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An edge-to-cloud framework for privacy-aware management of geospatial data
No full textIn today's fast-paced technological landscape, characterized by rapid innovation and transformation across sectors, a compelling demand emerges for sophisticated and adaptable systems within Smart Environments. These dynamic settings, marked by an influx of data from diverse sources and intricate distributed systems, offer remarkable opportunities alongside distinct challenges.
This Thesis responds to the need for a flexible, scalable framework that adeptly navigates the complexities of modern distributed data aggregation and processing systems, while upholding the paramount principles of data privacy and security.
At its core, the framework introduces a distributed architecture, housing a service placement algorithm that seamlessly spans from the Edge to the Cloud, meticulously crafted in accordance with the tenets of Fog Computing. This architectural approach indispensably relies on data privacy, significantly influencing applications and prioritizing reliable data management.
A second pivotal contribution is the Seamless Data Acquisition Protocol (SEAMDAP), a standard-based and modern approach meticulously designed to facilitate data collection within distributed systems. Engineered to be both user-friendly and highly customizable, SEAMDAP streamlines the intricate process of gathering data from a multitude of sources, reducing friction, and enhancing flexibility.
Lastly, the Thesis ventures deeply into the critical realms of data integrity and security, acutely acknowledging the inherent importance of georeferenced data and location verification. A robust architecture is proposed within the framework's toolkit, ensuring that data remains secure during transmission, storage, and processing, and culminating in the exploration of advanced processing techniques such as Homomorphic Encryption and Multi-Party Computation.
Crucially, the direction taken with this framework is firmly anchored in the pursuit of standardizing the realm of smart environments while proactively addressing identified issues. Several tools presented herein have undergone rigorous testing in Smart Farming environments, each accompanied by compelling use cases. The framework holds particular promise in settings characterized by high heterogeneity, an abundance of georeferenced data, a critical need for interoperability among systems operated by diverse stakeholders, and an strong commitment to data privacy
Rivestimenti antifouling per applicazioni marine
No full textMy work has focused on the design and fabrication of superhydrophobic coatings (SHS) and Slippery Liquid Porous Surfaces (SLIPS) for marine applications. The coatings were obtained by deposition of ceramic nanoparticles (Al2O3, SiO2-X) and chemical modification with low surface energy compounds (< 23mN/m). I designed the synthesis of SiO2 nanoparticles that are inherently superhydrophobic by sol-gel in isopropyl alcohol and ethyl alcohol, respectively with fluorinated and alkyl chains. I characterized nanoparticles in terms of size and Z potential using DLS and ELS technology.
I designed the one-step synthesis of flower-like boehmite (Al2O3) in hydrothermal route, in an aqueous solution, evaluating the morphology as different reaction conditions.
I explored two deposition techniques, dip coating and spray coating. After the deposition of nanoparticles, a heat treatment was necessary. Heat treatment has been optimized for some more sensitive materials such as steel and fiberglass. The proposed coatings involve the combination of a first inorganic layer alumina based, which after boiling water treatment has flower-like structure, and an organic layer is deposited with fluorinated or alkyl functionality. The second type of coating has a layer composed of silica nanoparticles intrinsically superhydrophobic. To obtain a SLIPS type surface it is necessary to infuse a lubricant inside the porosities. Fluorinated lubricating oils, alkanes with long alkyl chains and silicon oils were chosen.
I characterized the coatings in terms of wettability properties (static contact angle WCA and dynamic contact angle CAH) with water. After I determined the surface morphology with FESEM observation, determining the particle size, coating homogeneity, and thickness.
The superhydrophobic coatings were subjected to cell adhesion test (MTT cell viability assay) and cytotoxicity tests (Actin/DAPI), also evaluating the strength of the superhydrophobic properties after test.
The antifouling properties of the coating was tested with larvae settlement test (no choice assay), and the leaching toxicity of the coatings molecules was evaluated. Finally, the coatings were to the open sea to assess the antifouling qualities under more drastic conditions. Moreover, the durability of the superhydrophobic properties in underwater conditions has been monitored.
