Latent Space Mapping: Revolutionizing Predictive Models for Divertor Plasma Detachment Control

TL;DR

This paper presents DivControlNN, a machine learning surrogate model that enables ultra-fast, accurate predictions of divertor plasma behavior, significantly improving real-time control capabilities in fusion reactors.

Contribution

Introduces DivControlNN, a novel latent space mapping-based surrogate model that achieves real-time plasma predictions with high accuracy, trained on extensive simulation data.

Findings

Achieves over 10^8 speed-up compared to traditional simulations.

Maintains below 20% relative error in key plasma properties.

Successfully demonstrated detachment control in KSTAR experiments.

Abstract

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster, but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., $\sim0.2$ ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over $10^8$ compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.