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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Adiabatic particle motion in a nearly drift-free magnetic field: Application to the geomagnetic tail

The guiding center motion of particles in a nearly drift free magnetic field is analyzed in order to investigate the dependence of mean drift velocity on equatorial pitch angle, the variation of local drift velocity along the trajectory, and other properties. The mean drift for adiabatic particles is expressed by means of elliptic integrals. Approximations to the twice-averaged Hamiltonian W near z = O are derived, permitting simple representation of drift paths if an electric potential also exists. In addition, the use of W or of expressions for the longitudinal invariant allows the derivation of the twice averaged Liouville equation and of the corresponding Vlasov equation. Bounce times are calculated (using the drift-free approximation), as are instantaneous guiding center drift velocities, which are then used to provide a numerical check on the formulas for the mean drift.

Stern, D. P.↗
NASA NTRS1994

Empirical models of the magnetospheric magnetic field

A general overview of magnetospheric modeling is given, along with a more detailed discussion of several empirical models which are widely used. These models are composed of representations of the Earth's main internal field (basically a bipolar field), plus external field contributions due to ring currents (carried by the particles in the Van Allen radiation belts), magnetopause currents (the boundary surface between the Earth's magnetic field and interplanetary magnetic field carried by the solar wind), and tail currents (carried by particles in the neutral sheet of the magnetotail). The empirical models presented here are the Mead-Fairfield, Olsen-Pfitzer tilt-dependent (1977), Tsyganenko-Usamo, Tsyganenko (1987), Olsen-Pfitzer dynamic (1988), Tsyganenko (1989), and Hilmer-Voight models. The derivations, agreement with quiet time and storm time data from the two satellite programs, Spacecraft Charging at High Altitudes (SCATHA) and Combined Release Radiation Effects Satellite (CRRES), and computational requirements of these models are compared.

Jordan, C. E.↗
NASA NTRS2013

Atmospheric Dissolved Iron Depostiion to the Global Oceans: Effects of Oxalate-Promoted Fe Dissolution, Photochemical Redox Cycling, and Dust Mineralogy

Mineral dust deposition is suggested to be a significant atmospheric supply pathway of bioavailable iron (Fe) to Fe-depleted surface oceans. In this study, mineral dust and dissolved Fe (Fed) deposition rates are predicted for March 2009 to February 2010 using the 3-D chemical transport model GEOS-Chem implemented with a comprehensive dust-Fe dissolution scheme. The model simulates Fed production during the atmospheric transport of mineral dust taking into account inorganic and organic (oxalate)-promoted Fe dissolution processes, photochemical redox cycling between ferric (Fe(III)) and ferrous (Fe(II)) forms of Fe, dissolution of three different Fe-containing minerals (hematite, goethite, and aluminosilicates), and detailed mineralogy of windblown dust from the major desert regions. Our calculations suggest that during the yearlong simulation is approximately 0.26 Tg (1 Tg = 1012 g) of Fed was deposited to global oceanic regions. Compared to simulations only taking into account proton-promoted Fe dissolution, the addition of oxalate to the dust-Fe mobilization scheme increased total annual model-predicted Fed deposition to global oceanic regions by approximately 75%. The implementation of Fe(II)/Fe(III) photochemical redox cycling in the model allows for the distinction between different oxidation states of deposited Fed. Our calculations suggest that during the daytime, large fractions of Fed deposited to the global oceans is likely to be in Fe(II) form, while nocturnal fluxes of Fed are largely in Fe(III) form. Model simulations also show that atmospheric fluxes of Fed can be strongly influenced by the mineralogy of Fe-containing compounds. This study shows that Fed deposition to the oceans is controlled by total dust-Fe mass concentrations, mineralogy, the surface area of dust particles, atmospheric chemical composition, cloud processing, and meteorological parameters and exhibits complex and spatiotemporally variable patterns. Our study suggests that the explicit model representation of individual processes leading to Fed production within mineral dust are needed to improve the understanding of the atmospheric Fe cycle, and quantify the effect of dust-Fe on ocean biological productivity, carbon cycle, and climate.

