Separation control by means of oriented DBD plasma actuators: an experimental analysis in a wind tunnel

This thesis provides an experimental analysis of the effectiveness of oriented DBD plasma actuators over a NACA 0015 airfoil at low Reynolds numbers. Tests were performed in partnership with the Department of Electrical Engineering of Bologna University, in the wind tunnel of the Applied Aerodynamics Laboratory of Aerospace Engineering faculty. Lift coefficient measurements were carried out in order to verify how an oriented plasma jet succeeds in prevent boundary layer separation. Both actuators’ chord wise position and plasma jet orientation angle have been investigated to examine which configurations lead to the best results. A particular attention has been paid also to the analysis of results in steady and unsteady plasma actuation. Questa tesi offre un’analisi sperimentale sull’efficacia di attuatori al plasma orientabili, basati su una tecnologia DBD, installati su un profilo alare NACA 0015, a bassi numeri di Reynolds. Le prove sono state condotte in collaborazione con il Dipartimento di Ingegneria Elettrica dell’Università di Bologna, nella galleria del vento del Laboratorio di Aerodinamica Applicata della Facoltà di Ingegneria Aerospaziale di Forlì. Per verificare come un getto orientabile di plasma riesca a prevenire la separazione dello strato limite, sono state eseguite misure sul coefficiente di portanza. Sono state indagate sia la posizione degli attuatori lungo la corda che l’angolo con cui è orientato il getto di plasma, per vedere quali configurazioni conducono ai migliori risultati. Una particolare attenzione è stata riservata all’analisi dei risultati ottenuti con plasma continuo e pulsato.

Life cycle assessment in construction field: A life cycle cost analysis of reinforcement concrete bridge

The present work is included in the context of the assessment of sustainability in the construction field and is aimed at estimating and analyzing life cycle cost of the existing reinforced concrete bridge “Viadotto delle Capre” during its entire life. This was accomplished by a comprehensive data collection and results evaluation. In detail, the economic analysis of the project is performed. The work has investigated possible design alternatives for maintenance/rehabilitation and end-of-life operations, when structural, functional, economic and also environmental requirements have to be fulfilled. In detail, the economic impact of different design options for the given reinforced concrete bridge have been assessed, whereupon the most economically, structurally and environmentally efficient scenario was chosen. The Integrated Life-Cycle Analysis procedure and Environmental Impact Assessment were also discussed in this work. The scope of this thesis is to illustrate that Life Cycle Cost analysis as part of Life Cycle Assessment approach could be effectively used to drive the design and management strategy of new and existing structures. The final objective of this contribution is to show how an economic analysis can influence decision-making in the definition of the most sustainable design alternatives. The designers can monitor the economic impact of different design strategies in order to identify the most appropriate option.

Stability of the tunnel face reinforced by bolts under seepage flow conditions

Il presente lavoro tratta la stabilità del fronte di scavo, rinforzato con barre di consolidamento ed interessato da drenaggi in avanzamento, di gallerie sotto falda in rocce tenere o terreni. Tale studio è stato sviluppato dal progetto di Tesi attraverso l’analisi all’equilibrio limite che approssima il fronte di scavo con un rettangolo e considera un meccanismo di rottura composto da un cuneo, a tergo del fronte, caricato da un prisma. Il metodo descritto consente di tenere conto dell’effetto stabilizzante delle barre, mediante una distribuzione della pressione di supporto non uniforme. Nel caso di gallerie sotto falda, lo stesso metodo permette inoltre di considerare l’effetto destabilizzante dei gradienti idraulici. Sono state ricavate soluzioni analitiche per la valutazione della stabilità, ed implementate successivamente nel software di analisi numerica MATLAB. Dalle analisi condotte è emerso che il numero minimo di barre per garantire la stabilità del fronte di scavo è in molti casi elevato e risulta impossibile da porre in opera in terreni scarsamente coesivi o in gallerie sotto elevati battenti d’acqua. Per risolvere questa situazione si può prevedere l’inserimento di drenaggi in avanzamento, con lo scopo di diminuire i gradienti idraulici nei pressi del fronte della galleria. Il modello che descrive il nuovo andamento dei carichi idraulici, considerando la presenza di dreni, è stato realizzato con il software commerciale agli elementi finiti COMSOL. Una volta determinati gli andamenti dei carichi idraulici, sono stati condotti studi parametrici sull’effetto dei dreni combinato con gli elementi di rinforzo. Dopo tali analisi sono stati ricavati nomogrammi adimensionali che tengano conto della presenza contemporanea delle barre e dei dreni. Tali diagrammi costituiscono uno strumento utile e valido per la progettazione del rinforzo del fronte di scavo. Infine sono stati realizzati confronti fra casi di studio reali e risultati ottenuti dal modello.

