As technology for manufacturing small scale prototypes of aeroplanes has become cheaper and more easily viable, the process of Rapid Prototyping has become more common. Rapid Prototyping allows for the fundamental aerodynamic qualities of a geometric body to be tested in a wind tunnel using a small scale prototype. This means smaller prototypes of aircraft can be manufactured more rapidly and at a lower cost, allowing for more extensive testing of a design’s final aerodynamic qualities before any actual full-size production. In order to gain a better insight in the behaviours of the full-sized aircraft itself, a downscaled version of the KTH project UAV ALPHA has been deigned for testing in a low speed wind tunnel. The design will be used in further testing to help confirm simulations and estimations done on the ALPHA of its aerodynamic performance.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Nine 100x100x600 mm3 plastic scintillators, formerly built for the neutron time of ight measurements at iThemba LABS, were refurbished. The position resolution of these detectors was determined using muon cosmic rays and coincident measurement techniques. Average position resolution of 4.28 cm (FWHM) was found. In order to predict the time spectrum of the large-volume detector Monte Carlo simulations have been performed. These simulations aimed at anticipating the separation of statistical neutrons, prompt gamma rays and uncorrelated gamma rays from the fast neutrons emitted in the reaction of interest. One of the neutron detectors was tested using fast neutrons from the 232Th( ,xn) reaction at 42 MeV. Statistical neutrons from fusion evaporation reactions were produced in 152Sm(12C,xn) fusion evaporation reaction. Coincidences between neutrons and gamma rays were successfully identi ed. Prompt gamma rays and uncorrelated gamma rays were also identi ed. / AFRIKAANSE OPSOMMING: Nege 100x100x600 mm3 plastiese scintillators, wat aanvanklik gebou was vir neutron vlugtyds meetings by iThemba LABS, was hernu. Die posisie resolusie van die detektore was bepaal deurmiddel van muon kosmiese straling en koïnsidensie meet tegnieke. Posisie resolusie van 4.28 cm (FWHM) was verkry. Monte Carlo simulasies is gebruik om die posisie resolusie van'n groot volume detektor te voorspel. Hierdie simulasies is daarop gemik om onderskeid te maak tussen statistiese neutrone, gelyktydige gamma strale en ongekorreleerde gamma strale vanaf vinnige neutrone in die reaksie van belang uitgestraal word. Een neutron detektor was getoets deur gebruik te maak van vinnige neutrone wat uit die reaksie 232Th( ,xn) by 42 MeV ontstaan. Statistiese neutrone vanaf splitsings verdampingsreaksies, gelyktydige gamma strale en ongekorreleerde gamma strale was geidenti seer. Statistiese neutrone van samesmelting verdamping reaksies was geproduseer in die reaksie 152Sm(12C,xn). Toeval tussen neutrone en gamma strale was suksesvol geïdenti seer, gevra gamma strale en ongekorreleerd gamma strale was ook geïdenti seer.

Accurately modeling aerodynamic forces and moments are crucial for understanding thebehavior of an aircraft when performing various maneuvers at different flight conditions.However, this task is challenging due to complex nonlinear dependencies on manydifferent parameters. Currently, Computational Fluid Dynamics (CFD), wind tunnel,and flight tests are the most common methods used to gather information about thecoefficients, which are both costly and time–consuming. Consequently, great efforts aremade to find alternative methods such as machine learning. This thesis focus on finding machine learning models that can model the static and thedynamic aerodynamics coefficients for lift, drag, and pitching moment. Seven machinelearning models for static estimation were trained on data from CFD simulations.The main focus was on dynamic aerodynamics since these are more difficult toestimate. Here two machine learning models were implemented, Long Short–TermMemory (LSTM) and Gaussian Process Regression (GPR), as well as the ordinaryleast squares. These models were trained on data generated from simulated flighttrajectories of longitudinal movements. The results of the study showed that it was possible to model the static coefficients withlimited data and still get high accuracy. There was no machine learning model thatperformed best for all three coefficients or with respect to the size of the training data.The Support vector regression was the best for the drag coefficients, while there wasno clear best model for the lift and moment. For the dynamic coefficients, the ordinaryleast squares performed better than expected and even better than LSTM and GPR forsome flight trajectories. The Gaussian process regression produced better results whenestimating a known trajectory, while the LSTM was better when predicting values ofa flight trajectory not used to train the models. / Att noggrant modellera aerodynamiska krafter och moment är avgörande för att förståett flygplans beteende när man utför olika manövrar vid olika flygförhållanden. Dennauppgift är dock utmanande på grund av ett komplext olinjärt beroende av många olikaparametrar. I nuläget är beräkningsströmningsdynamik (CFD), vindtunneltestningoch flygtestning de vanligaste metoderna för att kunna modellera de aerodynamiskakoefficienterna, men de är både kostsamma och tidskrävande. Följaktligen görs storaansträngningar för att hitta alternativa metoder, till exempel maskininlärning. Detta examensarbete fokuserar på att hitta maskininlärningmodeller som kanmodellera de statiska och de dynamiska aerodynamiska koefficienterna för lyftkraft,luftmotstånd och stigningsmoment. Sju olika maskininlärningsmodeller för destatiska koefficienterna tränades på data från CFD–simuleringar. Huvudfokus lågpå den dynamiska koefficienterna, eftersom dessa är svårare att modellera. Härimplementerades två maskininlärningsmodeller, Long Short–Term Memory (LSTM)och Gaussian Process Regression (GPR), samt minstakvadratmetoden. Dessa modellertränades på data skapad från flygbanesimuleringar av longitudinella rörelser. Resultaten av studien visade att det är möjligt att modellera de statiskakoefficienterna med begränsad data och ändå få en hög noggrannhet. Ingen avde testade maskininslärningsmodelerna var tydligt bäst för alla koefficienterna ellermed hänsyn till mängden träningsdata. Support vector regression var bäst förluftmotstånds koefficienterna, men vilken modell som var bäst för lyftkraften ochstigningsmomentet var inte lika tydligt. För de dynamiska koefficienterna presterademinstakvadratmetoden bättre än förväntat och för vissa signaler även bättre än LSTMoch GPR. GPR gav bättre resultat när man uppskattade koefficienterna för enflygbanan man tränat modellen på, medan LSTM var bättre på att förutspå värdenaför en flybana man inte hade tränat modellen på.