Johnson, M. S.↗
NASA NTRS2015

Jovian plasma modeling for mission design

The purpose of this report is to address uncertainties in the plasma models at Jupiter responsible for surface charging and to update the jovian plasma models using the most recent data available. The updated plasma environment models were then used to evaluate two proposed Europa mission designs for spacecraft charging effects using the Nascap-2k code. The original Divine/Garrett jovian plasma model (or "DG1", T. N. Divine and H. B. Garrett, "Charged particle distributions in Jupiter's magnetosphere," J. Geophys. Res., vol. 88, pp. 6889-6903,1983) has not been updated in 30 years, and there are known errors in the model. As an example, the cold ion plasma temperatures between approx.5 and 10 Jupiter radii (Rj) were found by the experimenters who originally published the data to have been underestimated by approx.2 shortly after publication of the original DG1 model. As knowledge of the plasma environment is critical to any evaluation of the surface charging at Jupiter, the original DG1 model needed to be updated to correct for this and other changes in our interpretation of the data so that charging levels could beproperly estimated using the Nascap-2k charging code. As an additional task, the Nascap-2k spacecraft charging tool has been adapted to incorporate the so-called Kappa plasma distribution function--an important component of the plasma model necessary to compute the particle fluxes between approx.5 keV and 100 keV (at the outset of this study,Nascap-2k did not directly incorporate this common representation of the plasma thus limiting the accuracy of our charging estimates). The updating of the DG1 model and its integration into the Nascap-2k design tool means that charging concerns can now be more efficiently evaluated and mitigated. (We note that, given the subsequent decision by the Europa project to utilize solar arrays for its baseline design, surface charging effects have becomeeven more of an issue for its mission design). The modifications and results of those modifications to the DG1 model to produce the new DG2 model presented here and the steps taken to integrate the DG2 predictions into Nascap-2k are described in this report.

Voyager 2↗
NASA NTRS2015

Jovian Plasma Modeling for Mission Design

The purpose of this report is to address uncertainties in the plasma models at Jupiter responsible for surface charging and to update the jovian plasma models using the most recent data available. The updated plasma environment models were then used to evaluate two proposed Europa mission designs for spacecraft charging effects using the Nascap-2k code. The original Divine/Garrett jovian plasma model (or "DG1", T. N. Divine and H. B. Garrett, "Charged particle distributions in Jupiter's magnetosphere," J. Geophys. Res., vol. 88, pp. 6889-6903,1983) has not been updated in 30 years, and there are known errors in the model. As an example, the cold ion plasma temperatures between approx.5 and 10 Jupiter radii (Rj) were found by the experimenters who originally published the data to have been underestimated by approx.2 shortly after publication of the original DG1 model. As knowledge of the plasma environment is critical to any evaluation of the surface charging at Jupiter, the original DG1 model needed to be updated to correct for this and other changes in our interpretation of the data so that charging levels could beproperly estimated using the Nascap-2k charging code. As an additional task, the Nascap-2k spacecraft charging tool has been adapted to incorporate the so-called Kappa plasma distribution function--an important component of the plasma model necessary to compute the particle fluxes between approx.5 keV and 100 keV (at the outset of this study,Nascap-2k did not directly incorporate this common representation of the plasma thus limiting the accuracy of our charging estimates). The updating of the DG1 model and its integration into the Nascap-2k design tool means that charging concerns can now be more efficiently evaluated and mitigated. (We note that, given the subsequent decision by the Europa project to utilize solar arrays for its baseline design, surface charging effects have becomeeven more of an issue for its mission design). The modifications and results of those modifications to the DG1 model to produce the new DG2 model presented here and the steps taken to integrate the DG2 predictions into Nascap-2k are described in this report

