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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01rr1721061
Title: Data-Based Prediction and Analysis of the Plasma Pedestal in Tokamak Fusion Experiments
Authors: Zhao, Jinjin
Advisors: Kolemen, Egemen
Department: Computer Science
Class Year: 2019
Abstract: In 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.
URI: http://arks.princeton.edu/ark:/88435/dsp01rr1721061
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Computer Science, 1988-2019

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