Hasil untuk "Plasma physics. Ionized gases"

Nadia Derakhshan, Mahboub Hosseinpour

Kelvin–Helmholtz instability (KHI) is a shear flow-driven instability that imposes important changes in the macroscopic dynamics of some space and laboratory magnetized plasmas, such as the solar corona and astrophysical jets. Earth’s magnetopause and Tokamak devices. Using two-dimensional resistive magnetohydrodynamic (MHD) simulations, the effect of uniform resistivity on the amplification of magnetic energy during KHI with a uniform magnetic field is studied. Dimensionless resistivity value varies by four orders of magnitude, with the minimum value of 1.0×10−7. Irrespective of the resistivity value, the results indicate that, up to a specific time, amplification of magnetic energy, in particular in the linear and early nonlinear phases of KHI, happens by the flow’s work on the magnetic field. This work is mainly efficient on the boundaries of growing vortices of KHI, where, consequently, the magnetic field is amplified significantly. As the KHI proceeds into the fully nonlinear regime, magnetic energy dissipation via Ohmic heating balances the flow’s work, so the magnetic energy becomes saturated. We found that the sporadic magnetic reconnection initiated by KHI in the turbulent regime plays an important role in dissipating and converting magnetic energy within strong current sheets. We also show that increasing the plasma resistivity weakens the mechanism of generating magnetic energy. The amplification of the magnetic energy is completely suppressed in a highly collisional plasma.

Arvind Sundaram, Shiming Yin, Ugur Mertyurek et al.

This manuscript proposes a novel information-theoretic approach to the quantification of experimental relevance, i.e., coverage, to achieve optimal data assimilation results for nuclear engineering applications. Specifically, this work posits the need for a new metric, called coverage (qC) of an application’s quantity of interest, i.e., eigenvalue or power peaking for an advanced reactor concept, defined herein as the theoretically maximum achievable reduction in the quantity’s uncertainty given measurements from a pool of experiments in a manner that is independent of the data assimilation procedure employed. Currently, reduction in a quantity’s uncertainty is strongly biased by the underlying assumptions of the assimilation procedure to account for the under-determined nature of such problems and the similarity criterion employed to identify relevant experiments. To address this challenge, this work has developed a coverage metric, qC, based on mutual information, which establishes a new conceptual framework for assessing coverage, one that is independent of the model parameters and responses degree of variations in both the experimental and application domains, i.e., linear vs non-linear, and their prior uncertainty distributions, i.e., Gaussian vs. non-Gaussian. The qC is an entropic measure capable of addressing coverage for general nonlinear problems with non-Gaussian uncertainties and inclusive of the measurement uncertainties from multiple experiments. Numerical experiments from manufactured analytical problems as well as a set of benchmarks from the ICSBEP handbook are employed to demonstrate its theoretical and practical performance as compared to the ck-based experiment selection methodology, commonly employed in the neutronic community. The manuscript then employs other well-known adaptations to existing data assimilation methodologies for nonlinear and non-Gaussian problems capable of achieving the coverage posited by qC.

Amit Kumar, Amit Kumar, Sanjeev Gupta et al.

Spiros Alexiou

The purpose of this paper is to address conflicting results regarding a simple criterion that has been proposed as decisive in determining whether accounting for spiralling electron trajectories increases or decreases the widths of hydrogen lines in a parameter range relevant to the spectral lines of white dwarfs. We analyse the claims in detail and also provide explicit calculations. It is shown that the recent attempts to justify a simple theory are erroneous and miss important physics.

Erika Neeft, Guido Deissmann, Diederik Jacques

Within the framework of the European Joint Programme on Radioactive Waste Management, the work package ACED–Assessment of chemical evolution of intermediate level (ILW) and high level (HLW) waste at disposal cell scale–used combined experimental and modelling methods in a multi-scale approach with process integration to improve the long-term modelling and assessment of the chemical evolution at the disposal cell scale. Part I provides the relevance of the assessment of the chemical evolution for safety, performance, and optimization. It further describes the main characteristics of disposal cells for ILW and vitrified HLW waste in European disposal programmes. From that, a number of interfaces between different types of material are identified that are highly relevant for many national disposal programs: glass-steel, steel-concrete, steel-clay, steel-crystalline, concrete-clay, and concrete-crystalline. Based on literature review, the main processes and consequences occurring at these interfaces are described. The key element is the narrative of the evolution at the disposal cell scale based on process understanding. In part II, tools to obtain process understanding–experiments, analogues, modelling–are discussed in detail.

