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At least 1,279 records · Page 71

Measurement of atomic oxygen densities using TALIF on a dielectric barrier discharge: insights into the volume above a micro cavity plasma array

Dielectric barrier discharges, particularly micro cavity plasma arrays, offer significant potential for plasma-catalytic research due to their ability to ignite plasma in direct contact with a catalytic surface, enabling the observation of plasma-surface interactions. A key factor in their application is the generation of reactive species, such as atomic oxygen, within the cavities. These species can interact with both the surface (e.g. for activation or cleaning) and the gas being treated (e.g. for oxidation). Given the central role of oxygen atoms in plasma catalysis and their use as a model for more complex species, this work investigates the transport of these atoms out of the cavities. Two-photon absorption laser-induced fluorescence spectroscopy with picosecond laser excitation is performed in the volume above the cavities. The results are compared with a basic diffusion model. The reactor operates with a He/O 2 mixture at a flow rate of 1 slm and atmospheric pressure. Densities of up to 10 16 cm -3 are measured near the surface. Time-dependent measurements show that, at a distance of 350 µm from the surface, a density equilibrium is reached within less than 3 ms of reactor operation. Decay times due to ozone formation after the reactor is turned off are on a similar scale. Spatially resolved measurements show that the oxygen density decreases exponentially from the surface but remains detectable up to approximately 1 mm above the surface, indicating significant application potential. Variations in the O 2 admixture show a density maximum at 0.4%, confirming previous helium state enhanced actinometry measurements within the cavities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Hermes-3: Multi-component plasma simulations with BOUT++

A new open source tool for fluid simulation of multi-component plasmas is presented, based on a flexible software design that is applicable to scientific simulations in a wide range of fields. Hermes-3 is built on plasma simulation framework BOUT++, consolidating earlier SD1D and Hermes models into a single code that can be configured at run-time to solve plasma models in 1D, 2D or 3D, either for transport (steady-state) or turbulent (time-evolving) problems, with an arbitrary number of ion and neutral species. Here, we describe the improved numerical algorithms and software design that have been implemented in Hermes-3. To demonstrate the capabilities of this tool, applications relevant to the boundary of tokamak plasmas are presented: 1D simulations of diveror plasmas evolving equations for all charge states of neon and deuterium; 2D transport simulations of tokamak equilibria in single-null X-point geometry with plasma ion and neutral atom species; and simulations of the time-dependent propagation of plasma filaments (blobs). Hermes-3 is publicly available on Github under the GPL-3 open source license. The repository includes documentation and a suite of unit, integrated and convergence tests.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Modeling resistive-inductive evolution of currents in Wendelstein 7-X

This research investigates the temporal evolution of the toroidal plasma current in the Wendelstein 7-X (W7-X) stellarator under different heating, fueling, and current drive scenarios. The THRIFT code has been modernized and its predictions of the evolution of the toroidal current have been compared against experimentally measured currents in W7-X. Good agreement is found with respect to the characteristic timescale between experimentally measured and simulated toroidal currents. The total bootstrap current is under-predicted owing to the applicability of the BOOTSJ model for the plasma collisionalities in question. Edge plasma resistivity is found to play an important role in the asymptotic behavior of the evolution of the current, indicating a possible limitation of the minimum plasma temperature when applying this model. Simulations of ECCD and heating power steps show THRIFT is capable of capturing the dynamical evolution of the current in response to changes in current sources. Future uses of THRIFT include validating and benchmarking other non-inductive current models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Advanced silver sheathed 2212/Ag wire for high field magnets

Commercial High Temperature Superconductors (HTS) are all tape shaped (widths > 10 times thickness), because achieving useful current densities required them to be processed into this form, even though this also makes it more problematic to use them in some important coil types. Our product is based on 2212 (Bi2Sr2CaCu2O8) and it is the only HTS that can be processed into high current density wire forms that are similar to proven low temperature superconductors (LTS), while surpassing them in field generating and operating temperature capability. Required now are higher current density, lower cost and longer length 2212 wires to enable broad utilization where HTS tape usage is problematic. In Phase II we developed vital cost-lowering process technology elements and we test-processed longer wires with the standard commercial design as baseline and benchmark for our lower cost, higher quality and longer piece length wire advances. By initial application of Jc-boosting wire features, these wires exhibited significant performance increases. This Phase IIA program has now developed and qualified these technologies for lower cost, higher quality and longer piece length manufacturing in combination with current density-boosting wire features, paving the way to manufacturing capability that is essential for cost-effective commercial utilization of 2212 wire. Technical areas included automated, high uniformity powder packing, advanced drawing, annealing, lubricant cleaning and filament bundling, followed by integration of higher current density wire features and process variations.

