A methodological approach for the performance optimization of transient seepage models through inverse analysis

The topic of the Ph.D project focuses on the modelling of the soil-water dynamics inside an instrumented embankment section along Secchia River (Cavezzo (MO)) in the period from 2017 to 2018 and the quantification of the performance of the direct and indirect simulations . The commercial code Hydrus2D by Pc-Progress has been chosen to run the direct simulations. Different soil-hydraulic models have been adopted and compared. The parameters of the different hydraulic models are calibrated using a local optimization method based on the Levenberg - Marquardt algorithm implemented in the Hydrus package. The calibration program is carried out using different types of dataset of observation points, different weighting distributions, different combinations of optimized parameters and different initial sets of parameters. The final goal is an in-depth study of the potentialities and limits of the inverse analysis when applied to a complex geotechnical problem as the case study. The second part of the research focuses on the effects of plant roots and soil-vegetation-atmosphere interaction on the spatial and temporal distribution of pore water pressure in soil. The investigated soil belongs to the West Charlestown Bypass embankment, Newcastle, Australia, that showed in the past years shallow instabilities and the use of long stem planting is intended to stabilize the slope. The chosen plant species is the Malaleuca Styphelioides, native of eastern Australia. The research activity included the design and realization of a specific large scale apparatus for laboratory experiments. Local suction measurements at certain intervals of depth and radial distances from the root bulb are recorded within the vegetated soil mass under controlled boundary conditions. The experiments are then reproduced numerically using the commercial code Hydrus 2D. Laboratory data are used to calibrate the RWU parameters and the parameters of the hydraulic model.

Computing the structural and vibrational properties of polymorphic organic molecular crystals through van der Waals corrected density functional theory and the electronic properties of organic thin films through microelectrostatic calculations.

The present Thesis reports on the various research projects to which I have contributed during my PhD period, working with several research groups, and whose results have been communicated in a number of scientific publications. The main focus of my research activity was to learn, test, exploit and extend the recently developed vdW-DFT (van der Waals corrected Density Functional Theory) methods for computing the structural, vibrational and electronic properties of ordered molecular crystals from first principles. A secondary, and more recent, research activity has been the analysis with microelectrostatic methods of Molecular Dynamics (MD) simulations of disordered molecular systems. While only very unreliable methods based on empirical models were practically usable until a few years ago, accurate calculations of the crystal energy are now possible, thanks to very fast modern computers and to the excellent performance of the best vdW-DFT methods. Accurate energies are particularly important for describing organic molecular solids, since they often exhibit several alternative crystal structures (polymorphs), with very different packing arrangements but very small energy differences. Standard DFT methods do not describe the long-range electron correlations which give rise to the vdW interactions. Although weak, these interactions are extremely sensitive to the packing arrangement, and neglecting them used to be a problem. The calculations of reliable crystal structures and vibrational frequencies has been made possible only recently, thanks to development of some good representations of the vdW contribution to the energy (known as “vdW corrections”).

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

Development, mechanical characterization and qualification in liquid lead of stainless steels for structural applications in the future fission and fusion nuclear reactors

The research activity carried out in the Brasimone Research Center of ENEA concerns the development and mechanical characterization of steels conceived as structural materials for future fission reactors (Heavy Liquid Metal IV Generation reactors: MYRRHA and ALFRED) and for the future fusion reactor DEMO. Within this framework, two parallel lines of research have been carried out: (i) characterization in liquid lead of steels and weldings for the components of the IV Generation fission reactors (GIV) by means of creep and SSRT (Slow Strain Rate Tensile) tests; (ii) development and screening on mechanical properties of RAFM (Reduced Activation Ferritic Martensitic) steels to be employed as structural materials of the future DEMO fusion reactor. The doctoral work represents therefore a comprehensive report of the research carried out on nuclear materials both from the point of view of the qualification of existing (commercial) materials for their application in the typical environmental conditions of 4th generation fission reactors operating with lead as coolant, and from the point of view of the metallurgical study (with annexed microstructural and mechanical characterization of the selected compositions / Thermo Mechanical Treatment (TMT) options) of new compositional variants to be proposed for the “Breeding Blanket” of the future DEMO Fusion Reactor.

Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.

Innovative strategies for the mitigation of acrylamide content in different food products