Coatings have been designed to reduce wall friction in underwater conditions. The tests were conducted on aluminum panels, suitably microstructured with hierarchical structured and functionalized with fluorine-free compounds. The reduction of wall friction was measured in the cavitational tunnel, evaluating the reduction of wall hydrodynamic force as the water flow speed increased. Hydrodynamic force tests are conducted on surface rotors of a suitably nanostructured and functionalized rheometer for the alternative evaluation of morphological potentials easily applicable in future perspective. The ultimate goal was to find a superhydrophobic coating that combined antifouling and friction reduction properties
Development of a damage detection technique with a strong immunity to environmental influence implemented on a laboratory truss girder subjected to ambient variations
No full textIn recent years, Structural Health Monitoring (SHM) has become a fundamental branch of industrial, aerospace and civil engineering, offering a various array of techniques designed to predict, assess, and manage potential structural damage. Most of the Structural Health Monitoring (SHM) strategies rely on examining the damage-sensitive features of the system under analysis and conducting ongoing monitoring, evaluating the evolution in time of these characteristics. Several methods have been developed in recent years, some of the most promising ones are based on vibration analysis. This PhD thesis focuses on the development of an unsupervised learning approach for detecting structural damage based on vibrations. The benchmark of the work involves a laboratory truss girder designed ad-hoc for the development of damage detection techniques based on acceleration measurements. The approach is based on statistical pattern recognition and is focused on two key steps: the selection of damage-sensitive features and the automation of the outlier detection process. Particular consideration is given to the practical application of this approach, involving sparse sensor layout, handling long-term monitoring data, and suggesting synthetic damage indicators to aid in the maintenance decision-making process. The validation of the algorithm is performed on real data computed from the laboratory truss girder acceleration measurements. The laboratory structure has been monitored for several months under uncontrolled environmental factors such as ambient temperature drift and sunlight exposure evolution
The role of subjective cognitive decline in the evolution towards mild cognitive impairment and dementia : a systematic review
No full textIl presente elaborato, mediante una revisione sistematica della letteratura più aggiornata, si propone di indagare il ruolo del declino cognitivo soggettivo (SCD) come potenziale precursore di un declino cognitivo oggettivo e quindi come fattore di rischio per lo sviluppo di Mild Cognitive Impairment (MCI) e demenza. Il declino cognitivo soggettivo descrivere una condizione di peggioramento delle capacità cognitive auto-percepito rispetto alle prestazioni precedenti, senza evidenza oggettiva di deficit nei test neuropsicologici, per cui i clinici devono fare affidamento sui resoconti forniti direttamente dagli individui. Questa condizione, sebbene in alcuni casi può essere attribuita al normale processo di invecchiamento o a una varietà di fattori psicologici, ambientali o patologici, è comunemente considerata una tappa probabile nella progressione dell’Alzheimer, collocandosi tra la fase preclinica e quella prodromica. La ricerca scientifica degli ultimi due decenni ha evidenziato che il declino cognitivo soggettivo è associato a una maggiore probabilità di sviluppare demenza rispetto ai soggetti che non lamentano disturbi cognitivi. Quindi, è fondamentale riconoscere che questa condizione clinica potrebbe rappresentare un segnale precoce di una patologia neurodegenerativa emergente, il che rende questi soggetti un gruppo di studio di grande interesse sia per la ricerca che per la pratica clinica. L'identificazione precoce del declino cognitivo soggettivo potrebbe essere cruciale per individuare coloro che hanno un rischio maggiore di sviluppare la demenza. Questo non solo permetterebbe l'attuazione di misure preventive più efficaci, ma consentirebbe anche di monitorare da vicino la progressione della malattia e di avviare tempestivamente interventi terapeutici volti a contrastare la demenza negli anziani. The current work, by means of a systematic review of the most recent literature, aims to investigate the role of Subjective Cognitive Decline (SCD) as a potential precursor of objective cognitive decline and thus as a risk factor for the development of Mild Cognitive Impairment (MCI) and dementia. Subjective cognitive decline describes a condition of self-perceived worsening of cognitive abilities compared to previous performance, without objective evidence of deficits in neuropsychological tests, so clinicians must rely on reports provided directly by individuals. This condition, although in some cases it may be attributed to the normal ageing process or to a variety of psychological, environmental or pathological factors, is commonly considered a likely stage in the progression of Alzheimer's, lying between the preclinical and prodromal phases. Scientific research over the last two decades has highlighted that subjective cognitive decline is associated with a higher likelihood of developing dementia than subjects who do not complain of cognitive disorders. Therefore, it is crucial to recognise that this clinical condition may represent an early sign of an emerging neurodegenerative disease, which makes these subjects a study group of great interest for both research and clinical practice. Early identification of subjective cognitive decline could be crucial in identifying those at increased risk of developing dementia. This would not only enable the implementation of more effective preventive measures, but would also allow the progression of the disease to be closely monitored and therapeutic interventions aimed at combating dementia in the elderly to be initiated at an early stage
