Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented, which predicts the pedestal heights and widths of electron density (
$n_{\mathrm {e}}$
) and electron temperature (
$T_{\mathrm {e}}$
) profiles as well as the separatrix
$n_{\mathrm {e}}$
from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The one modeling framework for integrated tasks (OMFIT) framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train ML models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal component analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (
$I_{\mathrm {p}}$
), toroidal magnetic field (
$B_{\phi }$
), neutral beam heating power (
$P_{{\mathrm {NBI}}}$
), and shaping quantities. The deep jointly informed neural networks (DJINNs) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple linear regression (LR) analysis, indicating nonlinear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.