Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (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 plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

A high-density and high-confinement tokamak plasma regime for fusion energy

The tokamak approach, utilizing a toroidal magnetic field configuration to confine a hot plasma, is one of the most promising designs for developing reactors that can exploit nuclear fusion to generate electrical energy1,2. To reach the goal of an economical reactor, most tokamak reactor designs3-10 simultaneously require reaching a plasma line-averaged density above an empirical limit-the so-called Greenwald density11-and attaining an energy confinement quality better than the standard high-confinement mode12,13. However, such an operating regime has never been verified in experiments. In addition, a long-standing challenge in the high-confinement mode has been the compatibility between a high-performance core and avoiding large, transient edge perturbations that can cause very high heat loads on the plasma-facing-components in tokamaks. Here we report the demonstration of stable tokamak plasmas with a line-averaged density approximately 20% above the Greenwald density and an energy confinement quality of approximately 50% better than the standard high-confinement mode, which was realized by taking advantage of the enhanced suppression of turbulent transport granted by high density-gradients in the high-poloidal-beta scenario14,15. Furthermore, our experimental results show an integration of very low edge transient perturbations with the high normalized density and confinement core. The operating regime we report supports some critical requirements in many fusion reactor designs all over the world and opens a potential avenue to an operating point for producing economically attractive fusion energy.

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1-4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

A systematic approach to the modelling and comparison of the geometries of spherical electrodes in inertial electrostatic confinement fusion devices

Inertial electrostatic confinement fusion (IECF) devices often use two concentric spherical electrodes to converge ions in a plasma electrostatically. Using a highly transparent inner cathode, the ions can move through the cathode and collide at the center to undergo fusion reactions. This is a simple method to build a neutron source. Past research has focused chiefly on cathode "grids" manufactured by joining metal wire loops or disc-shaped elements via spot welding. There are two common geometries: "Globe" grids with a distinct latitude-longitude structure and "symmetric" grids with even-sized triangular-shaped apertures. Recent advances in additive manufacturing have opened the way to manufacturing a third class of grids in which the apertures are evenly distributed over the grid surface and have either circular or polygonal shaped apertures - here called "regular" grids. These three types are analyzed and compared based on a set of metrics, including transparency, homogeneity of aperture size, and the regularity of aperture distribution. It is shown that every type of grid comes with different advantages and disadvantages. The analysis focuses on grid geometries with 6 to 120 apertures.

Molecular dynamics simulations of displacement cascades in LiAlO2 and LiAl5O8 ceramics

Molecular dynamics was employed to investigate the radiation damage due to collision cascades in LiAlO2 and LiAl5O8, the latter being a secondary phase formed in the former during irradiation. Atomic displacement cascades were simulated by initiating primary knock-on atoms (PKA) with energy values = 5, 10 and 15 keV and the damage was quantified by the number of Frenkel pairs formed for each species: Li, Al and O. The primary challenges of modeling an ionic system with and without a core-shell model for oxygen atoms were addressed and new findings on the radiation resistance of these ceramics are presented. The working of a variable timestep function and the kinetics in the background of the simulations have been elaborated to highlight the novelty of the simulation approach. More importantly, the key results indicated that LiAlO2 experiences much more radiation damage than LiAl5O8, where the number of Li Frenkel pairs in LiAlO2 was 3-5 times higher than in LiAl5O8 while the number of Frenkel pairs for Al and O in LiAlO2 are ~ 2 times higher than in LiAl5O8. The primary reason is high displacement threshold energies (Ed) in LiAl5O8 for Li cations. The greater Ed for Li imparts higher resistance to damage during the collision cascade and thus inhibits amorphization in LiAl5O8. The presented results suggest that LiAl5O8 is likely to maintain structural integrity better than LiAlO2 in the irradiation conditions studied in this work.

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad-Shafranov equation

The force-balanced state of magnetically confined plasmas heated up to 100 million degrees Celsius must be sustained long enough to achieve a burning-plasma state, such as in the case of ITER, a fusion reactor that promises a net energy gain. This force balance between the Lorentz force and the pressure gradient force, known as a plasma equilibrium, can be theoretically portrayed together with Maxwell's equations as plasmas are collections of charged particles. Nevertheless, identifying the plasma equilibrium in real time is challenging owing to its free-boundary and ill-posed conditions, which conventionally involves iterative numerical approach with a certain degree of subjective human decisions such as including or excluding certain magnetic measurements to achieve numerical convergence on the solution as well as to avoid unphysical solutions. Here, we introduce GS-DeepNet, which learns plasma equilibria through solely unsupervised learning, without using traditional numerical algorithms. GS-DeepNet includes two neural networks and teaches itself. One neural network generates a possible candidate of an equilibrium following Maxwell's equations and is taught by the other network satisfying the force balance under the equilibrium. Measurements constrain both networks. Our GS-DeepNet achieves reliable equilibria with uncertainties in contrast with existing methods, leading to possible better control of fusion-grade plasmas.

