Confinement mode identification of fusion plasmas utilizing machine learning methods *

Distinguishing features between fusion plasma confinement regimes are explored via machine learning methods to analyze experimental data from the compact, high-field Alcator C-Mod tokamak. Supervised learning techniques with zero-dimensional data and time-independent quantities are employed which increases the generalizability of this approach for instant confinement mode identification and ultimately real-time prediction purposes for fusion devices. Binary classification of L- and H-modes utilizing Gaussian naïve Bayes, logistic regression, multilayer perceptron (i.e. feedforward neural networks), and random forests performed similarly and obtained an average accuracy of 97.2% for L-modes and 86.7% for H-modes using the plasma’s stored energy, volume-averaged density, poloidal beta, ohmic heating power, normalized internal inductance, magnetic axis radial position, and Hα as inputs. Additionally this work investigates I-modes leading to a multi-class classification problem. Development of a new confinement database with over 200 distinct shots consisting of approximately 400 L-, 200 H-, and 100 I-mode periods extends previous databases for large-scale comparative studies.