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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Preconditioning of overcast-to-broken cloud transitions by riming in marine cold air outbreaks

Marine cold air outbreaks (CAOs) commonly form overcast cloud decks that transition into broken cloud fields downwind, dramatically altering the local radiation budget. In this study, we investigate the impact of frozen hydrometeors on these transitions. We focus on a CAO case in the NW Atlantic, the location of the multi-year flight campaign ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment). We use MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2) reanalysis fields to drive large eddy simulations with mixed-phase two-moment microphysics in a Lagrangian framework. We find that transitions are triggered by substantial rain (rainwater paths >25 g m−2), and only simulations that allow for aerosol depletion result in sustained breakups, as observed. Using a range of diagnostic ice nucleating particle concentrations, Ninp, we find that increasing ice progressively accelerates transitions, thus abbreviating the overcast state. Ice particles affect the cloud-topped boundary layer evolution, primarily through riming-related processes prior to substantial rain, leading to (1) a reduction in cloud liquid water, (2) early consumption of cloud condensation nuclei, and (3) early and light precipitation cooling and moistening below cloud. We refer to these three effects collectively as “preconditioning by riming”. Greater boundary layer aerosol concentrations available as cloud condensation nuclei (CCN) delay the onset of substantial rain. However, cloud breakup and low CCN concentration final stages are found to be inevitable in this case, due, primarily, to liquid water path buildup. An ice-modulated cloud transition speed suggests the possibility of a negative cloud–climate feedback. To address prevailing uncertainties in the model representation of mixed-phase processes, the magnitude of ice formation and riming impacts and, thereby, the strength of an associated negative cloud–climate feedback process, requires further observational evaluation by targeting riming hot spots with in situ imaging probes that allow for both the characterization of ice particles and abundance of supercooled droplets.

Marine cold air outbreaks↗
NASA NTRS1995

Three-dimensional ring current decay model

This work is an extension of a previous ring current decay model. In the previous work, a two-dimensional kinetic model was constructed to study the temporal variations of the equatorially mirroring ring current ions, considering charge exchange and Coulomb drag losses along drift paths in a magnetic dipole field. In this work, particles with arbitrary pitch angle are considered. By bounce averaging the kinetic equation of the phase space density, information along magnetic field lines can be inferred from the equator. The three-dimensional model is used to simulate the recovery phase of a model great magnetic storm, similar to that which occurred in early February 1986. The initial distribution of ring current ions (at the minimum Dst) is extrapolated to all local times from AMPTE/CCE spacecraft observations on the dawnside and duskside of the inner magnetosphere spanning the L value range L = 2.25 to 6.75. Observations by AMPTE/CCE of ring current distributions over subsequent orbits during the storm recovery phase are compared to model outputs. In general, the calculated ion fluxes are consistent with observations, except for H(+) fluxes at tens of keV, which are always overestimated. A newly invented visualization idea, designated as a chromogram, is used to display the spatial and energy dependence of the ring current ion differential flux. Important features of storm time ring current, such as day-night asymmetry during injection and drift hole on the dayside at low energies (less than 10 keV), are manifested in the chromogram representation. The pitch angle distribution is well fit by the function, J(sub o)(1 + Ay(sup n)), where y is sine of the equatorial pitch angle. The evolution of the index n is a combined effect of charge exchange loss and particle drift. At low energies (less than 30 keV), both drift dispersion and charge exchange are important in determining n.

Fok, Mei-Ching↗
NASA NTRS1994

A three-dimensional ring current decay model

This work is an extension of a previous ring current decay model. In the previous work, a two-dimensional kinetic model was constructed to study the temporal variations of the equatorially mirroring ring current ions, considering charge exchange and Coulomb drag losses along drift paths in a magnetic dipole field. In this work, particles with arbitrary pitch angle are considered. By bounce averaging the kinetic equation of the phase space density, information along magnetic field lines can be inferred from the equator. The three-dimensional model is used to simulate the recovery phase of a model great magnetic storm, similar to that which occurred in early February 1986. The initial distribution of ring current ions (at the minimum Dst) is extrapolated to all local times from AMPTE/CCE spacecraft observations on the dawn and dusk sides of the inner magnetosphere spanning the L value range L = 2.25 to 6.75. Observations by AMPTE/CCE of ring current distributions over subsequent orbits during the storm recovery phase are compared to model outputs. In general, the calculated ion fluxes are consistent with observations, except for H+ fluxes at tens of keV, which are always over-estimated. A newly-invented visualization idea, designated as a chromogram, is used to display the spatial and energy dependence of the ring current ion differential flux. Important features of storm-time ring current, such as day-night asymmetry during injection and drift hole on the dayside at low energies (less than 10 keV), are manifested in the chromogram representation. The pitch angle distribution is well fit by the function, j(sub o)(1+Ay(exp n)), where y is sine of the equatorial pitch angle. The evolution of the index n is a combined effect of charge exchange loss and particle drift. At low energies (less than 30 keV), both drift dispersion and charge exchange are important in determining n.

