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Anagha Pavithran
Anagha Pavithran
Anagha Pallen Pavithran's CV
Priyanka
Aeroelastic and Dynamic Structural Analysis of a non-tapered wing
Aeroelastic and Dynamic Structural Analysis of a non-tapered wing
In this document a thorough analysis of flutter will be presented, applied on a wing box which has been discretized using a FEM tool, MSC. NASTRAN, to cover both the dynamic structural analysis as well as the aeroelastic solution to flutter. Once the structural part has been detailed, a discussion about the flutter speed obtained with the Nastran SOL 145 will be presented, followed by some other solutions.
Alvaro Moral Aranda, Agustín Pérez Arjona, Vincenzo Rosciano, Alvaro Sanjuán Florido and Alfonso Velencoso Gómez.
Aerodynamic optimization of the DONuT (dron with oriented thrust) rotor
Aerodynamic optimization of the DONuT (dron with oriented thrust) rotor
The present work analyses designs of coaxial rotor systems in an attempt to maximize its performance in hover flight. The study is carried out with the software XROTOR and CROTOR. Influence of parameters such as tip radius, revolutions per minute, lift coefficient and number of blades have been studied. The optimization process has been carried out on two possible models of the drone DONuT. Both of them are designed to allowfor torque cancellation. In order to validate the designs, the optimized models have been 3-D printed to be tested in a test bench.
Marion Marduel and Alfonso Velencoso Gomez
CV-Luana Valente
CV-Luana Valente
My CV. Created with the AltaCV template.
Valente
A Practical Introduction to Natural Language Processing
A Practical Introduction to Natural Language Processing
A two-day seminar on natural language processing applications and techniques, to undergraduates (diploma and bachelor programmes) at KDU College Penang, in March 2015.
LianTze Lim
Song Hit Prediction
Song Hit Prediction
In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model was random forest, which was able to predict Billboard song success with 88% accuracy.
Kai Middlebrook, Kian Sheik
OS Assignment
OS Assignment
Linux Distribution
Tamim