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Wonders of nuclear fusion : creating an ultimate energy source
Singer introduces young readers to what fusion is and isn't. He explains the ways scientists have approached and developed fusion and discusses its advantages over other forms of energy production.
Book
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 energy
1
,
2
. To reach the goal of an economical reactor, most tokamak reactor designs
3
–
10
simultaneously require reaching a plasma line-averaged density above an empirical limit—the so-called Greenwald density
11
—and attaining an energy confinement quality better than the standard high-confinement mode
12
,
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 scenario
14
,
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.
A stable tokamak plasma has been demonstrated with a high plasma density and a high energy confinement quality, both of which are simultaneously important for fusion reactors.
Journal Article
A sustained high-temperature fusion plasma regime facilitated by fast ions
Nuclear fusion is one of the most attractive alternatives to carbon-dependent energy sources
1
. Harnessing energy from nuclear fusion in a large reactor scale, however, still presents many scientific challenges despite the many years of research and steady advances in magnetic confinement approaches. State-of-the-art magnetic fusion devices cannot yet achieve a sustainable fusion performance, which requires a high temperature above 100 million kelvin and sufficient control of instabilities to ensure steady-state operation on the order of tens of seconds
2
,
3
. Here we report experiments at the Korea Superconducting Tokamak Advanced Research
4
device producing a plasma fusion regime that satisfies most of the above requirements: thanks to abundant fast ions stabilizing the core plasma turbulence, we generate plasmas at a temperature of 100 million kelvin lasting up to 20 seconds without plasma edge instabilities or impurity accumulation. A low plasma density combined with a moderate input power for operation is key to establishing this regime by preserving a high fraction of fast ions. This regime is rarely subject to disruption and can be sustained reliably even without a sophisticated control, and thus represents a promising path towards commercial fusion reactors.
A magnetic confinement regime established at the Korea Superconducting Tokamak Advanced Research device enables the generation of plasmas over 10
8
kelvin for 20 seconds with the aid of fast ions without plasma edge instabilities or impurity accumulation.
Journal Article
Disruption prediction with artificial intelligence techniques in tokamak plasmas
In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.
Tokamak plasmas are prone to sudden collapses that terminate the nuclear fusion reactions. This perspective discusses the prediction of these so-called disruptions with artificial intelligence techniques.
Journal Article
High critical current density and high-tolerance superconductivity in high-entropy alloy thin films
High-entropy alloy (HEA) superconductors—a new class of functional materials—can be utilized stably under extreme conditions, such as in space environments, owing to their high mechanical hardness and excellent irradiation tolerance. However, the feasibility of practical applications of HEA superconductors has not yet been demonstrated because the critical current density (
J
c
) for HEA superconductors has not yet been adequately characterized. Here, we report the fabrication of high-quality superconducting (SC) thin films of Ta–Nb–Hf–Zr–Ti HEAs via a pulsed laser deposition. The thin films exhibit a large
J
c
of >1 MA cm
−2
at 4.2 K and are therefore favorable for SC devices as well as large-scale applications. In addition, they show extremely robust superconductivity to irradiation-induced disorder controlled by the dose of Kr-ion irradiation. The superconductivity of the HEA films is more than 1000 times more resistant to displacement damage than that of other promising superconductors with technological applications, such as MgB
2
, Nb
3
Sn, Fe-based superconductors, and high-
T
c
cuprate superconductors. These results demonstrate that HEA superconductors have considerable potential for use under extreme conditions, such as in aerospace applications, nuclear fusion reactors, and high-field SC magnets.
Thin-film high-entropy alloy (HEA) superconductors have recently attracted a lot of attention, but their critical current density and potential usefulness in engineering applications has remained unclear. Here, the authors fabricate HEA films with remarkably high critical current density and resistance to radiation damage.
Journal Article
The data-driven future of high-energy-density physics
High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.
This Perspective discusses how high-energy-density physics could tap the potential of AI-inspired algorithms for extracting relevant information and how data-driven automatic control routines may be used for optimizing high-repetition-rate experiments.
Journal Article
Observation of a new type of self-generated current in magnetized plasmas
A tokamak, a torus-shaped nuclear fusion device, needs an electric current in the plasma to produce magnetic field in the poloidal direction for confining fusion plasmas. Plasma current is conventionally generated by electromagnetic induction. However, for a steady-state fusion reactor, minimizing the inductive current is essential to extend the tokamak operating duration. Several non-inductive current drive schemes have been developed for steady-state operations such as radio-frequency waves and neutral beams. However, commercial reactors require minimal use of these external sources to maximize the fusion gain, Q, the ratio of the fusion power to the external power. Apart from these external current drives, a self-generated current, so-called bootstrap current, was predicted theoretically and demonstrated experimentally. Here, we reveal another self-generated current that can exist in a tokamak and this has not yet been discussed by present theories. We report conclusive experimental evidence of this self-generated current observed in the KSTAR tokamak.
Fusion devices like tokamaks require plasma current to generate magnetic field for plasma confinement. Here the authors report an observation of a self-generated anomalous current that contributes up to 30% of the total current in the fusion plasma at KSTAR.
Journal Article
Review on Preparation Technology and Properties of Refractory High Entropy Alloys
Refractory high entropy alloys have broad application prospects due to their excellent comprehensive properties in high temperature environments, and they have been widely implemented in many complex working conditions. According to the latest research reports, the preparation technology of bulk and coating refractory high entropy alloys are summarized, and the advantages and disadvantages of each preparation technology are analyzed. In addition, the properties of refractory high entropy alloys, such as mechanical properties, wear resistance, corrosion resistance, oxidation resistance, and radiation resistance are reviewed. The existing scientific problems of refractory high entropy alloys, at present, are put forward, which provide reference for the development and application of refractory high entropy alloys in the future, especially for plasma-facing materials in nuclear fusion reactors.
Journal Article
Magnetically Confined Fusion Plasma Physics, Volume 3
This book describes advanced kinetic theory and applications for magnetically confined fusion plasmas. It fills the gap between plasma physics textbooks and up-to-date research developments in this field.
eBook
Disruption prediction for future tokamaks using parameter-based transfer learning
Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to predict incoming disruptions. However, future reactors, with much higher stored energy, cannot provide enough unmitigated disruption data at high performance to train the predictor before damaging themselves. Here we apply a deep parameter-based transfer learning method in disruption prediction. We train a model on the J-TEXT tokamak and transfer it, with only 20 discharges, to EAST, which has a large difference in size, operation regime, and configuration with respect to J-TEXT. Results demonstrate that the transfer learning method reaches a similar performance to the model trained directly with EAST using about 1900 discharge. Our results suggest that the proposed method can tackle the challenge in predicting disruptions for future tokamaks like ITER with knowledge learned from existing tokamaks.
Tokamak devices currently hold the technological advance to be considered the most promising approach to fusion energy, but disruption events prediction is a major challenge for larger devices. The authors show that a machine learning model trained on a smaller tokamak holds promises for transfer of knowledge of disrupting violent events prediction to another, larger tokamak.
Journal Article