Sviluppo di sperimentazioni per la produzione di getti in lega di alluminio a caratteristiche difettologiche e microstrutturali controllate, per la validazione di modelli di previsione delle porosità

Nei processi di progettazione e produzione tramite tecnologie di colata di componenti in alluminio ad elevate prestazioni, risulta fondamentale poter prevedere la presenza e la quantità di difetti correlabili a design non corretti e a determinate condizioni di processo. Fra le difettologie più comuni di un getto in alluminio, le porosità con dimensioni di decine o centinaia di m, note come microporosità, hanno un impatto estremamente negativo sulle caratteristiche meccaniche, sia statiche che a fatica. In questo lavoro, dopo un’adeguata analisi bibliografica, sono state progettate e messe a punto attrezzature e procedure sperimentali che permettessero la produzione di materiale a difettologia e microstruttura differenziata, a partire da condizioni di processo note ed accuratamente misurabili, che riproducessero la variabilità delle stesse nell’ambito della reale produzione di componenti fusi. Tutte le attività di progettazione delle sperimentazioni, sono state coadiuvate dall’ausilio di software di simulazione del processo fusorio che hanno a loro volta beneficiato di tarature e validazioni sperimentali ad hoc. L’apparato sperimentale ha dimostrato la propria efficacia nella produzione di materiale a microstruttura e difettologia differenziata, in maniera robusta e ripetibile. Utilizzando i risultati sperimentali ottenuti, si è svolta la validazione di un modello numerico di previsione delle porosità da ritiro e gas, ritenuto ad oggi allo stato dell’arte e già implementato in alcuni codici commerciali di simulazione del processo fusorio. I risultati numerici e sperimentali, una volta comparati, hanno evidenziato una buona accuratezza del modello numerico nella previsione delle difettologie sia in termini di ordini di grandezza che di gradienti della porosità nei getti realizzati.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Development of numerical methodologies to predict the liquid fuel sprays - wall interaction to optimize the mixing process of direct injection spark ignition engines

Nowadays the development of new Internal Combustion Engines is mainly driven by the need to reduce tailpipe emissions of pollutants, Green-House Gases and avoid the fossil fuels wasting. The design of dimension and shape of the combustion chamber together with the implementation of different injection strategies e.g., injection timing, spray targeting, higher injection pressure, play a key role in the accomplishment of the aforementioned targets. As far as the match between the fuel injection and evaporation and the combustion chamber shape is concerned, the assessment of the interaction between the liquid fuel spray and the engine walls in gasoline direct injection engines is crucial. The use of numerical simulations is an acknowledged technique to support the study of new technological solutions such as the design of new gasoline blends and of tailored injection strategies to pursue the target mixture formation. The current simulation framework lacks a well-defined best practice for the liquid fuel spray interaction simulation, which is a complex multi-physics problem. This thesis deals with the development of robust methodologies to approach the numerical simulation of the liquid fuel spray interaction with walls and lubricants. The accomplishment of this task was divided into three tasks: i) setup and validation of spray-wall impingement three-dimensional CFD spray simulations; ii) development of a one-dimensional model describing the liquid fuel – lubricant oil interaction; iii) development of a machine learning based algorithm aimed to define which mixture of known pure components mimics the physical behaviour of the real gasoline for the simulation of the liquid fuel spray interaction.

Numerical modeling and experimental validation of the recrystallization behaviour in the extrusion of 6XXX aluminum alloys

The microstructure of 6XXX aluminum alloys deeply affects mechanical, crash, corrosion and aesthetic properties of extruded profiles. Unfortunately, grain structure evolution during manufacturing processes is a complex phenomenon because several process and material parameters such as alloy chemical composition, temperature, extrusion speed, tools geometries, quenching and thermal treatment parameters affect the grain evolution during the manufacturing process. The aim of the present PhD thesis was the analysis of the recrystallization kinetics during the hot extrusion of 6XXX aluminum alloys and the development of reliable recrystallization models to be used in FEM codes for the microstructure prediction at a die design stage. Experimental activities have been carried out in order to acquire data for the recrystallization models development, validation and also to investigate the effect of process parameters and die design on the microstructure of the final component. The experimental campaign reported in this thesis involved the extrusion of AA6063, AA6060 and AA6082 profiles with different process parameters in order to provide a reliable amount of data for the models validation. A particular focus was made to investigate the PCG defect evolution during the extrusion of medium-strength alloys such as AA6082. Several die designs and process conditions were analysed in order to understand the influence of each of them on the recrystallization behaviour of the investigated alloy. From the numerical point of view, innovative models for the microstructure prediction were developed and validated over the extrusion of industrial-scale profiles with complex geometries, showing a good matching in terms of the grain size and surface recrystallization prediction. The achieved results suggest the reliability of the developed models and their application in the industrial field for process and material properties optimization at a die-design stage.

Validation of a new method for human nerve decellularization: toward a new tool in peripheral nerve reconstructive surgery.

Defects of the peripheral nervous system are extremely frequent in trauma and surgeries and have high socioeconomic costs. In case of peripheral nerve injury, the first approach is primary neurorrhaphy, which is direct nerve repair with epineural microsutures of the two stumps. However, this is not feasible in case of stump retraction or in case of tissue loss (gap > 2 cm), where the main surgical options are autologous grafts, allogenic grafts, or nerve conduits. While the gold standard is the autograft, it has disadvantages related to its harvesting, with an inevitable donor site morbidity and functional deficit. Fresh nerve allografts have therefore become a viable alternative option, but they require immunosuppression, which is often contraindicated. Acellular Nerve Allografts (ANA) represent a valid alternative, they do not need immunosuppression and appear to be safe and effective based on recent studies. The purpose of this study is to propose and develop an innovative method of nerve decellularization (Rizzoli method), conforming to cleanroom requirements in order to perform the direct tissue manipulation step and the nerve decellularization process within five hours, so as to accelerate the detachment of myelin and cellular debris, without detrimental effects on nerve architecture. In this study, the safety and the efficacy of the new method are evaluated in vitro and in vivo by histological, immunohistochemical, and histomorphometric studies in rabbits and humans. The new method is rapid, safe, and cheaper if compared with available commercial ANAs. The present study shows that the method, previously optimized in vitro and in vivo on animal model presented by our group, can be applied on human nerve samples. This work represents the first step in providing a novel, safe, and inexpensive tool for use by European tissue banks to democratize the use of nerve tissue transplantation for nerve injury reconstruction.