Garrett, Henry B.↗
NASA NTRS2018

Multi-Scale Modeling of Low-Density Carbon-Phenolic Ablators

Protecting a spacecraft during atmospheric entry is one of highest risk factors that needs to be mitigated during design of a space exploration mission. At entry speeds from space, air turns into high-temperature plasma, and spacecraft Thermal Protection Systems (TPS) are needed to protect the vehicle payload. Modern successful material architectures of spacecraft shields use a porous carbon fiber substrate impregnated with phenolic as an ablator material. In the lecture, efforts to build a Predictive Material Modeling framework for porous ablators from micro-scale to macro-scale will be presented. Several numerical methods and techniques will be summarized that use voxelized images to compute geometrical properties of the porous substrate. These computed properties include porosity, specific surface area and tortuosity that are otherwise indirectly measured through experimental techniques. Direct simulation Monte Carlo (DSMC), a particle-based method for approximating the Boltzmann equation, is used to compute the permeability coefficient of the porous substrate based on its digitized representation. The method computes the flow within the microstructure, where the size of the pores may approach the mean-free-path of the flow. Finally, a high-fidelity model implemented in PATO (Porous-material Analysis Toolbox) is discussed, and some examples of ablative material response are presented, including for the first time 3D simulations of the full tiled heat shield for the Mars Science Laboratory (MSL) capsule.

Ablators↗
DOE OSTI2020

A Dynamic Reduction Network for Point Clouds

Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs. In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure. It achieves this by dynamically learning the most important relationships between data via an intermediate clustering. The network architecture yields interesting results considering representation size and efficiency. It also adapts easily to a large number of tasks from image classification to energy regression in high energy particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A coarse-grained model of clay colloidal aggregation and consolidation with explicit representation of the electrical double layer

The aggregation of clay minerals in liquid water exemplifies colloidal self-assembly in nature. These negatively charged aluminosilicate platelets interact through multiple mechanisms with different sensitivities to particle shape, surface charge, aqueous chemistry, and interparticle distance and exhibit complex aggregation structures. Experiments have difficulty resolving the associated colloidal assemblages at the scale of individual particles. Conversely, all-atom molecular dynamics (MD) simulations provide detailed insight on clay colloidal interaction mechanisms, but they are limited to systems containing a few particles. We develop a new coarse-grained (CG) model capable of representing assemblages of hundreds of clay particles with accuracy approaching that of MD simulations, at a fraction of the computational cost. Our CG model is parameterized based on MD simulations of a pair of smectite clay particles in liquid water. A distinctive feature of our model is that it explicitly represents the electrical double layer (EDL), i.e., the cloud of charge-compensating cations that surrounds the clay particles. Our model captures the simultaneous importance of long-range colloidal interactions (i.e., interactions consistent with simplified analytical models, already included in extant clay CG models) and short-range interactions such as ion correlation and surface and ion hydration effects. The resulting simulations correctly predict, at low solid-water ratios, the existence of ordered arrangements of parallel particles separated by water films with a thickness up to ~10 nm and, at high solid-water ratios, the coexistence of crystalline and osmotic swelling states, in agreement with experimental observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators

We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Kolmogorov-Arnold wavefunctions

Here, this work investigates Kolmogorov-Arnold network-based (KAN) wave-function Ansätz as viable representations for quantum Monte Carlo simulations. Through systematic analysis of one-dimensional model systems, we evaluate their computational efficiency and representational power against established methods. Our numerical experiments suggest some efficient training methods and we explore how the computational cost scales with desired precision, particle number, and system parameters. Roughly speaking, KANs seem to be 10 times cheaper computationally than other neural-network-based Ansätz . We also introduce a novel approach for handling strong short-range potentials—a persistent challenge for many numerical techniques—which generalizes efficiently to higher-dimensional, physically relevant systems with short-ranged strong potentials common in atomic and nuclear physics.