Physico-chemical characteristics and processing properties of milk from Reggiana cattle breed
No full textThe aim of this thesis is to study the effect of days in lactation and number of parities on the
chemical composition, the physico-chemical and processing properties of milk from Reggiana
cattle breed. To this aim, 78 individual milk samples of Reggiana cattle breed were collected from
two dairy farms involved in Parmigiano Reggiano cheese production. Days in lactation
significantly impact protein, casein and lactose level, with protein concentration increasing as
lactation progresses. Parity order affects protein, casein, somatic cell counts and pH, with older
cows showing higher somatic cell counts. The study reveals that higher fat, protein and casein
level enhance cheese yield and solid recovery. These findings underscore the importance of
managing lactation stages and parity to optimize milk quality and cheese yield
Surrogate models, physics-informed neural networks and climate change
No full textThis research contributes to the advancement of surrogate modelling as a powerful technique in the field of computational simulation that offers numerous advantages for solving complex problems efficiently. In particular, this study emphasizes the pivotal role of surrogate modeling in groundwater management. By integrating key factors like climate change and leveraging machine learning, particularly neu-ral networks, the research facilitates more informed decision-making, significantly reducing the computational cost of complex numerical models.
The impact of climate change is a central focus and the first study aims to construct surrogate data-driven models for evaluating climate change effects on groundwater resources, also in the future. The study involves a comparison between statistical methods and different types of artificial neural networks (ANNs). The ef-fectiveness of surrogate models was demonstrated in Northern Tuscany (Italy) but can easily extend to any area of interest. The adopted statistical method involves analyzing historical precipitation and temperature data along with groundwater levels recorded in monitoring wells. Initially, the study explores potential correla-tions between meteorological and groundwater indices; if a correlation is identified, a linear regression analysis is employed to establish relationships between them. These established relationships are then used to estimate future groundwater le-vels based on projected precipitation and temperature obtained from an ensemble of Regional Climate Models, under two Representative Concentration Pathways, namely RCP4.5 and RCP8.5.
Then, three distinct Artificial Intelligence (AI) models, Nonlinear AutoRegres-sive with eXogenous inputs (NARX), Long-Short Term Memory (LSTM) and Con-volutional Neural Network (CNN) were implemented to evaluate the impact of cli-mate change on groundwater resources for the same case study. Specifically, these models were trained using directly historical precipitation and temperature data as input to provide groundwater levels as output. Following the training phase, the developed AI models were utilized to forecast future groundwater levels using the same precipitation and temperature projections and climate scenarios described above. The results highlighted different outputs among the models used in this work. However, most of them predict a decrease in groundwater levels as a result of future variations in precipitation and temperature. The study also presents the strengths and weaknesses of each model. Notably, the LSTM model emerges as the most promising approach to predict future groundwater levels.
Within the same field, an ANN was developed with the capability to simulate groundwater conditions in the Konya closed basin, Turkey, one of the pilot sites investigated as part of the InTheMED project. This model serves as a tool for examining the potential impacts of climate change and agricultural policies on groundwater resources within the region. The final goal of this application, is to provide a user-friendly tool, based on the trained neural network. The inherent simplicity of the surrogate model, with a straightforward interface and results that are simple to understand, plays a crucial role in decision-making processes.
Shifting to pollutant transport, an ANN was implemented to solve different direct and inverse problems. The direct problem deals with the evaluation of con-centrations in monitoring wells, while the inverse problem involves the identifica-tion of contaminant sources and their release history. It demonstrated efficiency in addressing both direct and inverse transport problems, offering reliable results with reduced computational burden.
