Search

Thesis: S.B., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2014., Cataloged from PDF version of thesis., Includes bibliographical references (pages 30-31)., Understanding the underlying dynamics of turbulence in magnetic confinement fusion experiments is extremely important. Turbulence greatly reduces the confinement time of these devices and therefore greater knowledge of turbulent dynamics can help with its mitigation. Experiments from the Alcator C-Mod tokamak [18] provide support for a theory that edge turbulence in tokamak fusion plasmas is the result of deterministic chaos, rather than stochastic processes [15]. Using readily available reflectometer data from Alcator C-Mod (C-Mod), analysis of C-Mod edge turbulence in Ohmic plasmas and Ion Cyclotron Range of Frequencies (ICRF) heated L-Mode plasmas shows that density fluctuations just inside or at the Last Closed Flux Surface (LCFS) exhibit exponential power spectra. Theoretically, the characteristic slope of the data on a semi-log plot gives the full width of the underlying Lorentzian pulses, which give rise to the exponential power spectra due to the dynamics of deterministic chaos. Using a separate fitting routine, individual Lorentzian pulses in the reflectometer time series data are identified, and the widths of the Lorentzian pulses match the inverse characteristic frequency of the exponential power spectra. Analysis of the waiting times between pulses and the pulse amplitudes indicate these are randomly distributed yet the pulse widths have a narrow distribution. These characteristics are consistent with a chaotic process. There is also a preliminary comparison of GPI data and a discussion of limitations of the analysis presented here and plans for future work. Overall, the experimental results in this study are consistent with edge turbulence that is at least partially generated by chaotic dynamics., by Victoria R. Winters., S.B.

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015., Cataloged from PDF version of thesis., Includes bibliographical references (pages 113-119)., Microturbulence is present in all magnetic confinement fusion devices, and is believed to play a major role in driving anomalous transport levels that exceed neoclassical theory predictions. In NSTX, electron thermal transport is found to dominate energy loss. Theory and experiments have shown that electron temperature gradient (ETG) turbulence on the electron gyro-scale, kḻpe

Thesis: S.B., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2014., Cataloged from PDF version of thesis., Includes bibliographical references (pages 30-31)., Understanding the underlying dynamics of turbulence in magnetic confinement fusion experiments is extremely important. Turbulence greatly reduces the confinement time of these devices and therefore greater knowledge of turbulent dynamics can help with its mitigation. Experiments from the Alcator C-Mod tokamak [18] provide support for a theory that edge turbulence in tokamak fusion plasmas is the result of deterministic chaos, rather than stochastic processes [15]. Using readily available reflectometer data from Alcator C-Mod (C-Mod), analysis of C-Mod edge turbulence in Ohmic plasmas and Ion Cyclotron Range of Frequencies (ICRF) heated L-Mode plasmas shows that density fluctuations just inside or at the Last Closed Flux Surface (LCFS) exhibit exponential power spectra. Theoretically, the characteristic slope of the data on a semi-log plot gives the full width of the underlying Lorentzian pulses, which give rise to the exponential power spectra due to the dynamics of deterministic chaos. Using a separate fitting routine, individual Lorentzian pulses in the reflectometer time series data are identified, and the widths of the Lorentzian pulses match the inverse characteristic frequency of the exponential power spectra. Analysis of the waiting times between pulses and the pulse amplitudes indicate these are randomly distributed yet the pulse widths have a narrow distribution. These characteristics are consistent with a chaotic process. There is also a preliminary comparison of GPI data and a discussion of limitations of the analysis presented here and plans for future work. Overall, the experimental results in this study are consistent with edge turbulence that is at least partially generated by chaotic dynamics., by Victoria R. Winters., S.B.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (pages 115-119)., Recent work in human and machine vision has increasingly focused on the problem of scene recognition. Scene types are largely defined by the actions one might typically do there: an office is a place someone would typically "work". I introduce the SUN Action database (short for "Scene UNderstanding - Action"): the first effort to collect and analyze free-response data from human subjects about the typical actions associated with different scene types. Responses were gathered on Mechanical Turk for twenty images per catgory, each depicting a characteristic view of one of 397 different scene types. The distribution of phrases is shown to be heavy-tailed and Zipf-like, whereas the distribution of semantic roots is not Zipf-like. Categories strongly associated with particular tasks or actions are shown to have lower overall diversity of responses. A hierarchical clustering analysis reveals a heterogeneous clustering structure, with some categories readily grouping together, and other categories remaining apart even at coarse clustering levels. Finally, two simple classifiers are introduced for predicting scene types from associated actions: a nearest centroid classifier, and an empirical maximum likelihood classifier. Both classifiers demonstrate greater than 50% classification performance in a 397-way classification task., by Catherine Anne White Olsson., M. Eng.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (pages 115-119)., Recent work in human and machine vision has increasingly focused on the problem of scene recognition. Scene types are largely defined by the actions one might typically do there: an office is a place someone would typically "work". I introduce the SUN Action database (short for "Scene UNderstanding - Action"): the first effort to collect and analyze free-response data from human subjects about the typical actions associated with different scene types. Responses were gathered on Mechanical Turk for twenty images per catgory, each depicting a characteristic view of one of 397 different scene types. The distribution of phrases is shown to be heavy-tailed and Zipf-like, whereas the distribution of semantic roots is not Zipf-like. Categories strongly associated with particular tasks or actions are shown to have lower overall diversity of responses. A hierarchical clustering analysis reveals a heterogeneous clustering structure, with some categories readily grouping together, and other categories remaining apart even at coarse clustering levels. Finally, two simple classifiers are introduced for predicting scene types from associated actions: a nearest centroid classifier, and an empirical maximum likelihood classifier. Both classifiers demonstrate greater than 50% classification performance in a 397-way classification task., by Catherine Anne White Olsson., M. Eng.