A Reinforcement Learning Agent for Distributed Task Allocation

Al giorno d'oggi il reinforcement learning ha dimostrato di essere davvero molto efficace nel machine learning in svariati campi, come ad esempio i giochi, il riconoscimento vocale e molti altri. Perciò, abbiamo deciso di applicare il reinforcement learning ai problemi di allocazione, in quanto sono un campo di ricerca non ancora studiato con questa tecnica e perchè questi problemi racchiudono nella loro formulazione un vasto insieme di sotto-problemi con simili caratteristiche, per cui una soluzione per uno di essi si estende ad ognuno di questi sotto-problemi. In questo progetto abbiamo realizzato un applicativo chiamato Service Broker, il quale, attraverso il reinforcement learning, apprende come distribuire l'esecuzione di tasks su dei lavoratori asincroni e distribuiti. L'analogia è quella di un cloud data center, il quale possiede delle risorse interne - possibilmente distribuite nella server farm -, riceve dei tasks dai suoi clienti e li esegue su queste risorse. L'obiettivo dell'applicativo, e quindi del data center, è quello di allocare questi tasks in maniera da minimizzare il costo di esecuzione. Inoltre, al fine di testare gli agenti del reinforcement learning sviluppati è stato creato un environment, un simulatore, che permettesse di concentrarsi nello sviluppo dei componenti necessari agli agenti, invece che doversi anche occupare di eventuali aspetti implementativi necessari in un vero data center, come ad esempio la comunicazione con i vari nodi e i tempi di latenza di quest'ultima. I risultati ottenuti hanno dunque confermato la teoria studiata, riuscendo a ottenere prestazioni migliori di alcuni dei metodi classici per il task allocation.

Evaluation of recycled carbon fibers as PLA reinforcement for additive manufacturing applications

The increased exploitation of carbon fiber reinforced polymers (CFRP) is inevitably bringing about an increase in production scraps and end-of-life components, resulting in a sharp increase in CFRP waste. Therefore, it is of paramount importance to find efficient ways to reintroduce waste into the manufacturing cycle. At present, several recycling methods for treating CFRPs are available, even if all of them still have to be optimized. The step after CFRP recycling, and also the key to build a solid and sustainable CFRP recycling market, is represented by the utilization of Re-CFs. The smartest way to utilize recovered carbon fibers is through the manufacturing of recycled CFRPs, that can be done by re-impregnating the recovered fibers with a new polymeric matrix. Fused Filament Fabrication (FFF) is one of the most widely used additive manufacturing (3D printing) techniques that fabricates parts with a polymeric filament deposition process that allows to produce parts adding material layer-by-layer, only where it is needed, saving energy, raw material cost, and waste. The filament can also contain fillers or reinforcements such as recycled short carbon fibers and this makes it perfectly compliant with the re-application of the shortened recycled CF. Therefore, in this thesis work recycled and virgin carbon fiber reinforced PLA filaments have been initially produced using 5% and 10% of CFs load. Properties and characteristics of the filaments have been determined conducting different analysis (TGA, DMA, DSC). Subsequently the 5%wt. Re-CFs filament has been used to 3D print specimens for mechanical characterization (DMA, tensile test and CTE), in order to evaluate properties of printed PLA composites containing Re-CFs and evaluate the feasibility of Re-CFs in 3D printing application.

