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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01rr1721061
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dc.contributor.advisorKolemen, Egemen-
dc.contributor.authorZhao, Jinjin-
dc.date.accessioned2019-09-04T17:52:32Z-
dc.date.available2019-09-04T17:52:32Z-
dc.date.created2019-05-06-
dc.date.issued2019-09-04-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01rr1721061-
dc.description.abstractIn tokamak fusion reactors, the pedestal is a steep pressure drop at the plasma edge in high confinement mode (H-mode). It is an important factor in both generating fusion power and machine wall deterioration, and understanding and predicting pedestal behavior can enable future fusion performance optimization. In this work, we present data-driven analysis on and prediction for the pedestal layer from experimental data. We introduce three different novel datasets, a neural network model (mNN) for calculating 5 key properties of the pedestal from basic machine parameter inputs, and an algorithm for data-cleaning based on the K-NN model.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titleData-Based Prediction and Analysis of the Plasma Pedestal in Tokamak Fusion Experimentsen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2019en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid961168218-
Appears in Collections:Computer Science, 1988-2022

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