Andrey Kozyrev, Victor Tarasenko

Runaway electron (RAE) generation in high-pressure gases is an important physical phenomenon that significantly influences discharge shapes and properties of initiated plasma. The diffuse discharges formed due to RAEs in the air and other gases at atmospheric pressure find wide applications. In the present review, theoretical and experimental results that explain the reason for RAE occurrence at high pressures are analyzed, and recommendations are given for the implementation of conditions under which the runaway electron beam (RAEB) with the highest current can be obtained at atmospheric pressure. The experimental results were obtained using subnanosecond, nanosecond, and submicrosecond generators, including those specially developed for runaway electron generation. The RAEBs were recorded using oscilloscopes and collectors with picosecond time resolution. To theoretically describe the phenomenon of continuous electron acceleration, the method of physical kinetics was used based on the Boltzmann kinetic equation that takes into account the minimum but sufficient number of elementary processes, including shock gas ionization and elastic electron scattering. The results of modeling allowed the main factors to be established that control the RAE appearance, the most important of which is electron scattering on neutral atoms and/or molecules. Theoretical modeling has allowed the influence of various parameters (including the voltage, pressure, gas type, and geometrical characteristics of the discharge gap) to be taken into account. The results of the research presented here allow RAE accelerators with desirable parameters to be developed and the possibility of obtaining diffuse discharges to be accessed under various conditions. The review consists of the Introduction, five sections, the Conclusion, and the References.

Hu-Sheng Li, He Huang, Wei Yang et al.

Machine learning methods have been widely used in the investigations of the complex plasmas. In this paper, we demonstrate that the unsupervised convolutional neural network can be applied to obtain the melting line in the two-dimensional complex plasmas based on the Langevin dynamics simulation results. The training samples do not need to be labeled. The resulting melting line coincides with those obtained by the analysis of hexatic order parameter and supervised machine learning method.

Chengyuan Li, Chengyuan Li, Meifu Li et al.

Introduction: The accurate prognosis of reactor accidents is essential for deploying effective strategies that prevent radioactive releases. However, research in the nuclear sector is limited. This paper introduces a novel Temporal Fusion Transformer (TFT) model-based method for accident prognosis that incorporates multi-headed self-attention and gating mechanisms.Methods: Our proposed method combines multi-headed self-attention and gating mechanisms of TFT with multiple covariates to enhance prediction accuracy. Additionally, we employ quantile regression for uncertainty assessment. We apply this method to the HPR1000 reactor to predict outcomes following loss of coolant accidents (LOCAs).Results: The experimental results reveal that our proposed method outperforms existing deep learning-based prediction models in both prediction accuracy and confidence intervals. We also demonstrate increased robustness through interference experiments with varying signal-to-noise ratios and ablation studies on static covariates.Discussion: Our method contributes to the development of intelligent and reduced-staff maintenance methods for reactor systems, showcasing its ability to effectively extract and utilize features of static and historical covariates for improved predictive performance.

Josef Krása, Michal Krupka, Shubham Agarwal et al.

This article provides an up-to-date overview of the problems associated with the detection of hot electrons escaping from laser-produced plasma and corresponding return current flowing from the ground to the target, which neutralises the positive charge occurring on the target due to the escaped electrons. In addition, the target holder system acts as an antenna emitting an electromagnetic pulse (EMP), which is powered by the return target. If the amount of positive charge generated on the target is equal to the amount of charge carried away from the plasma by the escaping electrons, the measurement of the return current makes it possible to determine this charge, and thus also the number of escaped electrons. Methods of return current detection in the mA–10 kA range is presented, and the corresponding charge is compared to the charge determined using calibrated magnetic electron energy analysers. The influence of grounded and insulated targets on the number of escaped electrons and EMP intensity is discussed. In addition to EMP detection, mapping of the electrical potential near the target is mentioned.

Suresh Basnet, Roshan Chalise, Num Prasad Acharya et al.