Experimental confirmation of driving pressure boosting and smoothing for hybrid-drive inertial fusion at the 100-kJ laser facility

In laser-driven inertial confinement fusion, driving pressure boosting and smoothing are major challenges. A proposed hybrid-drive (HD) scheme can offer such ideal HD pressure performing stable implosion and nonstagnation ignition. Here we report that in the hemispherical and planar ablator targets installed in the semicylindrical hohlraum scaled down from the spherical hohlraum of the designed ignition target, under indirect-drive (ID) laser energies of ~43-50 kJ, the peak radiation temperature of 200 ± 6 eV is achieved. And using only direct-drive (DD) laser energies of 3.6-4.0 kJ at an intensity of 1.8 × 1015 W/cm2, in the hemispherical and planar targets the boosted HD pressures reach 3.8-4.0 and 3.5-3.6 times the radiation ablation pressure respectively. In all the above experiments, significant HD pressure smoothing and the important phenomenon of how a symmetric strong HD shock suppresses the asymmetric ID shock pre-compressed fuel are demonstrated. The backscattering and hot-electron energy fractions both of which are about one-third of that in the DD scheme are also measured.

Applying FAIR4RS principles to develop an integrated modeling environment for the magnetic confinement fusion

Over the decades, the integrated modeling (IM) environment for magnetically confined fusion has evolved from a single, isolated, proprietary numerical computing software to an open, flexible platform emphasizing sharing, communication, and workflow. This development direction is consistent with the FAIR4RS principles put forward by the scientific community in recent years. In this article, we describe how the FAIR4RS principles were put into practice during the development of the IM management tool FyDev for the Experimental Advanced Superconducting Tokamak (EAST). FyDev integrates the process of building, deploying, and invoking research software, automating the entire process. FyDev can also assign a unique ID for each software, convert the software ID to a Python module, and encapsulate a package management tool to enhance the software building process, ensuring consistency throughout the entire phase of the research software find, access, use, and invocation in a uniform contextual environment.

Effects of transition metal carbide dispersoids on helium bubble formation in dispersion-strengthened tungsten

The formation of helium bubbles and subsequent property degradation poses a significant challenge to tungsten as a plasma-facing material in future long-pulse plasma-burning fusion reactors. In this study, we investigated helium bubble formation in dispersion-strengthened tungsten doped with transition metal carbides, including TaC, ZrC, and TiC. Of the three dispersoids, TaC exhibited the highest resistance to helium bubble formation, possibly due to the low vacancy mobility in the Group VB metal carbide and oxide phases. Under identical irradiation conditions, large helium bubbles formed at grain boundaries in tungsten, while no bubbles were observed at the interfaces between the carbide dispersoid and tungsten matrix. Moreover, our results showed the interfaces could suppress helium bubble formation in the nearby tungsten matrix, suggesting that the interfaces are more effective in trapping helium as tiny clusters. Our research provided new insights into optimizing the microstructure of dispersion-strengthened tungsten alloys to enhance their performance.

A simplified model to estimate nonlinear turbulent transport by linear dynamics in plasma turbulence

A simplified model to estimate nonlinear turbulent transport only by linear calculations is proposed, where the turbulent heat diffusivity in tokamak ion temperature gradient(ITG) driven turbulence is reproduced for a wide parameter range including near- and far-marginal ITG stability. The optimal nonlinear functional relation(NFR) between the turbulent diffusivity, the turbulence intensity [Formula: see text], and the zonal-flow intensity [Formula: see text] is determined by means of mathematical optimization methods. Then, an extended modeling for [Formula: see text] and [Formula: see text] to incorporate the turbulence suppression effects and the temperature gradient dependence is carried out. The simplified transport model is expressed as a modified nonlinear function composed of the linear growth rate and the linear zonal-flow decay time. Good accuracy and wide applicability of the model are demonstrated, where the regression error of [Formula: see text] indicates improvement by a factor of about 1/4 in comparison to that in the previous works.