Fok, Mei-Ching↗
NASA NTRS1979

Direct in situ measurements of thermospheric temperature

The open source neutral mass spectrometer on the Atmosphere Explorer satellites used for direct in situ measurements of the neutral gas temperature by means of the 'fly-through' mode of operation is evaluated. The derived neutral temperature (Tn) is compared with ion temperatures (T1) obtained simultaneously from the on-board retarding potential analyzer for altitudes and conditions where the two temperatures should be equal. A statistical analysis showed consistency between concurrently observed values of Tn and T1, also shown through profiles depicting their altitude distributions between 150 and 225 km. The overall magnitude of temperatures calculated from the Jacchia (1971) model results in a better representation of the observations than the higher temperatures predicted for this region by the MSIS model (Hedin, 1977), and agreement is also found between observed temperatures and neutral temperatures derived from altitude distributions of N2 particle densities.

Kayser, D. C.↗
NASA NTRS1980

A model for dust cloud autoignition

The paper deals with the problem of autoignition of a cloud of solid particles uniformly dispersed in a gas with one component of which it can react exothermally. Energy equations are derived similar to those of Rumanov and Khaikin (1969) but using methods of a phase-plane representation to describe ignition conditions resulting from these equations.

Krishna, C. R.↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Five-point functions and the permutation group 𝑆 5

Five-point functions and five-body wave functions play an important role in many areas of nuclear and particle physics, e.g., in 2 →3 scattering processes, in the five-gluon vertex, or in the study of pentaquarks. In this work we consider the permutation group 𝑆 5 to facilitate the description of such objects. We work out the multiplets transforming under irreducible representations of 𝑆 5 and provide compact formulas allowing one to cast the permutations of an object 𝑓 12345 into combinations with definite permutation symmetry. We also give the explicit expressions for the irreducible multiplet products. We consider several practical applications as examples: We arrange the four-momenta and Lorentz invariants of a five-point function into the multiplet structure, we work out the color tensors of the five-gluon vertex in the multiplet notation, and we discuss applications for five-body wave functions like those of pentaquarks.

Bethe-Salpeter equation↗
DOE OSTI2025

A review of low-rank methods for time-dependent kinetic simulations

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six–dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An examination of different representations of the electrostatic field and potential of an intense relativistic electron beam in a cylindrical conductor and their implications on foil focusing of an elliptical beam

Knowledge of the electrostatic field and potential of an intense relativistic electron beam (IREB) in a cylindrical conductor is essential for understanding the transport of IREBs including the interaction of a grounded conducting foil with the beam. Different representations of the electrostatic field or potential exist in the literature. The different representations are compared and an extension to foil focusing of elliptical beams is discussed.

43 PARTICLE ACCELERATORS↗
NASA NTRS1996

Structure of the Magnetotail Current Sheet

An orbit tracing technique was used to generate current sheets for three magnetotail models. Groups of ions were followed to calculate the resulting cross-tail current. Several groups then were combined to produce a current sheet. The goal is a model in which the ions and associated electrons carry the electric current distribution needed to generate the magnetic field B in which ion orbits were traced. The region -20 R(E) less than x less than -14 R(E) in geocentric solar magnetospheric coordinates was studied. Emphasis was placed on identifying the categories of ion orbits which contribute most to the cross-tail current and on gaining physical insight into the manner by which the ions carry the observed current distribution. Ions that were trapped near z = 0, ions that magnetically mirrored throughout the current sheet, and ions that mirrored near the Earth all were needed. The current sheet structure was determined primarily by ion magnetization currents. Electrons of the observed energies carried relatively little cross-tail current in these quiet time current sheets. Distribution functions were generated and integrated to evaluate fluid parameters. An earlier model in which B depended only on z produced a consistent current sheet, but it did not provide a realistic representation of the Earth's middle magnetotail. In the present study, B changed substantially in the x and z directions but only weakly in the y direction within our region of interest. Plasmas with three characteristic particle energies were used with each of the magnetic field models. A plasma was found for each model in which the density, average energy, cross-tail current, and bulk flow velocity agreed well with satellite observations.