17 WIND ENERGY↗

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Harnessing metastability for grain size control in multiprincipal element alloys during additive manufacturing

Abstract Controlling microstructure in fusion-based metal additive manufacturing (AM) remains a significant challenge due to the many parameters that directly impact solidification condition. Multiprincipal element alloys (MPEAs), also known as high entropy alloys, offer a vast compositional space to design for microstructural engineering due to their chemical complexity and exceptional properties. Here, we use the FeMnCoCr system as a model platform for exploring alloy design in MPEAs for AM. By exploiting the decreasing stability of the face-centered cubic phase with increasing Mn content, we achieve notable grain refinement and breakdown of epitaxial columnar grain growth. We employ a multifaceted approach encompassing thermodynamic modeling, operando synchrotron X-ray diffraction, multiscale microstructural characterization, and mechanical testing to gain insight into the solidification physics and its ramifications on the resulting microstructure of FeMnCoCr MPEAs. This work aims toward tailoring desirable grain sizes and morphology through targeted manipulation of phase stability, thereby advancing microstructure control in AM applications.

Wakai, Akane↗

Turbulence-reduced high-performance scenarios in Wendelstein 7-X

In the Wendelstein 7-X (W7-X) stellarator, turbulence is the dominant transport mechanism in most discharges. This leads to a 'clamping' of ion temperature over a wide range of heating power, predominantly flat density profiles where hollow profiles driven by neoclassical thermo-diffusion would be expected and by rapid impurity transport in injection experiments. Significantly reduced turbulent transport is observed in the presence of strong core density gradients found transiently after core pellet injection and irregularly after boronisation or boron pellet injection. Density peaking is also achieved in a controlled manner in purely neutral beam heated discharges where particle transport analysis reveals an abrupt reduction in the main-ion particle flux leading to significant density profile peaking not explained by the NBI particle source alone. The plasmas exhibit a heat diffusivity of around $\chi = 0.25 \pm 0.1\,\mathrm{m}^2\ \mathrm{s}^{-1}$ at mid radius, a factor of around 4 lower than ECRH dominated discharges. Despite the improved confinement, the achieved ion temperature is limited by broader heat deposition and the lower power-per-particle given the higher density. This is overcome with limited reintroduction of ECRH power, where the low heat diffusivity diffusivity is maintained, the density rise supressed and ion temperatures above the clamping limit are achieved. The applicability of these plasmas for a high performance scenario on transport relevant time scales is assessed, including initial predictions for planned heating upgrades of W7-X, based on a range of assumptions about particle transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

54 ENVIRONMENTAL SCIENCES↗

Microtructure and Low-Cycle Fatigue Behavior of Additively Manufactured 316L Stainless Steel at 300°C

Metal additive manufacturing (AM) or 3D printing has the potential to transform the nuclear industry by producing high quality components faster and cheaper, thus enhancing the operating performance of current plants and advanced reactors. Two AM 316L tubes - intended to act as surrogates for complex components where nuclear equipment vendors are more likely to consider AM technologies - were printed using a Renishaw AM400 Laser Powder Bed Fusion (L-PBF) system. The porosity of the as-built material was found to be small, 0.06%. In prior research, the fatigue and corrosion fatigue crack growth rate (CGR) response of the AM specimens in the as-built condition was found to be similar to that expected for conventional alloys, and extremely resistant to stress corrosion cracking (SCC). The present research demonstrates that the low-cycle fatigue behavior of the AM material in air is similar to that of wrought stainless steel at LWR temperature. The data suggest that low porosity levels do not have a significant effect on the fatigue performance of AM materials. Taken together, all data generated to date demonstrate that the use of AM alloys in a nuclear environment is plausible. Additional research on environmental fatigue, crack initiation, and SCC needs to be conducted to provide the performance information needed to qualify AM materials for LWR applications.

36 MATERIALS SCIENCE↗

Use of hydrodynamic theory to estimate electrical current redistribution in metals

Using the analogy between hydrodynamic and electrical current flow, we study herein how electrical current density j redistributes and amplifies due to two commonly encountered inhomogeneities in metals. First, we consider flow around a spherical resistive inclusion and find significant j amplification, independent of inclusion size. Hence, even μ m-scale inclusions can affect performance in applications by creating localized regions of enhanced Joule heating. Next, we investigate j redistribution due to surface roughness, idealized as a sinusoidal perturbation with amplitude A and wavelength λ . Theory predicts that j amplification is determined by the ratio A / λ , so that even “smooth” surface finishes (i.e., small A ) can generate significant amplification, if λ is correspondingly small. We compare theory with magnetohydrodynamic simulation to illustrate both the utility and limitations of the steady-state theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A nanosecond pulsed discharge circuit model for engine applications

Non-equilibrium plasma discharges in spark gaps have been an increasingly studied method for alleviating cycle to cycle variation in lean and dilute combustion environments. However, ignition models that account for streamer propagation, cathode fall, and transmission line amplification over nanosecond time scales have so far not been developed. The present study develops such a model, with emphasis on the energy delivered from circuit to cylinder. Here, key pieces of the relevant physics and chemistry are summarized, simplified, and systematically coupled to one another. The set of parameters is limited to a handful of key observables and modeled using Modelica. Results show non-trivial behavior in the energy delivery characteristics of such discharges with important implications for ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

Onset of fast magnetic reconnection and particle energization in laboratory and space plasmas

