layer deposited gate dielectric. A study on the effect of gate dielectric materials was performed, focusing the attention on the properties of the dielectric-semiconductor interface. Single and multi layer dielectric structures were compared during this work. Furthermore, the effect of annealing temperature was studied. The device performances were tested to understand the underlying physical processes. In this way, it was possible to determine a reliable fabrication procedure and an optimal structure for ROXFETs. An outstanding sensitivity of (65±3)V/Gy was measured in detectors with a bi-layer Ta₂O₅-Al₂O₃ gate dielectric with low temperature annealing performed at 180°C.

The molecular gas content of star-forming galaxies in the ALPINE fields.

In this thesis, I aim to study the evolution with redshift of the gas mass fraction of a sample of 53 sources (from z ∼ 0.5 to z > 5) serendipitously detected in ALMA band 7 as part of the ALMA Large Program to INvestigate C II at Early Times (ALPINE). First, I used SED-fitting software CIGALE, which is able to implement energy balancing between the optical and the far infrared part, to produce a best-fit template of my sources and to have an estimate of some physical properties, such as the star formation rate (SFR), the total infrared luminosity and the total stellar mass. Then, using the tight correlation found by Scoville et al. (2014) between the ISM molecular gas mass and the rest-frame 850 μm luminosity, I used the latter, extrapolating it from the best-fit template using a code that I wrote in Python, as a tracer for the molecular gas. For my sample, I then derived the most important physical properties, such as molecular gas mass, gas mass fractions, specific star formation rate and depletion timescales, which allowed me to better categorize them and find them a place within the evolutionary history of the Universe. I also fitted our sources, via another code I wrote again in Python, with a general modified blackbody (MBB) model taken from the literature (Gilli et al. (2014), D’Amato et al. (2020)) to have a direct method of comparison with similar galaxies. What is evident at the end of the paper is that the methods used to derive the physical quantities of the sources are consistent with each other, and these in turn are in good agreement with what is found in the literature.

Development of a novel heat treatment for high performances L-PBF Ti6Al4V alloy: microstructural and mechanical characterization

This work presents the experimental development of a novel heat treatment for a high performance Laser Powder Bed Fusion Ti6Al4V alloy. Additive manufacturing production processes for titanium alloys are particularly of interest in cutting-edge engineering fields, however, high frequency laser induced thermal cycles generate a brittle as built microstructure. For this reason, heat treatments compliant with near net shape components are needed before their homologation and usage. The experimental campaign focused on the development of a multi-step heat treatment leading to a bilamellar microstructure. In fact, according to literature, such a microstructure should be promising in terms of mechanical properties both under static and cyclic loads. The heat treatment development has asked for the preliminary analyses of samples annealed and aged in laboratory, implementing several cycles, differing for what concerns temperatures, times and cooling rates. Such a characterization has been carried out through optical and electron microscopy analyses, image analyses, hardness and tensile tests. As a result, the most suitable thermal cycle has been selected and performed using industrial equipment on mini bending fatigue samples with different surface conditions. The same tests have been performed on a batch of traditionally treated samples, to provide with a comparison. This master thesis activity has finally led to the definition of a heat treatment resulting into a bilamellar microstructure, promising in terms of fatigue performances with respect to the traditionally treated alloy ones. The industrial implementation of such a heat treatment will require further improvements, particularly for what concerns the post annealing water quench, in order to prevent any surface alteration potentially responsible for the fatigue performances drop. Further development of the research may also include push-pull fatigue tests, crack grow propagation and residual stresses analyses.

Heat-transfer enhancement with pulsed jets in turbulent pipe flow at high-Reynolds number

Improving heat transfer is a critical area of ​​research in various fields such as thermal engineering, energy conversion and aeronautical engineering. The aim of this thesis is to present the design, construction and testing of an experimental setup for the study of heat transfer enhancement in a turbulent boundary layer using cross-flow pulsed jets. The set-up is designed to generate and control pulsed jets, measure heat transfer and acquire all parameters related to wind tunnel flow and is also capable of varying the parameters of the pulsed jets, such as frequency, amplitude and the duty cycle, in order to study the effects on the increase in heat transfer. The thesis describes the design phases, the construction process and the final successful testing of the plant. The test results verify the functionality and accuracy of the set-up and ensure that it can be used to perform a full experimental campaign to investigate heat transfer enhancement using cross-flow pulsed jets in a turbulent layer boundary.