The study also addresses the interpretability challenge of ANNs and the so ca-lled “generalization problem” through Physics-Informed Neural Networks (PINNs). By incorporating physics-based constraints, PINNs bridge the gap between data-driven modeling and physics-based interpretations, offering a promising approach for groundwater numerical simulations. In this study, a PINN is developed to si-mulate flow in an unconfined aquifer.
Finally, two extra content are presented. First, an ANN is used to solve an inverse problem in the field of sewer systems. Then, an easily interpretable exam-ple of numerical groundwater flow modeling using spreadsheets, from a didactic perspective, is described.
In conclusion, this research underscores the importance of surrogate modeling, machine learning, climate change analysis, and physics-informed approaches in ad-vancing groundwater management strategies and beyond, providing valuable tools for decision-makers to address complex groundwater flow problems in changing environmental conditions.Questa ricerca propone nuovi avanzamenti nella modellazione surrogata che, nella simulazione di problemi complessi, offre vantaggi rilevanti a supporto della mo-dellazione numerica usuale. In particolare, questo studio sottolinea il ruolo cru-ciale della modellazione surrogata nella gestione delle risorse idriche sotterranee. Integrando fattori chiave come il cambiamento climatico e sfruttando l’appren-dimento automatico, in particolare le reti neurali, lo studio concorre a rendere più facile il processo decisionale informato, riducendo significativamente il costo computazionale dei complessi modelli numerici.
L’impatto del cambiamento climatico è al centro dell’attenzione e il primo studio mira a costruire modelli surrogati del tipo "data-driven" per valutare gli effetti del cambiamento climatico sulle risorse idriche sotterranee nel futuro. Esso confronta un metodo statistico e diversi tipi di reti neurali artificiali (ANN) per migliorare la comprensione e facilitare le decisioni nella gestione delle acque sotter-ranee. L’efficacia dei modelli surrogati è stata dimostrata in una applicazione nella Toscana settentrionale, ma può facilmente estendersi a qualsiasi area di interesse. Il metodo statistico adottato coinvolge l’analisi di dati storici sulle precipitazioni e sulla temperatura insieme ai livelli freatici registrati nei pozzi di monitoraggio. Ini-zialmente, lo studio esplora correlazioni potenziali tra indici meteorologici e indici delle acque sotterranee. Se viene individuata una valida correlazione tra questi, si costruisce una regressione lineare che stabilisce una relazione tra di essi. Queste relazioni vengono poi utilizzate per stimare futuri livelli di falda sulla base delle proiezioni di precipitazione e temperatura ottenute da un insieme di Modelli Cli-matici Regionali, considerando due Scenari di Emissione Rappresentativi, ovvero RCP4.5 e RCP8.5.
Successivamente, sono stati implementati tre distinti modelli di Intelligenza Ar-tificiale (AI), Rete neurale Autoregressiva con Ingressi Eterogenei (NARX), Rete Neurale con Memoria a Lungo e Breve Termine (LSTM) e Rete Neurale Convo-luzionale (CNN), per valutare l’impatto del cambiamento climatico sui livelli di falda per lo stesso caso di studio. In particolare, questi modelli sono stati adde-strati utilizzando direttamente dati storici di precipitazioni e di temperatura come input e per fornire i livelli freatici come output. Dopo la fase di addestramento, i modelli di AI sviluppati sono stati utilizzati per prevedere i livelli delle acque sotterranee utilizzando le stesse proiezioni di precipitazioni e temperatura e gli scenari climatici descritti in precedenza. I risultati hanno evidenziato diversi out-put tra i modelli utilizzati in questo studio. Tuttavia, la maggior parte di essi prevede una diminuzione dei livelli di falda a seguito di future variazioni di pre-cipitazione e temperatura. Lo studio presenta anche i punti di forza e debolezza di ciascun modello. In particolare, il modello LSTM emerge come l’approccio più promettente per prevedere i futuri livelli di falda.
Nello stesso campo, è stata sviluppata una rete neurale artificiale con la capa-cità di simulare lo stato dell’acquifero nel bacino di Konya, Turchia, uno dei siti pilota indagati nell’ambito del progetto InTheMED. Questo modello si compor-ta da strumento per esaminare gli impatti potenziali del cambiamento climatico e delle politiche agricole sulle risorse idriche sotterranee. L’obiettivo finale di questa applicazione è fornire uno strumento "user-friendly" basato sulla rete neu-rale addestrata. La semplicità intrinseca del modello surrogato, sviluppato con un’interfaccia chiara e risultati di facile comprensione, svolge un ruolo cruciale nei processi decisionali.
