Data-driven profile prediction for DIII-D
Joseph Abbate, Rory Conlin, Egemen Kolemen
Abstract
A new, fully data-driven algorithm has been developed that uses a neural network to predict plasma profiles on a scale of τ E into the future given an actuator trajectory and the plasma state history. The model was trained and tested on DIII-D data from the 2013–2018 experimental campaigns. The model runs in tens of milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It is also fast enough to be used for real-time model-predictive control.
Topics & Concepts
DIII-DNuclear engineeringNuclear physicsMaterials sciencePlasmaPhysicsTokamakEngineeringAdvanced Data Storage TechnologiesAdvancements in Semiconductor Devices and Circuit DesignSemiconductor materials and devices