Acrylamide (AA) is an undesirable food toxic compound, classified as 'probably carcinogenic to humans' by the International Agency for Research on Cancer due to its toxic effects, including neurotoxicity, genotoxicity, carcinogenicity and reproductive toxicity. AA is mainly formed during the heat treatment of foods (> 120 °C) by the Maillard reaction, an essential reaction that also allows the desired levels of shelf-life and sensory properties of various food products to be achieved. Over the years, authorities and regulations have become more restrictive regarding the maximum levels of AA permitted in foods and beverages. The latest Commission Regulation (EU) 2017/2158 contains reference levels and measures to reduce AA in several food groups that contribute to the highest dietary intake, making necessary the study of promising AA mitigation strategies. The aim of this PhD research project was to identify, characterise and optimise some AA mitigation strategies in the most at-risk widely consumed foods such as potato, coffee and bakery products. Some AA control strategies were selected and investigated for each food category, also considering the main quality characteristics of the final products. The comprehensive results obtained during the three years of research activity have allowed a deeper knowledge of the traditional and innovative AA mitigation strategies, which can be extremely useful for both the food industry and international authorities. The most promising strategies studied in terms of reduction of AA while maintaining the main quality characteristics of the examined foods were: the application of pulsed electric fields and yeast immersion as pre-treatments of chips for frying; the selection of high roasting degrees for coffee products; the selection of static baking conditions for biscuits; the optimisation of alternative biscuit’ formulations by both the use of chickpea legume flour and of flour from bean with intact cotyledon cell walls.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Relationships between airborne pollen allergens and environmental factors.

This thesis is a collection of scientific papers resulting from my research activity during the PhD course in Earth, Life, and Environmental Sciences. The main subject of the thesis is the capability of pollen to trigger a hypersensitive reaction in different environmental conditions, and the need to better characterise such allergenicity in order to measure it. This topic is discussed from different perspectives, using ecological, morphological, and molecular approaches. The thesis starts by summarising the importance of green infrastructures in the cities, from economical and conservational perspectives. It then focalises on the lesser-known ecosystem disservices urban vegetation can provide, and in particular on pollen allergy, exploring its causes and illustrating possible ways to monitor, foresee, and mitigate the allergenic risk. The possibility to monitor the allergenicity of urban green areas is then examined in depth, with an original research paper that proposes a method standardisation for existing allergenicity indices (Specific Allergenic Index and Urban Green Zones Allergenicty Index), and compares the indices results to evaluate their effectiveness. At the end of the thesis, pollen allergenicity is also approached from a molecular perspective, by investigating pollen allergens release mechanisms in the context of pollen hydration and germination. In particular, in an unpublished original research paper, the nature of allergen-carrying extracellular nanovesicles (pollensomes) released by pollen is extensively studied on a non-allergenic pollen model, to understand their biological role and thus the environmental conditions that trigger their release. Moreover, the last paper reported in the thesis demonstrates the secretion of a potential pollen allergen, a low-molecular weight cyclophilin, during pollen germination under stressful conditions. The thesis concludes with a brief description of other scientific activities carried on during the PhD, that still need more scientific corroboration to be published.

Wireless Power Link Design for Both High-Power Inductive Coupling and Smart Metasurfaces Exploitation

As future technologies are going to be autonomous under the umbrella of the Internet of things (IoT) we can expect WPT to be the solution for intelligent devices. WPT has many industrial and medical applications both in the near-field and far-field domains. Considering the impact of WPT, this thesis is an attempt to design and realize both near-field and far-field WPT solutions for different application scenarios. A 27 MHz high frequency inductive wireless power link has been designed together with the Class-E switching inverter to compensate for the efficiency loss because of the varying weak coupling between transmitter and receiver because of their mutual misalignment. Then a system of three coils was introduced for SWIPT. The outer coil for WPT and the inner two coils were designed to fulfil the purpose of communication and testing, operating at frequencies different from the WPT coil. In addition to that, a trapping filter technique has also been adopted to ensure the EM isolation of the coils. Moreover, a split ring resonator-based dual polarization converter has been designed with good efficiency over a wide frequency range. The gap or cuts have been introduced in the adjacent sides of the square ring to make it a dual-polarization converter. The converter is also stable over a wide range of incident angles. Furthermore, a meta-element based intelligent surface has been designed to work in the reflection mode at 5 GHz. In this research activity, interdigital capacitors (IDCs) instead of ICs are introduced and a thin layer of the HfZrO between substrate and meta elements is placed whose response can be tuned and controlled with the applied voltage to achieve IRS.

Landslide susceptibility in the Belt and Road Initiative

The Belt and Road Initiative (BRI) is a project launched by the Chinese Government whose main goal is to connect more than 65 countries in Asia, Europe, Africa and Oceania developing infrastructures and facilities. To support the prevention or mitigation of landslide hazards, which may affect the mainland infrastructures of BRI, a landslide susceptibility analysis in the countries involved has been carried out. Due to the large study area, the analysis has been carried out using a multi-scale approach which consists of mapping susceptibility firstly at continental scale, and then at national scale. The study area selected for the continental assessment is the south-Asia, where a pixel-based landslide susceptibility map has been carried out using the Weight of Evidence method and validated by Receiving Operating Characteristic (ROC) curves. Then, we selected the regions of west Tajikistan and north-east India to be investigated at national scale. Data scarcity is a common condition for many countries involved into the Initiative. Therefore in addition to the landslide susceptibility assessment of west Tajikistan, which has been conducted using a Generalized Additive Model and validated by ROC curves, we have examined, in the same study area, the effect of incomplete landslide dataset on the prediction capacity of statistical models. The entire PhD research activity has been conducted using only open data and open-source software. In this context, to support the analysis of the last years an open-source plugin for QGIS has been implemented. The SZ-tool allows the user to make susceptibility assessments from the data preprocessing, susceptibility mapping, to the final classification. All the output data of the analysis conducted are freely available and downloadable. This text describes the research activity of the last three years. Each chapter reports the text of the articles published in international scientific journal during the PhD.