The onset of magnetic reconnection in space, astrophysical and laboratory plasmas is reviewed discussing results from theory, numerical simulations and observations. After a brief introduction on magnetic reconnection and approach to the question of onset, in this paper we first discuss recent theoretical models and numerical simulations, followed by observations of reconnection and its effects in space and astrophysical plasmas from satellites and ground-based detectors, as well as measurements of reconnection in laboratory plasma experiments. Mechanisms allowing reconnection spanning from collisional resistivity to kinetic effects as well as partial ionization are described, providing a description valid over a wide range of plasma parameters, and therefore applicable in principle to many different astrophysical and laboratory environments. Finally, we summarize the implications of reconnection onset physics for plasma dynamics throughout the Universe and illustrate how capturing the dynamics correctly is important to understanding particle acceleration. The goal of this review is to give a view on the present status of this topic and future interesting investigations, offering a unified approach.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

X-ray imaging methods for high-energy density physics applications

Large scale high-energy density science facilities continue to grow in scale and complexity worldwide. The increase in driver capabilities, including pulsed-power and lasers, continue to push the boundaries of temperature, pressure, and densities, opening up new physics regimes. X-ray imaging is one of the many diagnostic techniques that are used to probe states of matter in these extreme conditions. Improved fabrication and polishing methods have provided improved x-ray microscope performance, while improving detector and x-ray sources now enable pico-second imaging with few micron resolutions. Here this Review will cover x-ray imaging methods, primarily absorption imaging, and their improvements over the last few decades.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Mitigation of plasma–wall interactions with low-Z powders in DIII-D high confinement plasmas

Experiments with low-Z powder injection in DIII-D high confinement discharges demonstrated increased divertor dissipation and detachment while maintaining good core energy confinement. Lithium (Li), boron (B), and boron nitride (BN) powders were injected in H-mode plasmas (I p = 1 MA, B t = 2 T, P NB = 6 MW, < n e > = 3.6–5.0 · 10 19 m –3 ) into the upper small-angle slot divertor for 2 s intervals at constant rates of 3–204 mg s –1 . The multi-species BN powders at a rate of 54 mg s –1 showed the most substantial increase in divertor neutral compression by more than an order of magnitude and lasting detachment with minor degradation of the stored magnetic energy W mhd by 5%. Rates of 204 mg s –1 of boron nitride powder further reduce edge localized mode-fluxes on the divertor but also cause a drop in confinement performance by 24% due to the onset of an n = 2 tearing mode. The application of powders also showed a substantial improvement of wall conditions manifesting in reduced wall fueling source and intrinsic carbon and oxygen content in response to the cumulative injection of non-recycling materials. Furthermore, the results suggest that low-Z powder injection, including mixed element compounds, is a promising new core-edge compatible technique that simultaneously enables divertor detachment and improves wall conditions during high confinement operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Magnetostrictive materials for enhanced sensors and electronic components

Magnetostriction is a property of magnetic materials that causes them to change their shape or dimensions proportional to their magnetization. Low-cost, mechanically robust magnetostrictive sensors would be valuable for a wide range of applications across the DOE and national security mission space, such as monitoring the internal conditions of pipelines, rapidly detecting high-impedance faults in power lines (e.g. trees touching power lines), or enhancing implantable systems for the human body. Dilute doping (<1 at.%) of rare earth elements (REE) has been shown to amplify the magnetostriction. Furthermore, alloy processing by rapid cooling from high temperature tends to result in texturing and therefore greater magnetostriction values, which bodes well for developing advanced manufacturing approaches significantly less expensive than single crystal growth. The goal of this effort was to develop robust, low-cost magnetostrictive materials compatible with advanced manufacturing techniques to exploit the rapid cooling of these approaches while retaining the ability to produce fully dense structures. To be useful as a sensor or actuator the coercivity of the material needs to be minimized so that there is minimal magnetic hysteresis. Employing a small scale laser powder bed fusion (L-PBF) system, specimens of Fe-Ga-Ce were built from alloy powder which had a magnetostriction of 289 ppm along the build direction and 197 ppm perpendicular to the build direction. These values are not far from the 310-350 ppm observed in single crystals of Fe-Ga. The results of the series of samples run suggest this is a very promising route for REE doping to higher levels, especially if the powders can be produced using far from equilibrium approaches such as ultrasonic atomization. The promising results from applying additive manufacturing techniques to these materials, particularly the inexpensive Fe-Al system, has potential for inexpensive high-performing magnetostrictive parts producible at large scales for low-cost sensors.

36 MATERIALS SCIENCE↗

Analysis of an induced Langmuir wave by ponderomotive forces and its applicability for plasma diagnostics

We present a numerical study on the electron and ion density perturbation in low-temperature plasmas driven by the frequency detuning of two intense laser beams. Our study is performed in the hydrodynamic regime, which becomes applicable when the plasma grating period induced by the beating of the laser beams is greater than the Debye length and collective processes such as plasma oscillations can be excited. Our findings show a resonance in electron density perturbation as the frequency detuning approaches a value consistent with the Bohm–Gross dispersion relation in low- and high-pressure plasmas. We discuss the potential of this resonance as a diagnostic tool for precisely measuring electron temperature and density in low-temperature plasmas through coherent scattering.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