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Streamwise Flow-Induced Oscillations Of Bluff Bodies - The Influence Of Symmetry Breaking, Tyler Gurian
Streamwise Flow-Induced Oscillations Of Bluff Bodies - The Influence Of Symmetry Breaking, Tyler Gurian
Masters Theses
The influence of symmetry breaking on the flow induced oscillations of bluff bodies in the steamwise direction is studied. First, a series of experiments is conducted on a one-degree-of-freedom circular cylinder allowed to exhibit pure translational motion in the streamwise direction over a range of reduced velocities, 1.4 < U* < 4.4, corresponding to a Reynolds number range of 970 < Re < 3370. Two distinct regions of displacements were observed in reduced velocity ranges of 1.6 < U* < 2.5 and 2.75 < U* < 3.85. Measured force coefficients in the drag and lift direction were examined, along with the wake visualization, through the range of reduced velocities, to infer the resulting wake modes. A new Alternating Symmetric (AS) mode was found. This transition from symmetric to AS shedding occurred near the end of the first region of response. Similar tests were run with a square prism in the parameter space of 2.4 < U* < 5.8 and 757 < Re < 1900 over angles of incidence of 0° ≤ α ≤ 45°. A distinct region of lock-in is observed for α = 0°, 2.5°, 5°, 7.5° over 3.2 < U* < 5.4 for α = 0°, and decreasing with increasing α. The wake structures were found to be roughly symmetric for α = 0°, but transitioned towards asymmetry …
Classification Of Eeg Signals Of User States In Gaming Using Machine Learning, Chandana Mallapragada
Classification Of Eeg Signals Of User States In Gaming Using Machine Learning, Chandana Mallapragada
Masters Theses
"In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and …