Passando al trasporto di inquinanti, è stata implementata una rete neurale artificiale per risolvere diversi problemi diretti e inversi. Il problema diretto ri-guarda la valutazione delle concentrazioni nei pozzi di monitoraggio, mentre il problema inverso comporta l’identificazione delle fonti di contaminazione e la loro storia di rilascio. La tecnica ha dimostrato efficienza nell’affrontare sia problemi diretti che inversi di trasporto, offrendo risultati affidabili con un ridotto onere computazionale.
Lo studio affronta anche la sfida dell’interpretabilità fisica delle reti neurali artificiali e del cosiddetto "problema della generalizzazione" attraverso le Reti Neurali Fisicamente Basate (PINN). Integrando vincoli basati sulla fisica, le PINN colmano il divario tra la modellazione basata sui dati e i modelli numerici costruiti sulle equazioni differenziali dedotte dalla fisica, offrendo un approccio promettente per le simulazioni numeriche delle acque sotterranee. In questo studio, una PINN è stata sviluppata per simulare il flusso in un acquifero non confinato.
Infine, vengono presentati due contenuti aggiuntivi. Nel primo, una rete neurale artificiale è utilizzata per risolvere un problema inverso nel campo dei sistemi fognari. In secondo, un esempio di modellazione numerica del flusso delle acque sotterranee mediante fogli di calcolo con una ottima prospettiva didattica.
In conclusione, questa ricerca sottolinea l’importanza della modellazione sur-rogata, dell’apprendimento automatico, dell’analisi del cambiamento climatico e degli approcci basati sulla fisica per progredire nelle strategie di gestione delle ac-que sotterranee e affrontare sfide complesse, offrendo strumenti preziosi ai decisori
Ruolo dei macrofagi nelle patologie metaboliche: focus su aterosclerosi e fibrosi epatica
No full textI macrofagi sono cellule del sistema immunitario caratterizzate da una significativa eterogeneità e plasticità che permette loro di adattarsi alle variazioni dinamiche del microambiente e di contribuire quindi al mantenimento dell’omeostasi. Quasi tutti i tessuti dell’organismo sono popolati da macrofagi residenti (trMacs), i quali, rispondendo a segnali tessuto-specifici, giocano un ruolo chiave nell’infiammazione, nei processi di riparazione, nella difesa dell’ospite contro agenti patogeni e nell’eliminazione delle cellule morte e/o senescenti. I trMacs si sviluppano durante l'embriogenesi e si sostengono attraverso meccanismi di auto-rinnovamento indipendenti dai monociti circolanti. Tuttavia, a seguito di danni, i monociti sono reclutati nel tessuto compromesso dove differenziano in macrofagi specializzati aventi fenotipo e funzioni distinguibili rispetto a quelli residenti. Anche se questo segna uno step fondamentale nella risposta omeostatica alla guarigione delle ferite, il danno tissutale persistente può innescare o aggravare condizioni patologiche. Negli ultimi anni si è manifestato un crescente interesse per lo studio e lo sviluppo di strategie mirate direttamente sui macrofagi, poiché la loro capacità di rispondere dinamicamente all'ambiente circostante li rende attori chiave in molti meccanismi patogenetici.
Questo studio aveva l’obiettivo di esplorare gli effetti della modulazione farmacologica dell'asse sfingosina 1-fostato (S1P)/recettori di S1P (S1PRs) sul metabolismo del colesterolo nei macrofagi, i quali ricoprono un ruolo centrale nello sviluppo e nella progressione delle lesioni aterosclerotiche. Parallelamente, si è proposto di approfondire il ruolo del fattore di trascrizione Bhlhe40 nelle cellule mieloidi nel contesto della fibrosi epatica, con particolare attenzione al crosstalk tra i macrofagi epatici (Kupffer cells e macrofagi derivati da monociti) e le cellule stellate epatiche.