For a wide range of plasma applications across diverse fields, a comprehensive understanding of the plasma-wall interaction mechanism is indispensable due to its inherent connection with confined plasma. This work compilation delves into the Kinetic Trajectory Simulation (KTS) method for the interaction of multi-component magnetized plasma with wall, specifically focusing on its implications for the tungsten wall sputtering model. In the evolution of the 1d3v (one dimensional spatial coordinates and three dimensional velocity coordinates) KTS method, the coupled set of kinetic equations has been solved under specified boundary conditions which yields results of higher accuracy. At the particle injection boundary, we have assumed the velocity distribution function of particle species to be cut-off Maxwellians, meeting essential requirements for plasma-wall transition processes: quasineutrality, sheath edge singularity, continuity of macroscopic fluid variables, and the kinetic Bohm sheath condition. The kinetic Bohm sheath condition, a fundamental criterion for plasma sheath formation, is extended for multi-component plasmas, accounting for the cut-off Maxwellian distribution of negatively charged particles. A comparative study of the kinetic Bohm sheath condition for cut-off and Boltzmann distributions reveals a deviation of less than 2.0% in magnitude. The concentration ratio of positive or negative ion species and the presheath side electron temperature influence various plasma-wall transition characteristics, including wall potential, Debye sheath thickness, particle densities, potential distribution, particle fluxes towards the surface, particle drift velocity, phase-space trajectory evolution, and physical sputtering of the tungsten surface. Although lighter ions possess higher energy when striking the surface, the physical sputtering yield of the tungsten surface is greater for heavier ions due to their lower threshold energy and larger collision cross-section. Furthermore, a comparative study of plasma-wall transition properties using kinetic and fluid approaches demonstrates qualitative similarities, with a notable deviation of approximately 4.0% in the magnitude in the vicinity of the material surface.

Keh-Fei Liu

A recent proposal of accelerator based fusion reactor considers a scheme where an ion beam from the accelerator hits the target plasma on the resonance of the fusion reaction so that the reactivity (σv) can be an order of magnitude larger than that of a thermonuclear reactor. One of the important inputs is the stopping power which is needed to assess the energy loss of the beam in the plasma. In this work, we shall use the analytic formulation of Brown, Preston and Singleton [1] to calculate the temperature dependence of the stopping power due to the target t,He3, and B11 plasmas in the resonance regions of their respective fusion reactions, i.e., d+t→n+α,d+3He→p+α, and p+11B→3α. It is found that the calculated stopping power, especially when the quantum corrections are included, does not go down with temperature as fast at T−3/2. Instead it decreases slower, more like T−x with x≤1 in the range of T from ∼ 5 to 50 keV for d on t and He3 plasmas around their resonance energies.

Jamiah Thomas, Alexander G. Volkov

A cold atmospheric-pressure He-plasma jet (CAPPJ) interacts with air and water, producing reactive oxygen and nitrogen species (RONS), including biologically active ions, radicals, and molecules such as NO_x, H₂O₂, HNO₃, HNO₂, and O₃. These compounds can activate interfacial redox processes in biological tissues. The CAPPJ can oxidize N₂ to HNO₃ and water to H₂O₂ at the interface between plasma and water. It can also induce the oxidation of water-soluble redox compounds in various organisms and in vitro. This includes salicylic acid, hydroquinone, and mixtures of antioxidants such as L (+)-ascorbic acid sodium salt with NADPH. It can react with redox indicators, such as ferroin, in a three-phase system consisting of air, CAPPJ, and water. Without reducing agents in the water, the CAPPJ will oxidize the water and decrease the pH of the solution. When antioxidants such as ascorbate, 1,4-hydroquinone, or NADPH are present in the aqueous phase, the CAPPJ oxidizes these substances first and then oxidizes water to H₂O₂. The multielectron mechanisms of the redox reactions in the plasma-air/water interfacial area are discussed and analyzed.

Subramanya Bhat K.N., Amita Das, V. Ravishankar et al.

The dynamics of strongly interacting particles are governed by Yang–Mills (Y–M) theory, which is a natural generalization of Maxwell Electrodynamics (ED). Its quantized version is known as quantum chromodynamics (QCD) (Gross and Wilczek, 1973; Politzer, 1973; ’t Hooft, 1972[1–3]) and has been very well studied. Classical Y–M theory is proving to be equally interesting because of the central role it plays in describing the physics of quark–gluon plasma (QGP) — which was prevalent in the early universe and is also produced in relativistic heavy ion collision experiments. This calls for a systematic study of classical Y–M theories. A good insight into classical Y–M dynamics would be best obtained by comparing and contrasting the Y–M results with their ED counterparts. In this article, a beginning has been made by considering streaming instabilities in Y–M fluids. We find that in addition to analogues of ED instabilities, novel nonabelian modes arise, reflecting the inherent nonabelian nature of the interaction. The new modes exhibit propagation/ growth, with growth rates that can be larger than what we find in ED. Interestingly, we also find a mode that propagates without getting affected by the medium.

