arXiv subjects

Ricardo Shousha

Publications and source records attributed to Ricardo Shousha.

6 recordsLinked to original sources

arXiv2026

Real-time feedback control of ELM frequency using divertor gas puffing and its effects on tungsten-induced radiation and plasma performance in KSTAR

The edge-localized mode (ELM) frequency ($f_{\mathrm{ELM}}$) was successfully controlled in real time on KSTAR using a proportional-integral (PI) feedback controller, employing a $\mathrm{D}_2$ divertor gas puff as the actuator under tungsten lower-divertor conditions. The controller accurately tracked a two-step target---a 30 Hz increase in $f_{\mathrm{ELM}}$ for 4 s, followed by a 30 Hz decrease for 3 s---yielding mean and median absolute percentage errors of approximately 13% and 12%, respectively. Compared to a reference discharge, the actively controlled shot did not exhibit a significant drop in volume-integrated core radiation, confirming that excessive gas use merely degrades overall plasma performance. However, when contrasted with the exponential increase in core radiation observed in the absence of divertor gas puffing, these results underscore the critical need for real-time optimization. Specifically, divertor gas commands must be actively managed to maintain an $f_{\mathrm{ELM}}$ sufficient for flushing tungsten from the core while maximizing global plasma performance.

physics.plasm-ph↗

arXiv2026

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challenge is controlling the plasma rotation profile, which strongly influences stability, confinement, and transport. While the average rotation can be controlled, controlling the full profile is challenging due to high dimensionality, response to multiple actuators and dependence on plasma condition. Learning-based control methods, such as reinforcement learning (RL), provide a potential solution to this challenging problem with ability to model complex interactions leading to effective multi-input multi-output control. However, learning such policies is challenging due to the lack of accurate simulators that can model the rotation profile dynamics. In this work, we investigate the use of offline RL and offline model-based RL algorithms for rotation profile control, training them solely on historical data from the DIII-D tokamak. Our final method uses probabilistic models of plasma dynamics to generate rollouts for RL training. We deploy this policy on the DIII-D Tokamak and observe promising real-world results. We conclude by highlighting key challenges and insights from training and deploying an RL policy on a complex physical device while using only limited past data.

cs.LG↗

arXiv2025

Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine Tearing Mode predictor-controller. There is also discussion on guiding principles for real-time machine learning controller design and implementation.

physics.plasm-ph↗

arXiv2025

Interpreting AI for Fusion: an application to Plasma Profile Analysis for Tearing Mode Stability

AI models have demonstrated strong predictive capabilities for various tokamak instabilities--including tearing modes (TM), ELMs, and disruptive event--but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a first demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, a large density profile is lightly destabilizing, but core electron temperature and rotation peaking play the primary role in TM stability. This work offers a generalizable ML-based event prediction methodology, from training to physics-driven interpretability, bridging the gap between physics understanding and opaque ML models.

physics.plasm-ph↗

arXiv2025

TorbeamNN: Machine learning based steering of ECH mirrors on KSTAR

We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating and current drive locations in tokamak plasmas. TorbeamNN provides more than a 100 times speed-up compared to the highly optimized and simplified real-time implementation of TORBEAM without any reduction in accuracy compared to the offline, full fidelity TORBEAM code. The model was trained using KSTAR electron cyclotron heating (ECH) mirror geometries and works for both O-mode and X-mode absorption. The TorbeamNN predictions have been validated both offline and real-time in experiment. TorbeamNN has been utilized to track an ECH absorption vertical position target in dynamic KSTAR plasmas as well as under varying toroidal mirror angles and with a minimal average tracking error of 0.5cm.

physics.plasm-ph↗

arXiv2024

Highest Fusion Performance without Harmful Edge Energy Bursts in Tokamak

The path of tokamak fusion and ITER is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of high-confinement plasmas. The application of 3D magnetic perturbations is the method in ITER and possibly in future fusion power plants to suppress this instability and avoid energy busts damaging the device. Unfortunately, the conventional use of the 3D field in tokamaks typically leads to degraded fusion performance and an increased risk of other plasma instabilities, two severe issues for reactor implementation. In this work, we present an innovative 3D field optimization, exploiting machine learning, real-time adaptability, and multi-device capabilities to overcome these limitations. This integrated scheme is successfully deployed on DIII-D and KSTAR tokamaks, consistently achieving reactor-relevant core confinement and the highest fusion performance without triggering damaging instabilities or bursts while demonstrating ITER-relevant automated 3D optimization for the first time. This is enabled both by advances in the physics understanding of self-organized transport in the plasma edge and by advances in machine-learning technology, which is used to optimize the 3D field spectrum for automated management of a volatile and complex system. These findings establish real-time adaptive 3D field optimization as a crucial tool for ITER and future reactors to maximize fusion performance while simultaneously minimizing damage to machine components.

physics.plasm-ph↗