Larson, Douglas J.↗
NASA NTRS1996

Structure of the Magnetotail Current Sheet

An orbit tracing technique was used to generate current sheets for three magnetotail models. Groups of ions were followed to calculate the resulting cross-tail current. Several groups then were combined to produce a current sheet. The goal is a model in which the ions and associated electrons carry the electric current distribution needed to generate the magnetic field B in which ion orbits were traced. The region -20 R(sub E) less than x less than - 14 R(sub E) in geocentric solar magnetospheric coordinates was studied. Emphasis was placed on identifying the categories of ion orbits which contribute most to the cross-tail current and on gaining physical insight into the manner by which the ions carry the observed current distribution. Ions that were trapped near z = 0, ions that magnetically mirrored throughout the current sheet, and ions that mirrored near the Earth all were needed. The current sheet structure was determined primarily by ion magnetization currents. Electrons of the observed energies carried relatively little cross-tail current in these quiet time current sheets. Distribution functions were generated and integrated to evaluate fluid parameters. An earlier model in which B depended only on z produced a consistent current sheet, but it did not provide a realistic representation of the Earth's middle magnetotail. In the present study, B changed substantially in the x and z directions but only weakly in the y direction within our region of interest. Plasmas with three characteristic particle energies were used with each of the magnetic field models. A plasma was found for each model in which the density, average energy, cross-tail current, and bulk flow velocity agreed well with satellite observations.

Larson, Douglas J.↗
DOE OSTI2023

Vertical distribution of boundary layer new particle formation and implications for nanoparticle growth mechanisms (Final Report)

New particles can form high above ground in the atmosphere before such formation events are observed by ground-based instruments. Once formed, these particles can take up significant amounts of water vapor, which has implications on the mechanisms by which nanoparticles grow and, ultimately, impact cloud formation processes. These observations motivated the research performed in this project using a combination of modeling, laboratory experiments, and the analysis of field observations. We hypothesized is that ground-based measurements do not always accurately represent the location and timing of new particle formation events, nor do they adequately characterize the dominant physical and chemical processes that are responsible for the subsequent growth of newly formed particles. Essentially, we wanted to study the new particle formation process under conditions that we expect may be more relevant to the actual location in the atmosphere where such events take place, which are often characterized by higher relative humidities, lower temperatures, and a broader range of precursors and oxidants compared to those observed at ground level. This project had two main objectives, each of which directly addressed our hypothesis. Our first is to investigate the timing, distribution, meteorological conditions, and nanoparticle properties associated with boundary layer new particle formation through the analysis of ARM field campaigns such as HI-SCALE and long-term observations and observations performed in other locales such as in China and India where atmospheric conditions could play a crucial role in determining the mechanisms of new particle formation. This activity provided a comprehensive description, both in time and in space, of the gases and nanoparticle properties that are responsible for these events. We also performed modeling studies to explore the vertical profile and timing of new particle formation events from these campaigns. Our second objective was to perform laboratory and process-level modeling studies to investigate the role that conditions such as low ambient temperature, high relative humidity, and different oxidants such as nitrate radical play in determining unique chemical pathways for nanoparticle growth. In doing so, we revisited the representation of nanoparticle growth in global models and updated assumptions of formation and growth based on the findings of the field, laboratory, and process-level-model work. Our research provides crucial insights into the most important regions of the atmospheric boundary layer for future observational and modeling studies. It also identifies the most relevant environmental conditions, precursors, and oxidants that are responsible for nanoparticle growth, which will aid in the design of laboratory studies and process-level model experiments. The unique pathways that we discovered can be incorporated into regional and global models, thereby improving predictions and attribution of the role of new particle formation in, e.g., air pollution formation and cloud properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Charged Particle Tracking via Edge-Classifying Interaction Networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high-energy particle physics. In particular, particle tracking data are naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges, given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN’s excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

accelerator physics↗
NASA NTRS2009

Biophysics Representation of the Two-Hit Model of Alzheimer's Disease for the Exploration of Late CNS Risks from Space Radiation

