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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High reliability, long lifetime, negative ion source

A negative ion source includes a plasma chamber, a microwave source, a negative ion converter, a magnetic filter and a beam formation mechanism. The plasma chamber contains gas to be ionized. The microwave source transmits microwaves to the plasma chamber to ionize the gas into atomic species including hyperthermal neutral atoms. The negative ion converter converts the hyperthermal neutral atoms to negative ions. The magnetic filter reduces a temperature of an electron density provided between the plasma chamber and the negative ion converter. The beam formation mechanism extract the negative ions.

Sherman, Jason D.↗
NASA NTRS1982

The Venus ionosphere at grazing incidence of solar radiation - Transport of plasma to the night ionosphere

A quasi-two-dimensional model of the Venusian ionosphere is used to calculate the ion number densities and horizontal ion bulk velocities expected for a range of solar zenith angles near the terminator (80-100 deg). These results are compared with data from the Pioneer Venus Orbiter retarding potential analyzer. It is shown that antisunward horizontal plasma fluxes produced by solar EUV-induced pressure gradients are sufficient to maintain the nighttime ionosphere. While photoionization is the dominant source of ionospheric plasma for solar zenith angles less than 92 deg, plasma transport from the dayside is the dominant plasma source for solar zenith angles greater than 95 deg. It is also shown that the main nightside plasma peak at a height of 140 km is of the F2 type; its height and shape are therefore quite insensitive to the height of the ion source.

Whitten, R. C.↗
NASA NTRS1981

Atomic clouds as distributed sources for the Io plasma torus

Several recent developments have implications for the neutral particle environment of Jupiter. Very hot sulfur ions have been detected in the Io torus with gyrospeeds comparable to the corotation speed, a phenomenon that would result from a neutral sulfur cloud. Current evidence supports the hypothesis that extensive neutral clouds of oxygen and sulfur exist in the Jupiter magnetosphere and that they are important sources of ions and energy for the Io torus.

Brown, R. A.↗
DOE OSTI2022

Microwave breakdown of sub-atmospheric argon gas in the vicinity of a microgap

The microwave electric field at breakdown for argon gas in a microgap (generally, gap size <1 mm) over a wide range of pressures and gap sizes was found to exhibit two behavioral pressure regimes. A high microwave electric field was established in the gap of a re-entrant mode resonator. By slowly raising the microwave electric field level, a breakdown level is reached where impact ionization induces low pressure argon gas to breakdown preferentially outside of the gap. At high pressure, breakdown is found to occur preferentially inside the gap, but only after the pressure is high enough for impact ionization to produce breakdown there. Impact ionization has a lower threshold electric field than the boundary-controlled processes, such as secondary electron emission, that would eventually produce breakdown inside the microgap if impact ionization did not occur first. Thus, the lower pressure regime is characterized by breakdown outside of the gap, while in the high-pressure regime, breakdown occurs inside the gap, in both cases by impact ionization. The transition pressure P T between these two pressure regimes depends on the gap size d gap , roughly as P T ∝ d gap –2 . Consistent with the model developed here, this corresponds to the full-amplitude of electron oscillation in the gap field being commensurate with the gap size.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1990

Triton torus and Neptune aurora

Triton is shown to be the dominant source of plasma for L equal to or greater than 7 in the magnetosphere of Neptune. Triton maintains a neutral hydrogen torus of average density comparable to a greater than that of the Titan torus at Saturn. The Triton torus may be detectable in H Lyman-alpha emissions. However, the energy source from plasma outward transport and mass loading in the Triton torus is insufficient to explain the Neptune aurora. It is proposed that Neptune's aurora is driven mainly by a solar wind interaction.

Cheng, Andrew F.↗
DOE OSTI2021

Experimental validation of an integrated modelling approach to neutron emission studies at JET

An integrated modelling methodology for the calculation of realistic plasma neutron sources for the JET tokamak has been developed. The computational chain comprises TRANSP plasma transport and DRESS neutron spectrum calculations, and their coupling to the MCNP neutron transport code, bridging plasma physics and neutronics. Here, we apply the developed methodology to the analysis of neutron emission properties of deuterium and helium plasmas at JET, and validate individual modelling steps against neutron diagnostic measurements. Two types of JET discharges are modelled—baseline-like and three-ion radio-frequency scenarios—due to their diversity in plasma heating, characteristics of the induced fast ion population, and the imprint of these on neutron emission properties. The neutron emission modelling results are quantitatively compared to the total neutron yield from fission chambers, neutron emissivity profiles from the neutron camera, neutron spectra from the time-of-flight spectrometer, and neutron activation measurements. The agreement between measured and calculated quantities is found to be satisfactory for all four diagnostic systems within the estimated experimental and computational uncertainties. Additionally, the effect of neutrons not originating from the dominating D(D, n) 3 He reactions is studied through modelling of triton burnup DT neutrons, and, in mixed D- 3 He plasmas, neutrons produced in the 9 Be(D, nγ) 10 B reaction on impurities. It is found that these reactions can contribute up to several percent to the total neutron yield and dominate the neutron activation of samples. Finally, the effect of MeV-range fast ions on the neutron activation of 115 In and 27 Al samples is measured and computationally validated.

integrated modelling↗
DOE OSTI2025

Fluid modeling of low-temperature plasmas

Fluid models are essential for understanding and predicting low-temperature plasma (LTP) behavior in various scientific and industrial settings. This paper provides an introductory tutorial on fluid modeling of LTPs, covering model formulation, implementation, and computational simulations. The tutorial focuses on five main components of the formulation of LTP fluid models: fluid flow, energy, chemistry, electromagnetism, and material properties, as well as in essential aspects of model implementations, including multiscale phenomena, multiphysics coupling, and numerical convergence. Designed for students and early-career researchers, this work offers a practical foundation for developing and using fluid models, from in-house computational codes to commercial software, bridging fundamental theory with real-world applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1975

Substorm-associated reconfiguration of the dusk side equatorial magnetosphere - A possible source mechanism for isolated plasma regions

A rapid reconfiguration of the dusk side magnetosphere was observed near the magnetic equator by particle and field experiments aboard Explorer 45 at 1715 UT on June 17, 1972. Following the onset of an 800-gamma negative bay in the auroral zone, Explorer 45, inbound at 1700 MLT and L about 5, observed a sharp dropout of outer zone electrons. Simultaneously, the plasma density dropped from a level typical of the plasmasphere to one typical of the ion trough, and plasma sheet proton fluxes increased, producing a 100-gamma depression in B, in a shift to a taillike configuration. Outer zone electrons with pitch angles near 45 deg returned with delay times consistent with injection near midnight at the time of dropout and eastward drift to the dusk meridian. Approximately 45 min after the reconfiguration, the satellite reentered the plasmasphere, and the electron fluxes returned to previous levels. In the next orbit, approximately 6 hours later, a region of isolated plasma was detected near apogee at 1400 MLT. There is therefore circumstantial evidence that the type of magnetospheric reconfiguration observed here is responsible for the detachment of regions of cold plasma from the plasmasphere.

Barfield, J. N.↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