This study investigates cross-field particle transport and intermittent fluctuations in the boundary region of magnetically confined plasmas using numerical turbulence simulations based on a reduced two-field fluid model. The model captures turbulent dynamics and blob-like structures in the drift plane perpendicular to the magnetic field, while parametrizing parallel losses in the scrape-off layer through sheath interactions at material surfaces. A systematic analysis of fluctuation statistics is performed by varying the parallel loss rate parameter. Results show that increasing parallel losses steepens the radial particle density profile, enhances the linear growth rate, reduces the dominant fluctuation length scale, increases the relative fluctuation level, and amplifies the intermittency of fluctuations. Despite these changes, the statistical properties of the fluctuations remain consistent across all parallel loss rates. Large-amplitude bursts are well described by a two-sided exponential pulse function, with peak amplitudes and waiting times following exponential distributions, fluctuations obeying Gamma distributions, and frequency spectra exhibiting a Lorentzian shape. These features are accurately captured by a stochastic model based on the superposition of uncorrelated pulses. The methodology presented here offers a robust framework for verifying fluctuation statistics in more advanced boundary turbulence simulation codes.

The magnetic field configuration of the CN-H1 stellarator exhibits non-axisymmetric characteristics, which significantly affect the propagation and absorption of electromagnetic waves. Therefore, the wave launch position plays a crucial role in determining the effect of ion cyclotron resonance heating (ICRH). This paper utilizes the three-dimensional full-wave code to simulate ICRH with different wave launch positions on CN-H1. Employing the 4He (H) minority ion heating scheme, it explores the impacts of the wave launch position [poloidal position, toroidal position as well as low-field side (LFS) and high-field side (HFS)] on ICRH. Our study suggests that the wave launch position in the toroidal direction should aim to maximize the length of the ion cyclotron resonance layer (ICRL) at the magnetic axis directly opposite the ion cyclotron range of frequency antenna. In the poloidal direction, the wave launch position should be placed as close as possible to the ICRL at the target heating position. Ion absorption in the target region requires the fulfillment of multiple conditions, and electron absorption exhibited favorable absorption characteristics across several simulated wave launch positions, particularly at the toroidal 0° position, where excellent core electron absorption was observed. Due to reduced collisional damping, better wave penetration, and more favorable wave accessibility on the LFS under current device conditions, the heating effect on the LFS is superior to that on the HFS.

Effects of impurities on zonal flows (ZFs) driven by the ion density and temperature perturbations in tokamak plasmas are investigated using gyrokinetic theory and simulations. The Rosenbluth–Hinton collisionless gyrokinetic model for ZFs is used, with model source terms designed to represent the drive from different plasma species in the plasma. For ZFs driven by the main ion density perturbations, the presence of impurities could decrease the amplitude of ZFs by increasing the total ion mass density of plasmas. For ZFs driven by the main ion temperature perturbations, the presence of impurities could decrease the amplitude of ZFs by decreasing the main ion mass density fraction. In addition, the driving effects of the impurity ion temperature perturbations on ZFs can be ignored, due to the low concentration of impurities.

We study the consistency between the formulas of the sound speed of the degenerate free electron gas at zero and finite temperature. Using the Sommerfeld expansion, we show that the expression of the sound speed of the degenerate free electron gas at finite temperature gives the expression of the sound speed of the degenerate free electron gas at zero temperature when the temperature tends to zero at fixed Fermi energy. The present and non-trivial work completes the previous results about the sound speed of the degenerate free electron gas at zero and finite temperature. It is also valid for the pure neutron or proton gas by considering the rest-mass of interest.

The design of inertial fusion experiments is a complex task as driver energy must be delivered in a precise manner to a structured target to achieve a fast, but hydrodynamically stable, implosion. Radiation-hydrodynamics simulation codes are an essential tool in this design process. However, multi-dimensional simulations that capture hydrodynamic instabilities are more computationally expensive than optimistic, one-dimensional (1D), spherically symmetric simulations, which are often the primary design tool. In this work, we develop a machine learning framework that aims to effectively use information from a large number of 1D simulations to inform design in the presence of hydrodynamic instabilities. We use an ensemble of neural network surrogate models trained on both 1D and 2D data to capture the space of good designs, i.e., those that are robust to hydrodynamic instabilities. We use this surrogate to perform Bayesian optimization to find optimal designs for a 25 kJ laser driver. The optimal designs share key similarities with traditional designs, such as shock timing, and familiar strategies are employed to increase robustness to hydrodynamic instability growth, such as thicker DT ice and higher power picket pulses. We perform hydrodynamic scaling on these designs to confirm the achievement of high gain for a 2 MJ laser driver, using 2D simulations including alpha heating effects.

