Modellazione Tridimensionale del Processo di Combustione di un Razzo a Propellente Solido

La regolazione dei sistemi di propulsione a razzo a propellente solido (Solid Rocket Motors) ha da sempre rappresentato una delle principali problematiche legate a questa tipologia di motori. L’assenza di un qualsiasi genere di controllo diretto del processo di combustione del grano solido, fa si che la previsione della balistica interna rappresenti da sempre il principale strumento utilizzato sia per definire in fase di progetto la configurazione ottimale del motore, sia per analizzare le eventuali anomalie riscontrate in ambito sperimentale. Variazioni locali nella struttura del propellente, difettosità interne o eterogeneità nelle condizioni di camera posso dare origine ad alterazioni del rateo locale di combustione del propellente e conseguentemente a profili di pressione e di spinta sperimentali differenti da quelli previsti per via teorica. Molti dei codici attualmente in uso offrono un approccio piuttosto semplificato al problema, facendo per lo più ricorso a fattori correttivi (fattori HUMP) semi-empirici, senza tuttavia andare a ricostruire in maniera più realistica le eterogeneità di prestazione del propellente. Questo lavoro di tesi vuole dunque proporre un nuovo approccio alla previsione numerica delle prestazioni dei sistemi a propellente solido, attraverso la realizzazione di un nuovo codice di simulazione, denominato ROBOOST (ROcket BOOst Simulation Tool). Richiamando concetti e techiche proprie della Computer Grafica, questo nuovo codice è in grado di ricostruire in processo di regressione superficiale del grano in maniera puntuale, attraverso l’utilizzo di una mesh triangolare mobile. Variazioni locali del rateo di combustione posso quindi essere facilmente riprodotte ed il calcolo della balistica interna avviene mediante l’accoppiamento di un modello 0D non-stazionario e di uno 1D quasi-stazionario. L’attività è stata svolta in collaborazione con l’azienda Avio Space Division e il nuovo codice è stato implementato con successo sul motore Zefiro 9.

Analysis of the main phenomena affecting solid rocket motors internal ballistics

In solid rocket motors, the absence of combustion controllability and the large amount of financial resources involved in full-scale firing tests, increase the importance of numerical simulations in order to asses stringent mission thrust requirements and evaluate the influence of thrust chamber phenomena affecting the grain combustion. Among those phenomena, grain local defects (propellant casting inclusions and debondings), combustion heat accumulation involving pressure peaks (Friedman Curl effect), and case-insulating thermal protection material ablation affect thrust prediction in terms of not negligible deviations with respect to the nominal expected trace. Most of the recent models have proposed a simplified treatment to the problem using empirical corrective functions, with the disadvantages of not fully understanding the physical dynamics and thus of not obtaining predictive results for different configurations of solid rocket motors in a boundary conditions-varied scenario. This work is aimed to introduce different mathematical approaches to model, analyze, and predict the abovementioned phenomena, presenting a detailed physical interpretation based on existing SRMs configurations. Internal ballistics predictions are obtained with an in-house simulation software, where the adoption of a dynamic three-dimensional triangular mesh together with advanced computer graphics methods, allows the previous target to be reached. Numerical procedures are explained in detail. Simulation results are carried out and discussed based on experimental data.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Understanding 3D structures from sketches

Sketches are a unique way to communicate: drawing a simple sketch does not require any training, sketches convey information that is hard to describe with words, they are powerful enough to represent almost any concept, and nowadays, it is possible to draw directly from mobile devices. Motivated from the unique characteristics of sketches and fascinated by the human ability to imagine 3D objects from drawings, this thesis focuses on automatically associating geometric information to sketches. The main research directions of the thesis can be summarized as obtaining geometric information from freehand scene sketches to improve 2D sketch-based tasks and investigating Vision-Language models to overcome 3D sketch-based tasks limitations. The first part of the thesis concerns geometric information prediction from scene sketches improving scene sketch to image generation and unlocking new creativity effects. The thesis proceeds showing a study conducted on the Vision-Language models embedding space considering sketches, line renderings and RGB renderings of 3D shape to overcome the use of supervised datasets for 3D sketch-based tasks, that are limited and hard to acquire. Following the obtained observations and results, Vision-Language models are applied to Sketch Based Shape Retrieval without the need of training on supervised datasets. We then analyze the use of Vision-Language models for sketch based 3D reconstruction in an unsupervised manner. In the final chapter we report the results obtained in an additional project carried during the PhD, which has lead to the development of a framework to learn an embedding space of neural networks that can be navigated to get ready-to-use models with desired characteristics.