A concern for long-term space travel outside the Earth s magnetic field is the late effects to the central nervous system (CNS) from galactic cosmic ray (GCR) or solar particle events (SPE). Human epidemiology data is severely limited for making CNS risk estimates and it is not clear such effects occur following low LET exposures. We are developing systems biology models based on biological information on specific diseases, and experimental data for proton and heavy ion radiation. A two-hit model of Alzheimer s disease (AD) has been proposed by Zhu et al.(1), which is the framework of our model. Of importance is that over 50% of the US population over the age of 75-y have mild to severe forms of AD. Therefore we recommend that risk assessment for a potential AD risk from space radiation should focus on the projection of an earlier age of onset of AD and the prevention of this possible acceleration through countermeasures. In the two-hit model, oxidative stress and aberrant cell cycle-related abnormalities leading to amyloid-beta plaques and neurofibrillary tangles are necessary and invariant steps in AD. We have formulated a stochastic cell kinetics model of the two-hit AD model. In our model a population of neuronal cells is allowed to undergo renewal through neurogenesis and is susceptible to oxidative stress or cell cycle abnormalities with age-specific accumulation of damage. Baseline rates are fitted to AD population data for specific ages, gender, and for persons with an apolipoprotein 4 allele. We then explore how low LET or heavy ions may increase either of the two-hits or neurogenesis either through persistent oxidative stress, direct mutation, or through changes to the micro-environment, and suggest possible ways to develop accurate quantitative estimates of these processes for predicting AD risks following long-term space travel.

Cucinotta, Francis A.↗
DOE OSTI2025

High-order limiting methods using maximum principle bounds derived from the Boltzmann equation I: Euler equations

The use of limiting methods for high-order numerical approximations of hyperbolic conservation laws generally requires defining an admissible region/bounds for the solution. In this work, we present a novel approach for computing solution bounds and limiting for the Euler equations through the kinetic representation provided by the Boltzmann equation, which allows for extending limiters designed for linear advection directly to the Euler equations. Given an arbitrary set of solution values to compute bounds over (e.g., numerical stencil) and a desired linear advection limiter, the proposed approach yields an analytic expression for the admissible region of particle distribution function values, which may be numerically integrated to yield a set of bounds for the density, momentum, and total energy. Further, these solution bounds are shown to preserve positivity of density/pressure/internal energy and, when paired with a limiting technique, can robustly resolve strong discontinuities while recovering high-order accuracy in smooth regions without any ad hoc corrections (e.g., relaxing the bounds). This approach is demonstrated in the context of an explicit unstructured high-order discontinuous Galerkin/flux reconstruction scheme for a variety of difficult problems in gas dynamics, including cases with extreme shocks and shock-vortex interactions. Furthermore, this work presents a foundation for limiting techniques for more complex macroscopic governing equations that can be derived from an underlying kinetic representation for which admissible solution bounds are not well-understood.

42 ENGINEERING↗
DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Accelerated statistical failure analysis of multifidelity TRISO fuel models

Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Efficient core-excited state orbital perspective on calculating x-ray absorption transitions in determinant framework

X-ray absorption spectroscopy (XAS) is an explicit probe of the unoccupied electronic structure of materials and an invaluable tool for fingerprinting various electronic properties and phenomena. Computational methods capable of simulating and analyzing such spectra are therefore in high demand for complementing experimental results and for extracting valuable insights therefrom. In particular, a recently proposed first-principles approach titled many-body XAS (MBXAS), which approximates the final (initial) state as a Slater determinant constructed from Kohn-Sham (KS) orbitals optimized in the absence (presence) of the relevant core electron has shown promising prospects in evaluating the transition amplitudes. In this article, we show that the MBXAS approach can be rederived using a transition operator expressed entirely in the basis of core-excited state KS orbitals and that this reformulation offers substantial practical and conceptual advantages. In addition to circumventing previous issues of convergence with respect to the number of unoccupied ground-state orbitals, the aforementioned representation reduces the computational expense by rendering the calculation of such orbitals unnecessary altogether. Furthermore, the reformulated approach also provides a direct pathway for comparing the many-body approximation with the so-called single-particle treatment and indicates the relative importance in observed XAS intensity of the relaxation of the valence occupied subspace induced by the core excitation. Finally, using the core-excited state basis, we define auxiliary orbitals for x-ray absorption and demonstrate their utility in explaining the spectral intensity by contrasting them with single-particle approximations to the excited state.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