1-dimensional systems↗
DOE OSTI2024

Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment

Abstract. Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Theory of multicolor lattice gas - A cellular automaton Poisson solver

The present class of models for cellular automata involving a quiescent hydrodynamic lattice gas with multiple-valued passive labels termed 'colors', the lattice collisions change individual particle colors while preserving net color. The rigorous proofs of the multicolor lattice gases' essential features are rendered more tractable by an equivalent subparticle representation in which the color is represented by underlying two-state 'spins'. Schemes for the introduction of Dirichlet and Neumann boundary conditions are described, and two illustrative numerical test cases are used to verify the theory. The lattice gas model is equivalent to a Poisson equation solution.

Chen, H.↗
NASA NTRS1993

Remarks about massive and massless particles in supersymmetry

The internal space-time symmetry and simple supersymmetry of relativistic particles are briefly discussed in terms of the little group of the Poincare group. The little group generators in a finite-dimensional matrix representation of the N = 1 super-Poincare algebra are explicitly constructed. The supergeometry of a massive case continuously becomes that of a massless case in the infinite-momentum limit. The origin of the gage transformations associated with the massless supermultiplets becomes transparent in that limit.

Ketov, S. V.↗
DOE OSTI2024

Full phase space resonant anomaly detection

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Foundation model framework for all tasks involving jet physics

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the omnilearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the omnilearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over 1 × 10 9 jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate omnilearned with three representative tasks: top-quark jet tagging with the community delphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, omnilearned is the state of the art, further expanding the discovery potential of past, current, and future collider experiments.

Bhimji, Wahid [Lawrence Berkeley National Laborato↗
DOE OSTI2020

Comparison of Eulerian Bin and Lagrangian Particle-Based Schemes in Simulations of Pi Chamber Dynamics and Microphysics

This paper presents a comparison of simulations applying either a traditional Eulerian bin microphysics or a novel particle-based Lagrangian approach to represent CCN activation and cloud droplet growth. The Eulerian microphysics solve the evolution equation for the spectral density function, whereas the Lagrangian approach follows computational particles referred to as superdroplets. Each superdroplet represents a multiplicity of natural droplets that makes the Lagrangian approach computationally feasible. The two schemes apply identical representation of CCN activation and use the same droplet growth equation; these make direct comparison between the two schemes practical. The comparison, the first of its kind, applies an idealized simulation setup motivated by laboratory experiments with the Pi Chamber and previous model simulations of the Pi Chamber dynamics and microphysics. The Pi Chamber laboratory apparatus considers interactions between turbulence, CCN activation, and cloud droplet growth in moist Rayleigh–Bénard convection. Simulated steady-state droplet spectra averaged over the entire chamber are similar, with the mean droplet concentration, mean radius, and spectral width close in Eulerian and Lagrangian simulations. Small differences that do exist are explained by the inherent differences between the two schemes and their numerical implementation. The local droplet spectra differ substantially, again in agreement with the inherent limitations of the theoretical foundation behind each approach. There is a general agreement between simulations and Pi Chamber observations, with simplifications of the CCN activation and droplet growth equation used in the simulations likely explaining specific differences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Binary collision rates of relativistic thermal plasmas. II - Spectra

Spectra of importance for the analysis of relativistic thermal plasmas are numerically calculated assuming a thermal form for the particle distribution functions. Complete sets of optically thin thermal electron-proton, electron-electron, and electron-positron bremsstrahlung spectra are calculated throughout the transrelativistic regime of electron temperatures and compared with approximate expressions for the spectra in the nonrelativistic and extreme relativistic regimes of temperature. A method for calculating accurate secondary particle production spectra in proton-proton collisions from threshold to the highest energies is presented based on an isobaric model near threshold and scaling representations at high energies. The production spectra of charged and neutral pions resulting from proton-proton collisions in relativistic proton plasmas are calculated, and the resultant electron, positron, and gamma-ray spectra from the decay of secondary pions are presented.

Dermer, C. D.↗