Il nostro studio suggerisce che la stimolazione farmacologica dei recettori di S1P potrebbe favorire l'omeostasi del colesterolo nei macrofagi, riducendo l'accumulo lipidico e promuovendo l'efflusso verso le lipoproteine. Questa strategia rappresenterebbe, quindi, un nuovo possibile approccio terapeutico per la malattia cardiovascolare aterosclerotica. Inoltre, la nostra ricerca ha rivelato il potenziale coinvolgimento del fattore di trascrizione mieloide Bhlhe40 nella fibrosi epatica. La modulazione dell'infiammazione e la regolazione della produzione di citochine, associate alla carenza di Bhlhe40, sembrano contribuire a una significativa riduzione dell'infiltrazione di leucociti e all'inibizione dell'attivazione delle cellule stellate epatiche.Macrophages are immune cells characterized by significant heterogeneity and plasticity, allowing them to adapt to dynamic changes in the microenvironment and contribute to maintaining homeostasis. Nearly all tissues are populated by resident macrophages (trMacs), which, in response to tissue-specific signals, play a key role in inflammation, repair processes, host defense against pathogens and clearance of dead and/or senescent cells. trMacs develop during embryogenesis and are sustained by self-renewal mechanisms independent of circulating monocytes. However, following injury, monocytes are recruited to the compromised tissue where they differentiate into specialized macrophages with distinct phenotypes and functions compared to resident counterparts. Although this represents a crucial step in the homeostatic response to wound healing, persistent tissue damage can trigger or exacerbate pathological conditions. In the last few years, a growing interest has emerged in studying and developing strategies that directly target macrophages, since their ability to dynamically respond to the surrounding environment makes them key players in many pathogenetic mechanisms.
This study aimed to explore the effects of pharmacological modulation of the sphingosine 1-phosphate (S1P)/S1P receptors (S1PRs) axis on cholesterol metabolism in macrophages, which play a central role in the development and progression of atherosclerotic lesions. Concurrently, our study set out to investigate the role of the transcription factor Bhlhe40 in myeloid cells in the context of hepatic fibrosis, with a specific focus on the crosstalk between hepatic macrophages (Kupffer cells and monocyte-derived macrophages) and hepatic stellate cells. Our preliminary data suggest that the pharmacological stimulation of S1P receptors could promote cholesterol homeostasis in macrophages, reducing lipid accumulation and facilitating efflux to lipoproteins. This strategy would, therefore, represent a new potential therapeutic approach for atherosclerotic cardiovascular disease. Moreover, our investigation has identified a potential implication of the myeloid transcription factor Bhlhe40 in hepatic fibrosis. The modulation of inflammation and the regulation of cytokine production, associated with Bhlhe40 deficiency, seem to be key features in substantially reducing the infiltration of leukocytes and inhibition of hepatic stellate cell activation
Development of innovative 3D-printable liquid coolers for power semiconductor devices and modules
No full textL’attività di ricerca principale del mio progetto di ricerca del dottorato ha riguardato la progettazione di dissipatori a liquido metallici fabbricabili con stampa 3D per dispositivi a semiconduttore di potenza. Una prima parte del lavoro, in collaborazione con l’azienda Poseico S.p.A. di Genova, ha riguardato la progettazione di coldplate per dispositivi di potenza di tipo press-pack. In particolare, con apposite campagne di simulazioni termofluidodinamiche agli elementi finiti, sono state ottimizzate alcune geometrie dotate di blocco interno di canali paralleli. Dopo la fabbricazione di una prima versione, durante le caratterizzazioni con apposito banco di misura sono emersi alcuni problemi di ostruzione dei canali interni, con conseguente comparsa di punti caldi sulle superfici di scambio. Si è quindi proceduto con l’ottimizzazione del progetto, introducendo raccordi ad imbuto e migliorando i parametri di stampa, in particolare riducendo il diametro della polvere di metallo utilizzata. L’ultima versione prodotta ha mostrato prestazioni molto simili a quelle previste con le simulazioni. Il dissipatore realizzato, ormai considerabile un prodotto maturo, è pronto per le prove su un convertitore completo, prima della definitiva immissione nel mercato.
Una seconda parte dell’attività di ricerca, ancora in collaborazione con Poseico S.p.A., ha riguardato la progettazione di un dissipatore analogo a quello descritto in precedenza, ma di forma rettangolare e adatto al raffreddamento di 3 moduli di potenza. Le simulazioni, con le quali sono state confrontate le nuove geometrie lamellari con quelle tradizionali a serpentina, hanno mostrato un notevole miglioramento delle prestazioni, in particolare per quanto riguarda la perdita di carico, notevolmente ridotta, a parità di portata. La fabbricazione di un prototipo e le relative caratterizzazioni con banco di misura sono pianificate per i prossimi mesi.
