Kolemen, EgemenZhao, Jinjin2019-09-042026-09-292019-09-042026-09-292019-05-062019-09-04http://arks.princeton.edu/ark:/88435/dsp01rr1721061https://theses-dissertations.princeton.edu/handle/88435/dsp01rr1721061In 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.application/pdfenPrinceton University Senior Theses