Studio e Sperimentazione di Algoritmi di Reinforcement Learning Applicati a Video Game

Nella prima parte del mio lavoro viene presentato uno studio di una prima soluzione "from scratch" sviluppata da Andrew Karpathy. Seguono due miei miglioramenti: il primo modificando direttamente il codice della precedente soluzione e introducendo, come obbiettivo aggiuntivo per la rete nelle prime fasi di gioco, l'intercettazione della pallina da parte della racchetta, migliorando l'addestramento iniziale; il secondo é una mia personale implementazione utilizzando algoritmi più complessi, che sono allo stato dell'arte su giochi dell'Atari, e che portano un addestramento molto più veloce della rete.

Reinforcement learning over encrypted data

Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinforcement learning in particular has never been applied to such cases before. Being able to successfully make use of reinforcement learning in an encrypted scenario would allow us to create an agent that efficiently controls a system without providing it with full knowledge of the environment it is operating in, leading the way to many possible use cases. Therefore, we have decided to apply the reinforcement learning paradigm to encrypted data. In this project we have applied one of the most well-known reinforcement learning algorithms, called Deep Q-Learning, to simple simulated environments and studied how the encryption affects the training performance of the agent, in order to see if it is still able to learn how to behave even when the input data is no longer readable by humans. The results of this work highlight that the agent is still able to learn with no issues whatsoever in small state spaces with non-secure encryptions, like AES in ECB mode. For fixed environments, it is also able to reach a suboptimal solution even in the presence of secure modes, like AES in CBC mode, showing a significant improvement with respect to a random agent; however, its ability to generalize in stochastic environments or big state spaces suffers greatly.

Design and implementation of a Reinforcement Learning framework for iOS devices

Reinforcement Learning is an increasingly popular area of Artificial Intelligence. The applications of this learning paradigm are many, but its application in mobile computing is in its infancy. This study aims to provide an overview of current Reinforcement Learning applications on mobile devices, as well as to introduce a new framework for iOS devices: Swift-RL Lib. This new Swift package allows developers to easily support and integrate two of the most common RL algorithms, Q-Learning and Deep Q-Network, in a fully customizable environment. All processes are performed on the device, without any need for remote computation. The framework was tested in different settings and evaluated through several use cases. Through an in-depth performance analysis, we show that the platform provides effective and efficient support for Reinforcement Learning for mobile applications.

Structural health of a road tunnel intersecting a large and active rockslide

This work presents the case of the San Lorenzo road tunnel, a transportation infrastructure located in the northern part of Italy, involved in the so-called Passo della Morte landslide. This tunnel crosses a large rockslide characterized by slow movements. Damages like water seepage inside the tunnel and concrete lining detachments have surfaced through the years, increasing the risk. This work develops the objective of tracing back the landslide-induced stresses directly responsible for the cracks’ pattern on the most damaged segments of the tunnel. The first section of this work gives information about the global framework: site geography and its strategic relevance, geological setting, hydrological and climate conditions will be provided. The road tunnel infrastructure and its interaction with the landslide phenomena will be discussed together with the active monitoring system, which has been working for more than 20 years. In the second part the several steps and tools used to add more details about the road damages are reported. A visualization of the actual state of the most damaged portions of the road has been reached. Then the attention has been addressed to the stresses acting on the road tunnel’s aforesaid portions, developing a FEM model of a section of the tunnel through a selected software. This latter process can be deemed as a beginning for further developments. Some preliminary results are shown to demonstrate the goodness of the assumptions made. The possible future set by this work aims at constant enlargement of information to be provided to the FEM software, and at the validation of the obtained results through the monitoring data interpretative tools.

Autonomous Pricing using Policy-Gradient Reinforcement Learning

Nella letteratura economica e di teoria dei giochi vi è un dibattito aperto sulla possibilità di emergenza di comportamenti anticompetitivi da parte di algoritmi di determinazione automatica dei prezzi di mercato. L'obiettivo di questa tesi è sviluppare un modello di reinforcement learning di tipo actor-critic con entropy regularization per impostare i prezzi in un gioco dinamico di competizione oligopolistica con prezzi continui. Il modello che propongo esibisce in modo coerente comportamenti cooperativi supportati da meccanismi di punizione che scoraggiano la deviazione dall'equilibrio raggiunto a convergenza. Il comportamento di questo modello durante l'apprendimento e a convergenza avvenuta aiuta inoltre a interpretare le azioni compiute da Q-learning tabellare e altri algoritmi di prezzo in condizioni simili. I risultati sono robusti alla variazione del numero di agenti in competizione e al tipo di deviazione dall'equilibrio ottenuto a convergenza, punendo anche deviazioni a prezzi più alti.