Un’ulteriore attività importante del mio percorso di dottorato ha riguardato la progettazione di cooler diretti per moduli di potenza. Tali dispositivi consistono in vasche stampate in 3D dotate di ugelli, dai quali fuoriesce il liquido refrigerante che colpisce direttamente il baseplate del modulo da raffreddare,
riducendo il percorso termico rispetto alle soluzioni tradizionali. È stata realizzata una prima versione quadrata per un resistore da 600 W, sia con 9 che con 16 ugelli. Con opportune campagne di simulazione e un algoritmo di ottimizzazione automatica si è proceduto all’ottimizzazione del diametro degli ugelli e della loro altezza. Le prestazioni ottenute dai primi prototipi sono in linea con quanto previsto dalle simulazioni. Altre attività di ricerca marginali svolte sono descritte nella tesi, con i principali risultati ottenuti e le relative considerazioni.The main research activity of my PhD research project involved the design of 3D-printable metallic liquid heatsinks for power semiconductor devices. A first part of the work, in collaboration with the company Poseico S.p.A. of Genoa, concerned the design of cold plates for press-pack type power devices. In particular, with specific finite element thermo-fluid dynamic simulation campaigns, some geometries equipped with an internal block of parallel channels were optimised. After the manufacturing of a first version, during the characterizations with a specific measuring bench, some problems of obstruction of the internal channels emerged, with the consequent appearance of hot spots on the exchange surfaces. We then proceeded with the optimization of the project, introducing funnel fittings and improving the printing parameters, in particular by reducing the diameter of the metal powder used. The latest version produced showed performance very similar to the ones estimated by FEM simulations. The heat sink created, now considered a mature product, is ready for testing on a complete converter, before being definitively placed on the market.
A second part of the research activity, again in collaboration with Poseico S.p.A., concerned the design of a heat sink similar to the one described previously, but with a rectangular shape and suitable for cooling 3 power modules. The simulations, with which the new lamellar geometries were compared with the traditional serpentine ones, showed a notable improvement in performance, in particular with regard to the significantly reduced pressure drop, for the same flow rate. The fabrication of a prototype and the related characterizations with a measurement bench are planned for the next few months.
A further important activity of my doctoral career concerned the design of direct coolers for power modules. These devices consist of 3D printed tanks equipped with nozzles, from which the refrigerant liquid comes out and directly hits the baseplate of the module to be cooled,
reducing the thermal path compared to traditional solutions. A first square version was made for a 600-W resistor, with both 9 and 16 nozzles. With appropriate simulation campaigns and an automatic optimization algorithm, the diameter of the nozzles and their height were optimized. The performances obtained from the first prototypes are in line with what was predicted by the simulations. Other marginal research activities carried out are described in the thesis, with the main results obtained and the related considerations
Modelling of hydro-geomorphological processes related to sediment transport: case study of the baganza river (Italy)
No full textSediment transport and the processes that shape river landscapes have a significant impact on flood dynamics. This interdisciplinary study combines Earth Sciences and Hydraulic Engineering principles to investigate sediment bahaviour within the Baganza River catchment in Northern Italy. The research particularly focuses on developing a robust sediment transport numerical model, which is essential for simulating the bed morphological changes in river systems.
It's imperative to note that while numerical models are valuable tools, their effectiveness depends on addressing various limitations, such as spatial and temporal scales, data requirements, model complexity, numerical stability, and computational demands. This research aims to overcome these constraints by examining a 2D numerical model specifically designed for simulating bedload transport in real-world applications. The model incorporates weak coupling of shallow water and Exner equations, which represent the liquid and solid phases, respectively.
Besides the modification and improvement of the pre-existing numerical model, one of the novelties of this research lies in the fact that it incorporates actual grain size obtained from field-derived data, used as input parameter for the model. The technique employed for obtaining granulometric distribution is of hybrid nature i.e., combining sieve analysis and photogrammetric technique. This offers an efficient and cost-effective approach, enhancing sediment analysis precision while reducing fieldwork demands. By applying the built model to the Baganza River, it provides valuable insights into sediment conveyance, flow-bed interactions, erosion, and deposition.
