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Design of magnetic mirror machines for controlled fusion research apparatus, and optimization of magnetic field coils by adiabatic confinement of largest number of particles for given cost
The Plasma Surface Interactions in Controlled Fusion Devices (PSI) conference reached an important milestone in 2024 with its 50th anniversary. It was celebrated at its venue in Marseille by a special round table discussion gathering 6 former chairmen of its Programme Committees, who gave some highlights presented at the conference during the five decades. The article provides a summary of this overview.
Controlled thermonuclear fusion technology including production, heating and containment of extreme temperature plasmas
Development of methods for generating high laser pulse energy has stimulated research leading to new ideas for practical controlled thermonuclear fusion machines. A review is presented of some important efforts in progress, and two different approaches have been selected as examples for discussion. One involves the concept of very short pulse lasers with power output tailored, in time, to obtain a nearly isentropic compression of a deuterium-tritium pellet to very high densities and temperatures. A second approach utilizing long wavelength, long pulse, efficient gas lasers to heat a column of plasma contained in a solenoidal field is also discussed. The working requirements of the laser and various magnetic field geometries of this approach are described.
Nuclear fusion power delivered by magnetic-confinement tokamak reactors holds the promise of sustainable and clean energy1. The avoidance of large-scale plasma instabilities called disruptions within these reactors is one of the most pressing challenges, because disruptions can halt power production and damage key components. Disruptions are particularly harmful for large burning-plasma systems such as the multibillion-dollar International Thermonuclear Experimental Reactor (ITER) project currently under construction, which aims to be the first reactor that produces more power from fusion than is injected to heat the plasma. Here we present a method based on deep learning for forecasting disruptions. Our method extends considerably the capabilities of previous strategies such as first-principles-based and classical machine-learning approaches. In particular, it delivers reliable predictions for machines other than the one on which it was trained—a crucial requirement for future large reactors that cannot afford training disruptions. Our approach takes advantage of high-dimensional training data to boost predictive performance while also engaging supercomputing resources at the largest scale to improve accuracy and speed. Trained on experimental data from the largest tokamaks in the United States (DIII-D) and the world (Joint European Torus, JET), our method can also be applied to specific tasks such as prediction with long warning times: this opens up the possibility of moving from passive disruption prediction to active reactor control and optimization. These initial results illustrate the potential for deep learning to accelerate progress in fusion-energy science and, more generally, in the understanding and prediction of complex physical systems.
Significance In plants, the retromer complex (VPS35, VPS29, VPS26) has been implicated in retrograde traffic from the endosomes back to the trans -Golgi network. Nevertheless, vps35 mutants exhibit impaired anterograde traffic toward the vacuole (synthetic, endocytic, and autophagic). This paradox remains an open question. We show that the small molecule endosidin17 (ES17) targets VPS35, which disrupts its association with RABG3f. This, in turn, impairs retromer complex membrane anchoring, producing vps35 mutant-like phenotypes. Using ES17, we found that VPS35–RABG3f binding precedes the release of RABG3f–GTP to interact with the vacuolar homotypic fusion and vacuole protein sorting (HOPS) complex, which is required for fusion of late compartments with the tonoplast. Thus, VPS35–RABG3f interaction constitutes a checkpoint before proceeding with membrane fusion.
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Liquid metal corrosion of solid metals - diffusion effects
Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.
Plasma magnetohydrodynamic flow, waves, and oscillations, and applied plasma physics to controlled nuclear fusion
Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.
In (Imada et al 2019 Nucl. Fusion 59 046016 and references therein) a new 4D drift kinetic nonlinear theory, valid in the limit of a low beta, small inverse aspect ratio, circular cross section, toroidal geometry, to describe the plasma response to the neoclassical tearing mode (NTM) magnetic perturbation is derived. In (Dudkovskaia et al 2021 Plasma Phys. Control. Fusion 63 054001) this theory is reduced in a low collisionality limit, which allows a dimensionality reduction to a 3D problem to efficiently resolve the collisional dissipation layer in the vicinity of the trapped-passing boundary. (Dudkovskaia et al 2021 Plasma Phys. Control. Fusion 63 054001) adopts an improved model for the magnetic drift frequency, which reduces the threshold magnetic island half-width from $8.73\rho_{b i}$, where $\rho_{b i}$ is the trapped ion banana orbit width, to $1.46 \rho_{b i}$, making it in closer agreement with experimental observations for the large aspect ratio tokamak equilibrium. In the present paper, the theory is extended to a high beta, arbitrary tokamak geometry to capture the plasma shaping effects on the NTM threshold physics with the focus on the non-zero triangularity discharges that are known to have a strong impact on the plasma MHD stability. First, it is found that the higher triangularity plasma is more prone to NTMs which is in agreement with the 2/1 tearing mode onset relative frequency measurements in DIII-D. Second, the NTM threshold dependence on the tokamak inverse aspect ratio obtained in (Dudkovskaia et al 2021 Plasma Phys. Control. Fusion 63 054001) is refined and extended to a finite aspect ratio limit. Third, the NTM threshold dependence on poloidal beta is obtained and benchmarked against the EAST threshold island width measurements.
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As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.
Energetic-particle-induced geodesic acoustic modes, EGAMs (Fu, Phys. Rev. Let., vol. 101, 2008, pp. 185002), driven by neutral beam injection (NBI), have been observed in many DIII-D tokamak experiments (Nazikian et al., Phys. Rev. Lett., vol. 101, 2008, pp. 185001). This mechanism has been theoretically investigated in (Qiu et al., Plasma Phys. Control. Fusion, vol. 52, 2010, pp. 095003), using a sharp energetic particle distribution function, and in (Qu et al., Plasma Phys. Control. Fusion, vol. 59, 2017, pp. 055018), where the dispersion relation and eigenmode behaviour were obtained for the situation of early beam scenario, that is, for times smaller than the beam slowing down time. In this work, we extend these studies determining the eigenmode for beyond the slowing down time, in a scenario with reverse safety factor q profile, where a small concentration of energetic ions can produce an off-axis maximum in the GAM dispersion relation. Here, the characteristics of EGAM are analytically studied with the drift kinetic equation together with the MHD code NOVA. The toroidal energetic ion transit frequency, coupled with the GAM frequency, produces the maximum in the dispersion relation where the eigenmode can be found. The quantitative correspondence of experimental results with the predictions of the proposed model is analysed.
Abstract Triplet-fusion-based photon upconversion holds promise for a wide range of applications, from photovoltaics to bioimaging. The efficiency of triplet fusion, however, is fundamentally limited in conventional molecular and polymeric systems by its spin dependence. Here, we show that the inherent tailorability of metal–organic frameworks (MOFs), combined with their highly porous but ordered structure, minimizes intertriplet exchange coupling and engineers effective spin mixing between singlet and quintet triplet–triplet pair states. We demonstrate singlet–quintet coupling in a pyrene-based MOF, NU-1000. An anomalous magnetic field effect is observed from NU-1000 corresponding to an induced resonance between singlet and quintet states that yields an increased fusion rate at room temperature under a relatively low applied magnetic field of 0.14 T. Our results suggest that MOFs offer particular promise for engineering the spin dynamics of multiexcitonic processes and improving their upconversion performance.
Studies and experiments to determine time dependency of plasma density, high frequency electron-phonon interactions in magnetic fields, and operation of stuffed-cusp plasma facility