Numerical Analysis and Redesign of a High Speed Linear Cascade Wind Tunnel for Aircraft Gas Turbine Engines Testing

Linear cascade testing serves a fundamental role in the research, development, and design of turbomachines as it is a simple yet very effective way to compute the performance of a generic blade geometry. These kinds of experiments are usually carried out in specialized wind tunnel facilities. This thesis deals with the numerical characterization and subsequent partial redesign of the S-1/C Continuous High Speed Wind Tunnel of the Von Karman Institute for Fluid Dynamics. The current facility is powered by a 13-stage axial compressor that is not powerful enough to balance the energy loss experienced when testing low turning airfoils. In order to address this issue a performance assessment of the wind tunnel was performed under several flow regimes via numerical simulations. After that, a redesign proposal aimed at reducing the pressure loss was investigated. This consists of a linear cascade of turning blades to be placed downstream of the test section and designed specifically for the type of linear cascade being tested. An automatic design procedure was created taking as input parameters those measured at the outlet of the cascade. The parametrization method employed Bézier curves to produce an airfoil geometry that could be imported into a CAD software so that a cascade could be designed. The proposal was simulated via CFD analysis and proved to be effective in reducing pressure losses up to 41%. The same tool developed in this thesis could be adopted to design similar apparatuses and could also be optimized and specialized for the design of turbomachines components.

Trajectory Planning in UAV-Aided Wireless Networks Via Reinforcement Learning

This thesis is focused on the design of a flexible, dynamic and innovative telecommunication's system for future 6G applications on vehicular communications. The system is based on the development of drones acting as mobile base stations in an urban scenario to cope with the increasing traffic demand and avoid network's congestion conditions. In particular, the exploitation of Reinforcement Learning algorithms is used to let the drone learn autonomously how to behave in a scenario full of obstacles with the goal of tracking and serve the maximum number of moving vehicles, by at the same time, minimizing the energy consumed to perform its tasks. This project is an extraordinary opportunity to open the doors to a new way of applying and develop telecommunications in an urban scenario by mixing it to the rising world of the Artificial Intelligence.

Development and Automatization of a Planar Jet Wind Tunnel for the X-wire Calibration

The scope of this study is to design an automatic control system and create an automatic x-wire calibrator for a facility named Plane Air Tunnel; whose exit creates planar jet flow. The controlling power state as well as automatic speed adjustment of the inverter has been achieved. Thus, the wind tunnel can be run with respect to any desired speed and the x-wire can automatically be calibrated at that speed. To achieve that, VI programming using the LabView environment was learned, to acquire the pressure and temperature, and to calculate the velocity based on the acquisition data thanks to a pitot-static tube. Furthermore, communication with the inverter to give the commands for power on/off and speed control was also done using the LabView VI coding environment. The connection of the computer to the inverter was achieved by the proper cabling using DAQmx Analog/Digital (A/D) input/output (I/O). Moreover, the pressure profile along the streamwise direction of the plane air tunnel was studied. Pressure tappings and a multichannel pressure scanner were used to acquire the pressure values at different locations. Thanks to that, the aerodynamic efficiency of the contraction ratio was observed, and the pressure behavior was related to the velocity at the exit section. Furthermore, the control of the speed was accomplished by implementing a closed-loop PI controller on the LabView environment with and without using a pitot-static tube thanks to the pressure behavior information. The responses of the two controllers were analyzed and commented on by giving suggestions. In addition, hot wire experiments were performed to calibrate automatically and investigate the velocity profile of a turbulent planar jet. To be able to analyze the results, the physics of turbulent planar jet flow was studied. The fundamental terms, the methods used in the derivation of the equations, velocity profile, shear stress behavior, and the effect of vorticity were reviewed.