To estimate sediment discharge, equations by Meyer-Peter and Müller (1948) and Smart (1984) were employed, which are optimized for computational efficiency using Graphics Processing Unit (GPU) parallelization. Hereafter, Smart (1984) will be referred to as the SMART and Meyer-Peter and Müller (1948) as the MPM approach. The model simulates morphological transformations that occurred in the Baganza River between 2008 and 2014, utilizing a high-resolution (4 m x 4 m) Digital Terrain Model (DTM). The model's accuracy is validated by benchmark testing against 1D and 2D dam break scenarios with mobile bed conditions, including sensitivity analyses of model input parameters. In the absence of calibration data, sensitivity analysis explores the influence of key input parameters on the predictive capabilities of the bedload transport model. Three distinct cases are considered, each examining the model's response to alterations in specific parameters, i.e. median grain size D50 and manning roughness “n” value while keeping other variables constant. The results of the sensitivity analysis shed light on the performance of the 2D sediment transport model.
The findings contribute to best parameter selection and model enhancements, ultimately improving the model's predictive capabilities. Furthermore, the analysis of 2D model outputs related to the Baganza River delineates eight distinct in-channel bed Morphological Units (MU’s), based on thresholds measuring depth and Froude number. These units encompass pools, runs, chutes, riffles, riffle transitions, fast glides, slow glides, and slackwater zones.
In conclusion, this research underscores the significance of an interdisciplinary approach in comprehending the intricate sediment dynamics of riverine environments
Food: graphic tools for knowledge transfer
No full textLa tesi analizza il trasferimento della conoscenza nel settore alimentare, evidenziando come l'uso di strumenti grafici e metodologie comunicative possa migliorare la comprensione delle informazioni, rendendole più efficaci. L'attenzione si è focalizzata sui principali destinatari della conoscenza—maestranze, agenti di vendita e clienti—e sulle modalità attraverso cui questa viene trasmessa, considerando in particolare gli studi del Professor Luigi Odello sull'analisi sensoriale.
Dalla ricerca è emerso che le tecniche tradizionali di trasferimento della conoscenza stanno subendo un'evoluzione verso modelli più flessibili, dinamici e interattivi. Sebbene i metodi classici, come le Academy, i corsi e le visite aziendali, rimangono fondamentali, vengono ora integrati con nuove strategie formative, in risposta a un pubblico sempre più variegato. Anche le visite ai clienti e la presenza su siti web e social media sono diventate parte integrante di una comunicazione finalizzata a costruire fiducia e trasparenza, rafforzando il legame con i destinatari.
Un'importante proposta di questa tesi è la nuova formula di trasferimento della conoscenza, articolata in tre modalità: formazione ibrida, corale e continua.
Per garantire un efficace trasferimento della conoscenza nel settore alimentare, è essenziale adottare strategie che si adattino ai cambiamenti e sfruttino al meglio gli strumenti grafici e le nuove tecnologie. Solo attraverso un approccio innovativo e diversificato si possono assicurare la comprensione, la memorizzazione e l'applicazione efficace delle informazioni.This thesis analyzes knowledge transfer in the food industry, highlighting how the use of graphic tools and communication methodologies can improve the understanding of information, making it more effective. The focus was on the main recipients of knowledge-workers, sales agents and customers-and the ways through which it is transmitted, considering in particular Professor Luigi Odello's studies on sensory analysis.
The research found that traditional knowledge transfer techniques are undergoing an evolution toward more flexible, dynamic and interactive models. Although classic methods, such as academies, courses and company visits, remain fundamental, they are now being supplemented with new training strategies in response to an increasingly diverse audience. Customer visits and presence on websites and social media have also become an integral part of communication aimed at building trust and transparency, strengthening the bond with the target audience.
An important proposition of this thesis is the new knowledge transfer formula, articulated in three modes: hybrid, choral and continuous training.
To ensure effective knowledge transfer in the food sector, it is essential to adopt strategies that adapt to changes and make the best use of graphic tools and new technologies. Only through an innovative and diverse approach can understanding, memorization and effective application of information be ensured