Scott E. Parker, Calder S. Haubrich, Stefan Tirkas et al.

Theory-based transport modeling has been widely successful and is built on the foundations of quasilinear theory. Specifically, the quasilinear expression of the flux can be used in combination with a saturation rule for the toroidal mode amplitude. Most transport models follow this approach. Saturation rules are heuristic and difficult to rigorously derive. We compare three common saturation rules using a fairly accurate quasilinear expression for the fluxes computed using local linear gyrokinetic simulation. We take plasma parameters from experimental H-mode profiles and magnetic equilibrium and include electrons, deuterium, and carbon species. We find that the various saturation rules provide qualitatively similar behavior. This may help to explain why the different theory-based transport models can all predict core tokamak profiles reasonably well. Comparisons with nonlinear local and global gyrokinetic simulations are discussed.

A. Ulbricht, J. Dykas, J. Dykas et al.

Post-irradiation annealing of neutron-irradiated reactor pressure vessel steels is a matter of both technical and scientific interest. Small-angle neutron scattering (SANS), while being sensitive to nm-sized irradiation-induced solute-atom clusters, provides macroscopically representative and statistically reliable measures of cluster volume fraction, number density and size. In the present study, SANS was applied to uncover the size distribution of clusters in as-irradiated samples of a VVER-1000 weld and their gradual dissolution as function of the post-irradiation annealing temperature. The same samples were used to measure Vickers hardness. The results are consistent with Mn-Ni-Si-rich clusters of less than 2 nm radius to be the dominant source of both scattering and hardening. Annealing gave rise to small but significant partial recovery at 350°C and almost complete recovery at 475°C. The dispersed-barrier hardening model was applied to bridge the gap between the characteristics of nano-features and macro-hardness.

Jinqiang Shi, Fubao Jin, Shangang Ma et al.

The DC negative corona of needle-plate electrodes can generate atmospheric pressure low-temperature plasma active particles, which have important effects on biological mutagenesis. The DC negative corona discharge of an air needle-plate electrode with effective consideration of NO_x particles was simulated and the Trichel pulse current was obtained, focusing on the development of particles and the distribution of active nitrogen oxides (RONS) at four moments in the pulse process. The simulation results indicate that the positive ions (N₂⁺ and O₂⁺) and negative ions (O⁻ and O₂⁻) were closely related to the current changes, and the negative ions (O⁻ and O₂⁻) presented a typical stratification phenomenon. RONS (H₂O₂, O₃, and NO) were approximately uniformly distributed above the level of the plate electrode at the same instant, with H₂O₂ and O₃ except for the area below the needle tip. They trended to a cumulative increase in concentration with time. This study provides a theoretical basis for corona discharge plasma seed treatment technology.

Baolian Cheng, Paul A. Bradley

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

Natalie M. Stuart, Karl Sohlberg

A vibrational analysis of various poly(o-aminophenol) structures has been undertaken using first principles methods. It is shown that a mixture of quinoid and keto forms of poly(o-aminophenol) gives rise to a simulated spectrum that replicates the experimental infrared spectra of plasma-produced poly(o-aminophenol) better than either the quinoid or keto poly(o-aminophenol) spectra alone. An unassigned peak in the spectrum is attributed to hydrogen bonding to the silica substrate.

Mikhail Gryaznevich, Valery A. Chuyanov, Yuichi Takase

Traditionally, spherical tokamak (ST) reactors are considered to operate in a steady state. This paper analyses the advantages of a pulsed ST reactor. The methodology developed for conventional tokamak (CT) reactors is used and it is shown that advantages of a pulsed operation are even more pronounced in an ST reactor because of its ability to operate at a higher beta, therefore achieving a higher bootstrap current fraction, which, together with a lower inductance, reduces requirements for magnetic flux from the central solenoid for the plasma current ramp-up and sustainment.