Descrizione:
Due to the peculiar transport properties of organic semiconductors, ad-hoc codes, currently commercially unavailable, are necessary to properly simulate organic-based devices. In the framework of a collaboration with Dipartimento di Matematica and IIT-CNST, the candidate will employ a specially designed 2D code to simulate organic devices, and will validate numerical findings by means of experimental measurements. The final aim is the measurement of the shape of the density of states by means of simple measurements on transistor structures, extending and simplifying previous studies of the group (https://www.nature.com/articles/s41598-017-03882-8).

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The electrification of agricolture vehicles and implements requires the study of new architectures able to manage power on board respect to the need of the several devices mounted. Each of them is on the same DC bus, but with different needs in term of power supply and energy access. Aim of the thesis is to implement a simulation model suitable for the testing and validation of this architectures.

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Plastic transistor performances are constantly improving. Amon the others, the enhancement of their frequency behaviour is mandatory to successfully address IoT, flexible, large-area applications. This thesis aims at elucidating the interplay between transistor geometry and and semiconductor mobility in the definition of the transition frequency, with a combined experimental and numerical approach.

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Distribution grids probabilistic analysis is an essential step in order to assess the daily network operability under uncertain and stress conditions. It is also functional to the development of new services that require load growth capacity or to the exploitation of new energy resources affected by uncertainty. Efficient numerical tools able to forecast the possible scenarios while accounting for loads and sources uncertainty are thus of paramount importance. The majority of available uncertainty-aware predictive tools are based on Monte Carlo analysis which allows probabilistic evaluations of the network state at the price of time consuming simulations. The aim of the thesis is to develop new algorithm for Probabilistic load Flow

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Machine learning/Artificial Intelligence has the potential to accelerate technological advances in leaps and bounds. The aim of this project is use machine learning to learn from Pareto-front results in the case of magnetic design, and generate component outputs without the need of going through the Pareto-front analysis, in order to reduce computation time, in order to generate information to aid power system design

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L’obiettivo della tesi è la modellazione del campo elettrico, della distribuzione di cariche e della temperatura all’interno dell’isolante di cavi HVDC in condizioni non stazionarie. La conoscenza delle grandezze fisiche succitate ricopre un’importanza rilevante per la progettazione dei cavi HVDC ma è assai difficile ottenerla tramite misure su cavi in opera. Lo sviluppo di un modello numerico è sfidante in quanto deve poter descrivere accuratamente 1) fenomeni con tempi caratteristici diversi 2) geometrie con fattori di forma estremi e 3) materiali con microstruttura complessa.

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