Achievement of Reactor-Relevant Performance in Negative Triangularity Shape in the DIII-D Tokamak

Deceleration of fast ion rarefied beam due to Cherenkov interaction with ion-acoustic waves

The deceleration of a low-density beam of ions in plasma with developed ion-acoustic turbulence arising in strong electric field is described. The time and length of beam deceleration along and across the anisotropy axis of the wave number distribution of ion-acoustic waves are found. It is shown to what extent an increase in the strength of the electric field that generates turbulence is accompanied by a decrease in the time and length of braking. As the beam propagates along the anisotropy axis, its velocity decreases to approximately the velocity of ion sound, and the direction of propagation does not change. When the beam is decelerated with an initial velocity across the anisotropy axis, a velocity component appears along the anisotropy axis during deceleration, which results in the beam deflection from the initial direction. In this case, the modulus of the beam velocity at the end of deceleration is close to the ion sound velocity.

Tailoring tokamak error fields to control plasma instabilities and transport

A tokamak relies on the axisymmetric magnetic fields to confine fusion plasmas and aims to deliver sustainable and clean energy. However, misalignments arise inevitably in the tokamak construction, leading to small asymmetries in the magnetic field known as error fields (EFs). The EFs have been a major concern in the tokamak approaches because small EFs, even less than 0.1%, can drive a plasma disruption. Meanwhile, the EFs in the tokamak can be favorably used for controlling plasma instabilities, such as edge-localized modes (ELMs). Here we show an optimization that tailors the EFs to maintain an edge 3D response for ELM control with a minimized core 3D response to avoid plasma disruption and unnecessary confinement degradation. We design and demonstrate such an edge-localized 3D response in the KSTAR facility, benefiting from its unique flexibility to change many degrees of freedom in the 3D coil space for the various fusion plasma regimes. This favorable control of the tokamak EF represents a notable advance for designing intrinsically 3D tokamaks to optimize stability and confinement for next-step fusion reactors.

Robust Avoidance of Edge-Localized Modes alongside Gradient Formation in the Negative Triangularity Tokamak Edge

In a series of high performance diverted discharges on DIII-D, we demonstrate that strong negative triangularity (NT) shaping robustly suppresses all edge-localized mode (ELM) activity over a wide range of plasma conditions: ⟨n⟩=0.1-1.5×10^{20}  m^{-3}, P_{aux}=0-15  MW, and |B_{t}|=1-2.2  T, corresponding to P_{loss}/P_{LH08}∼8. The full dataset is consistent with the theoretical prediction that magnetic shear in the NT edge inhibits access to ELMing H-mode regimes; all experimental pressure profiles are found to be at or below the infinite-n ballooning stability limit. Our present dataset also features edge pressure gradients in strong NT that are closer to an H-mode than a typical L-mode plasma, supporting the consideration of NT for reactor design.

Plasma Scenarios for the DTT Tokamak with Optimized Poloidal Field Coil Current Waveforms

In the field of nuclear fusion, the power exhaust problem is still an open issue and represents one of the biggest problems for the realization of a commercial fusion power plant. According to the “European Fusion Roadmap”, a dedicated facility able to investigate possible solutions to heat exhaust is mandatory. For this purpose, the mission of the Divertor Tokamak Test (DTT) tokamak is the study of different solutions for the divertor. This paper presents the plasma scenarios for standard and alternative configurations in DTT. The Single Null scenario is described in detail. The alternative configurations are also presented, showing the good flexibility of the machine.

Magnetic control of tokamak plasmas through deep reinforcement learning

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable^1,2, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied.

A newly designed control architecture uses deep reinforcement learning to learn to command the coils of a tokamak, and successfully stabilizes a wide variety of fusion plasma configurations.