We investigate the energy transfer rate ranging from the energy-containing to the sub-ion scales in the Earth's magnetotail bursty bulk flow turbulence, characterized by the dominance of either incompressible Alfvénic-like or compressible magnetosonic-like fluctuations, using data from the magnetospheric multiscale mission. At the energy-containing scale, the von Kármán decay law governs the energy budget. Inertial-range incompressible and compressible cascade rates are estimated using exact relations. A multi-spacecraft technique is employed to estimate the general divergence form of incompressible and compressible cascade rate at the kinetic scales, which partially measures the kinetic-scales turbulent energy transfer. Our results indicate that the energy-containing scale decay rate is close to the inertial-range cascade rate and is higher than the kinetic scale cascade rate. Moreover, the cascade rate in the bursty bulk flow is found to be higher than the ones observed both in the solar wind and Earth's magnetosheath. It is also shown that density fluctuations only slightly amplify the energy transfer rate.

Accurate forecasting of high-latitude ionospheric total electron content (TEC) is essential for mitigating space-weather impacts on Global Navigation Satellite System (GNSS) performance. This study benchmarks three machine-learning models—random forest (RF), support vector machine, and long short-term memory networks—against a three-point moving average [MA(3)], the International Reference Ionosphere (IRI-2020), and a Persistence baseline. Using GNSS-derived TEC from four high-latitude stations during the geomagnetically active year 2023, we evaluate model skill using root mean squared error (RMSE), MAE, R2, and error distributions. The results reveal a clear, physically interpretable regime dependence. In the auroral zone, RF reduces RMSE by up to 30% relative to persistence, demonstrating superior skill under storm-driven, intermittent variability. In the polar cap, MA(3) outperforms all machine-learning approaches, achieving R2=0.86–0.87 and reducing RMSE by ∼ 26%, reflecting the smooth, strongly autocorrelated nature of convection-driven TEC. Persistence provides the weakest forecasts but remains an essential lower benchmark. Feature relevance analysis shows that short-term lagged TEC and IRI climatology dominate prediction skill, with geomagnetic drivers exerting stronger influence in auroral regions. By linking forecast performance to plasma regime, this work establishes a physically grounded framework for TEC predictability and clarifies why nonlinear ensemble methods excel in auroral conditions while smoothing-based approaches dominate in the polar cap.

On the spherical Globus-M2 tokamak, small edge localized modes (ELMs) that develop in regimes with high toroidal magnetic field and plasma current values were studied using a multi-diagnostics approach. A systematic increase in Te and ne values by almost 50% in the scrape-off layer (SOL) was observed during ELM bursts. Direct measurements of divertor heat fluxes made in the form of floating potential were interpreted based on the analysis of the structure of the SOL and private flux region currents. The perpendicular plasma rotation velocity V⊥ values increased by about 50% during ELM bursts up to 7 cm inside the last closed field surface. Experimental observations indicate transport of fast ions with energies up to 52 keV (6 keV above the injection energy) and suprathermal electrons to the plasma periphery. Filamentary structures moving in a downward direction toward the divertor region at a velocity ranging from 3 to 10 km/s during the ELM cycle were estimated to be around 3 cm in diameter with a distance within a series of filaments of about 10 cm.

In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a fully convolutional neural network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit particle-in-cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with a smaller number of particles per cell and showing that our results generalize to a simulation with a large number of particles per cell. We evaluate the statistical properties of the learned equation of state, with a focus on pressure-strain interaction, which is crucial for understanding energy channels in turbulent plasmas. The resulting equation of state learned via FCNN significantly outperforms local closures, such as those learned by multi-layer perceptron (MLP) or double adiabatic expressions. We report that the overall spatial distribution of pressure-strain and its conditional averages are reconstructed well. However, some small-scale features are missed, especially for the off-diagonal components of the pressure tensor. Nevertheless, the results are substantially improved with more training data, indicating favorable scaling and potential for improvement, which will be addressed in future work.

We present and discuss systematic collisional-radiative calculations of tungsten plasmas at an electron density of about 5×1013 cm−3 and for temperatures in the range 50 eV–30 keV, a range relevant to the operation of fusion reactors. The calculations are based on state-of-the-art configuration average methods that include essential results of the unresolved transition array theory. Among our main results are the mean ionization, charge state distribution, broadband emissivity, and the cooling factor of tungsten. Some of these data are compared with previous predictions. The differences are noticeable, and a tentative discussion of these is presented throughout the article. Our results remain consistent with existing experimental results.