RF energy harvesting solutions for electromagnetic harsh environments: from industrial plants to wearable/implantable devices

The presented Thesis describes the design of RF-energy harvesting systems with applications on different environments, from the biomedical side to the industrial one, tackling the common thread problem which is the design of complete energy autonomous tags each of them with its dedicated purpose. This Thesis gathers a work of three years in the field of energy harvesting system design, a combination of full-wave electromagnetic designs to optimize not only the antenna performance but also to fulfill the requirements given by each case study such as dimensions, insensitivity from the surrounding environment, flexibility and compliance with regulations. The research activity has been based on the development of highly-demanded ideas and real-case necessities which are in line with the environment in which modern IoT applications can really make a positive contribution. The Thesis is organized as follows: the first application, described in Chapter 2, regards the design and experimental validations of a rotation-insensitive WPT system for implantable devices. Chapter 3 presents the design of a wearable energy autonomous detector to identify the presence of ethanol on the body surface. Chapter 4 describes investigations in the use of Bessel Beam launchers for creating a highly-focused energy harvesting link for wearable applications. Reduced dimensions, high focusing and decoupling from the human body are the key points to be addressed during the full-wave design and nonlinear optimization of the receiver antenna. Finally, Chapter 5 presents an energy autonomous system exploiting LoRa (Long Range) nodes for tracking trailers in industrial plants. The novelty behind this design lies on the aim of obtaining a perfectly scalable system that exploits not only EH basic operating system but embeds a seamless solution for collecting a certain amount of power that varies with respect the received power level on the antenna, without the need of additional off-the-shelf components.

Modelling the influence of diradical character and aggregation on conjugated molecular materials

Molecular materials are made by the assembly of specifically designed molecules to obtain bulk structures with desired solid-state properties, enabling the development of materials with tunable chemical and physical properties. These properties result from the interplay of intra-molecular constituents and weak intermolecular interactions. Thus, small changes in individual molecular and electronic structure can substantially change the properties of the material in bulk. The purpose of this dissertation is, thus, to discuss and to contribute to the structure-property relationships governing the electronic, optical and charge transport properties of organic molecular materials through theoretical and computational studies. In particular, the main focus is on the interplay of intra-molecular properties and inter-molecular interactions in organic molecular materials. In my three-years of research activity, I have focused on three major areas: 1) the investigation of isolated-molecule properties for the class of conjugated chromophores displaying diradical character which are building blocks for promising functional materials; 2) the determination of intra- and intermolecular parameters governing charge transport in molecular materials and, 3) the development and application of diabatization procedures for the analysis of exciton states in molecular aggregates. The properties of diradicaloids are extensively studied both regarding their ground state (diradical character, aromatic vs quinoidal structures, spin dynamics, etc.) and the low-lying singlet excited states including the elusive double-exciton state. The efficiency of charge transport, for specific classes of organic semiconductors (including diradicaloids), is investigated by combining the effects of intra-molecular reorganization energy, inter-molecular electronic coupling and crystal packing. Finally, protocols aimed at unravelling the nature of exciton states are introduced and applied to different molecular aggregates. The role of intermolecular interactions and charge transfer contributions in determining the exciton state character and in modulating the H- to J- aggregation is also highlighted.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Study of risky situations using experimental data coming from an instrumented bicycle

The aim of this master’s thesis is to study the risky situations of the cyclist when they interact with road infrastructure and other road users as well as the influence of speed on safety. This research activity is linked with the SAFERUP (Sustainable, Accessible, Resilient, and Smart Urban Pavement) European funded project where one of the doctoral candidate has performed experiments on the bicycle simulation at the Gustave Eiffel university in the PICS-L laboratory (Paris) and instrumented bicycle at the Stockholm (Sweden). The approach of the experiment was to hire a number of people who have participated in the riding of the Instrumented bicycle (Stockholm) and bicycle simulator (PICS-L) which were developed by attaching different sensors and devices to measure important parameters of the bicycle riding and their data was collected to analysis in order to understand the behavior of the cyclist to improve the safety. In addition, a mobile eye tracker wore by participants to record the real experiment scenario, and after the end of the trip, each participant shared their remarks regarding their experience of bicycle riding according to different portions of the road infrastructure. In this research main focus is to analyze the relevant data such as speed profiles, video recordings and questionnaire surveys from the instrumented bicycle experiment. In fact, critical situations, where there was a higher probability, were compared with the subjective evaluation of the participant to be conscious of the issues related to the safety and comfort of the cyclist in different road characteristics.