Assessment of ITER radiation environment during the remote-handling operation of In-Vessel components with D1SUNED

During ITER operational life, a remote-handled cask will be used to transfer In-Vessel components to the Hot Cell for maintenance, storage and decommissioning purposes. Due to the distribution of penetrations for system allocation in the facility, the radiation field of each transfer operation presents a high spatial variability; all operations must be studied independently for workers and electronics protection. In this paper, we present a fully representative approach to describe the radiation environment during the complete remote-handling scenario of In-Vessel components in the ITER facility. The impact of all relevant radiation sources during different stages of the operation is addressed. As-built structures and 2020 baseline designs are considered to produce the most detailed neutronics model of the Tokamak Complex, the 400,000-tonne civil structure hosting the tokamak, up to date. Novel capabilities of the D1SUNED code have allowed to compute the integral dose, the dose rate and the photon-induced neutron flux of both moving and static radiation sources. Time bins are included in the simulations to compute the dose rate caused by In-Vessel components at all positions along the transfer. The time evolution of the dose rate is built in video format with a 1-m resolution, especially valuable for hot-spots identification.

First measurements of p(11)B fusion in a magnetically confined plasma

Proton-boron (p¹¹B) fusion is an attractive potential energy source but technically challenging to implement. Developing techniques to realize its potential requires first developing the experimental capability to produce p¹¹B fusion in the magnetically-confined, thermonuclear plasma environment. Here we report clear experimental measurements supported by simulation of p¹¹B fusion with high-energy neutral beams and boron powder injection in a high-temperature fusion plasma (the Large Helical Device) that have resulted in diagnostically significant levels of alpha particle emission. The injection of boron powder into the plasma edge results in boron accumulation in the core. Three 2 MW, 160 kV hydrogen neutral beam injectors create a large population of well-confined, high -energy protons to react with the boron plasma. The fusion products, MeV alpha particles, are measured with a custom designed particle detector which gives a fusion rate in very good relative agreement with calculations of the global rate. This is the first such realization of p¹¹B fusion in a magnetically confined plasma.

Investigation of interfacial strength in nacre-mimicking tungsten heavy alloys for nuclear fusion applications

Tungsten heavy alloys have been proposed as plasma facing material components in nuclear fusion reactors and require experimental investigation in their confirmation. For this purpose, a 90W-7Ni-3Fe alloy has been selected and microstructurally manipulated to present a multiphase brick-and-mortar structure of W-phase 'bricks' surrounded by a ductile 'mortar'. This work draws inspiration from nature to artificially imitate the extraordinary combination of strength and stiffness exhibited by mollusks and produce a nacre-mimicking metal matrix composite capable of withstanding the extremely hostile environment of the reactor interior and maintaining structural integrity. The underlying mechanisms behind this integrity have been probed through high-resolution structural and chemical characterization techniques and have revealed chemically diffuse phase boundaries exhibiting unexpected lattice coherency. These features have been attributed to an increase in the energy required for interfacial decohesion in these systems and the simultaneous expression of high strength and toughness in tungsten heavy alloys.

An open time-series simulated dataset covering various accidents for nuclear power plants

Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.

Discovery, nuclear properties, synthesis and applications of technetium-101

Technetium-101 (¹⁰¹Tc) has been poorly studied in comparison with other Tc isotopes, although it was first identified over ~80 years ago shortly after the discovery of the element Tc itself. Its workable half-life and array of production modes, i.e., light/heavy particle reactions, fission, fusion-evaporation, etc., allow it to be produced and isolated using an equally diverse selection of chemical separation pathways. The inherent nuclear properties of ¹⁰¹Tc make it important for research and applications related to radioanalytical tracer studies, as a fission signature, fusion materials, fission reactor fuels, and potentially as a radioisotope for nuclear medicine. In this review, an aggregation of the known literature concerning the chemical, nuclear, and physical properties of ¹⁰¹Tc and some its applications are presented. This work aims at providing an up-to-date and first-of-its-kind overview of ¹⁰¹Tc that could be of importance for further development of the fundamental and applied nuclear and radiochemistry of ¹⁰¹Tc.

Computational methods to simulate molten salt thermophysical properties

Molten salts are important thermal conductors used in molten salt reactors and solar applications. To use molten salts safely, accurate knowledge of their thermophysical properties is necessary. However, it is experimentally challenging to measure these properties and a comprehensive evaluation of the full chemical space is unfeasible. Computational methods provide an alternative route to access these properties. Here, we summarize the developments in methods over the last 70 years and cluster them into three relevant eras. We review the main advances and limitations of each era and conclude with an optimistic perspective for the next decade, which will likely be dominated by emerging machine learning techniques. This article is aimed to help researchers in peripheral scientific domains understand the current challenges of molten salt simulation and identify opportunities to contribute.

