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.

A numerical study of temperature effects on hot wire measurements inside turbulent channel flows

A way to investigate turbulence is through experiments where hot wire measurements are performed. Analysis of the in turbulence of a temperature gradient on hot wire measurements is the aim of this thesis work. Actually - to author's knowledge - this investigation is the first attempt to document, understand and ultimately correct the effect of temperature gradients on turbulence statistics. However a numerical approach is used since instantaneous temperature and streamwise velocity fields are required to evaluate this effect. A channel flow simulation at Re_tau = 180 is analyzed to make a first evaluation of the amount of error introduced by temperature gradient inside the domain. Hot wire data field is obtained processing the numerical flow field through the application of a proper version of the King's law, which connect voltage, velocity and temperature. A drift in mean streamwise velocity profile and rms is observed when temperature correction is performed by means of centerline temperature. A correct mean velocity pro�le is achieved correcting temperature through its mean value at each wall normal position, but a not negligible error is still present into rms. The key point to correct properly the sensed velocity from the hot wire is the knowledge of the instantaneous temperature field. For this purpose three correction methods are proposed. At the end a numerical simulation at Re_tau =590 is also evaluated in order to confirm the results discussed earlier.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.