Integration of full divertor detachment with improved core confinement for tokamak fusion plasmas

Divertor detachment offers a promising solution to the challenge of plasma-wall interactions for steady-state operation of fusion reactors. Here, we demonstrate the excellent compatibility of actively controlled full divertor detachment with a high-performance (βN ~ 3, H98 ~ 1.5) core plasma, using high-βp (poloidal beta, βp > 2) scenario characterized by a sustained core internal transport barrier (ITB) and a modest edge transport barrier (ETB) in DIII-D tokamak. The high-βp high-confinement scenario facilitates divertor detachment which, in turn, promotes the development of an even stronger ITB at large radius with a weaker ETB. This self-organized synergy between ITB and ETB, leads to a net gain in energy confinement, in contrast to the net confinement loss caused by divertor detachment in standard H-modes. These results show the potential of integrating excellent core plasma performance with an efficient divertor solution, an essential step towards steady-state operation of reactor-grade plasmas.

Predicting structural material degradation in advanced nuclear reactors with ion irradiation

Swelling associated with the formation and growth of cavities is among the most damaging of radiation-induced degradation modes for structural materials in advanced nuclear reactor concepts. Ion irradiation has emerged as the only practical option to rapidly assess swelling in candidate materials. For decades, researchers have tried to simulate the harsh environment in a nuclear reactor in the laboratory at an accelerated rate. Here we present the first case in which swelling in a candidate alloy irradiated ~ 2 years in a nuclear reactor was replicated using dual ion irradiation in ~ 1 day with precise control over damage rate, helium injection rate, and temperature and utilize physical models to predict the effects of radiation in reactors. The capability to predict and replicate the complex processes surrounding cavity nucleation and growth across many decades of radiation dose rate highlights the potential of accelerated radiation damage experiments. More importantly, it demonstrates the capability to predict the swelling evolution and the possibility to predict other features of the irradiated microstructure evolution that control material property degradation required to accelerate the development of new, radiation-tolerant materials.

The effect of different fuels and clads on neutronic calculations in a boiling water reactor using the Monte Carlo method

In this study, a Boiling Water Reactor (BWR) modeling was done for the reactor core divided into square lattice 8 × 8 type using the Monte Carlo Method. Each of the square lattices in the reactor core was divided into small square lattices 7 × 7 type in groups of four. In the BWR designed in this study, modeling was made on fuel assemblies at pin-by-pin level by using neptunium mixed fuels as fuel rod, Zr-2 and SiC as fuel cladding, H2O as coolant. In fuel rods were used NpO2 and NpF4 fuels at the rate of 0.2%-1% as neptunium mixed fuels. In this study, the effect on the neutronic calculations as keff, neutron flux, fission energy, heating of NpO2 and NpF4 fuels in 0.2%-1% rates, and Zr-2 and SiC clads were investigated in the designed BWR system. The three-dimensional (3-D) modelling of the reactor core and fuel assembly into the designed BWR system was performed by using MCNPX-2.7.0 Monte Carlo method and the ENDF/B-VII.0 nuclear data library.

Observation of persistent species temperature separation in inertial confinement fusion mixtures

The injection and mixing of contaminant mass into the fuel in inertial confinement fusion (ICF) implosions is a primary factor preventing ignition. ICF experiments have recently achieved an alpha-heating regime, in which fusion self-heating is the dominant source of yield, by reducing the susceptibility of implosions to instabilities that inject this mass. We report the results of unique separated reactants implosion experiments studying pre-mixed contaminant as well as detailed high-resolution three-dimensional simulations that are in good agreement with experiments. At conditions relevant to mixing regions in high-yield implosions, we observe persistent chunks of contaminant that do not achieve thermal equilibrium with the fuel throughout the burn phase. The assumption of thermal equilibrium is made in nearly all computational ICF modeling and methods used to infer levels of contaminant from experiments. We estimate that these methods may underestimate the amount of contaminant by a factor of two or more.

Employing antineutrino detectors to safeguard future nuclear reactors from diversions

The Non-Proliferation Treaty and other non-proliferation agreements are in place worldwide to ensure that nuclear material and facilities are used only for peaceful purposes. Antineutrino detectors, sensitive to reactor power and fuel changes, can complement the tools already at the disposal of international agencies to safeguard nuclear facilities and to verify the States' compliance with the agreements. Recent advancement in these detectors has made it possible to leverage them to reduce the likelihood of an undetected diversion of irradiated nuclear material. Here we show the sensitivity of antineutrino monitors to fuel divergence from two reactor types: a traditional light-water reactor and an advanced sodium-cooled reactor design, a likely candidate for future deployment. The analysis demonstrates that a variety of potential diversion scenarios can be detected by such a system. We outline recent developments in monitoring capabilities and discuss their potential security implications to the international community.