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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Constructing tensor network influence functionals for general quantum dynamics

Here, we describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fracture Network Localization Preceding Catastrophic Failure in Triaxial Compression Experiments on Rocks

We quantify the spatial distribution of fracture networks throughout six in situ X-ray tomography triaxial compression experiments on crystalline rocks at confining stresses of 5–35 MPa in order to quantify how fracture development controls the final macroscopic failure of the rock, a process analogous to those that control geohazards such as earthquakes and landslides. Tracking the proportion of the cumulative volume of fractures with volumes >90th percentile to the total fracture volume, ∑ v 90 / v t o t indicates that the fracture networks tend to increase in localization toward these largest fractures for up to 80% of the applied differential stress. The evolution of this metric also matches the evolution of the Gini coefficient, which measures the deviation of a population from uniformity. These results are consistent with observations of localizing low magnitude seismicity before large earthquakes in southern California. In both this analysis and the present work, phases of delocalization interrupt the general increase in localization preceding catastrophic failure, indicating that delocalization does not necessarily indicate a reduction of seismic hazard. However, the proportion of the maximum fracture volume to the total fracture volume does not increase monotonically. Experiments with higher confining stress tend to experience greater localization. To further quantify localization, we compare the geometry of the largest fractures, with volumes >90th percentile, to the best fit plane through these fractures immediately preceding failure. The r 2 scores and the mean distance of the fractures to the plane indicate greater localization in monzonite than in granite. The smaller mean mineral diameter and lower confining stress in the granite experiments may contribute to this result. Tracking these various metrics of localization reveals a close association between macroscopic yielding and the acceleration of fracture network localization. Near yielding, ∑ v 90 / v t o t and the Gini coefficient increase while the mean distance to the final failure plane decreases. Macroscopic yielding thus occurs when the rate of fracture network localization increases.

58 GEOSCIENCES↗
DOE OSTI2021

Distinctive rheological and temporal viscoelastic behaviour of alkali-activated fly ash/slag pastes: A comparative study with cement paste

This paper presents the results of the investigation on the fundamental differences in the viscoelasticity between alkali-activated fly ash/slag materials (AAMs) and cement pastes. The effects of the precursor and the activator on the rheological behaviour of AAM pastes were studied. Given the specific precursor, the activator viscosity significantly affected the AAM paste viscosity. The high viscous activator increased the plastic viscosity of AAM pastes by 4–8 times higher than that of cement paste and drastically decreased the yield stress due to viscous effects and weak colloidal interactions. Temporal changes in viscoelasticity showed that the negligible colloidal interactions between particles in AAM paste made the system non-percolated until the initial setting. This resulted in very different viscoelastic behaviour compared to the one from the well-percolated network in cement paste. Considering all results obtained in this study, the paper describes the short-term evolution of the AAM paste from the fresh condition to the initial setting.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The landscape of disc outflows from black hole–neutron star mergers

ABSTRACT We investigate mass ejection from accretion discs formed in mergers of black holes (BHs) and neutron stars (NSs). The third observing run of the LIGO/Virgo interferometers provided BH–NS candidate events that yielded no electromagnetic (EM) counterparts. The broad range of disc configurations expected from BH–NS mergers motivates a thorough exploration of parameter space to improve EM signal predictions. Here we conduct 27 high-resolution, axisymmetric, long-term hydrodynamic simulations of the viscous evolution of BH accretion discs that include neutrino emission/absorption effects and post-processing with a nuclear reaction network. In the absence of magnetic fields, these simulations provide a lower limit to the fraction of the initial disc mass ejected. We find a nearly linear inverse dependence of this fraction on disc compactness (BH mass over initial disc radius). The dependence is related to the fraction of the disc mass accreted before the ouflow is launched, which depends on the disc position relative to the innermost stable circular orbit. We also characterize a trend of decreasing ejected fraction and decreasing lanthanide/actinide content with increasing disc mass at fixed BH mass. This trend results from a longer time to reach weak freezout and an increasingly dominant role of neutrino absorption at higher disc masses. We estimate the radioactive luminosity from the disc outflow alone available to power kilonovae over the range of configurations studied, finding a spread of two orders of magnitude. For most of the BH–NS parameter space, the disc outflow contribution is well below the kilonova mass upper limits for GW190814.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

New high-resolution rocket-ultraviolet filtergrams of the solar disc

A rocket-borne solar ultraviolet telescope named Transition Region Camera was launched successfully for the third on July 13, 1982. High quality calibrated photographic images of the sun were obtained at Lyman alpha and in the continuum at 160 nm and 220 nm. The angular resolution achieved is better than one arcsec. A flare, active regions, sunspots, the 8 Mm mesostructure, the chromospheric network, bright UV grains and coronal loops were observed during the flight. The results are presented and the evolution with height in the solar atmosphere of the various structures observed is followed from one wavelength to the other, showing distinct differences. The value of the field's intensity of magnetic flux tubes is deduced from the observations.

Foing, B.↗
NASA NTRS1994

Telecommunications issues of intelligent database management for ground processing systems in the EOS era

Future NASA earth science missions, including the Earth Observing System (EOS), will be generating vast amounts of data that must be processed and stored at various locations around the world. Here we present a stepwise-refinement of the intelligent database management (IDM) of the distributed active archive center (DAAC - one of seven regionally-located EOSDIS archive sites) architecture, to showcase the telecommunications issues involved. We develop this architecture into a general overall design. We show that the current evolution of protocols is sufficient to support IDM at Gbps rates over large distances. We also show that network design can accommodate a flexible data ingestion storage pipeline and a user extraction and visualization engine, without interference between the two.

Touch, Joseph D.↗
NASA NTRS2010

Transition in Gas Turbine Control System Architecture: Modular, Distributed, and Embedded

Controls systems are an increasingly important component of turbine-engine system technology. However, as engines become more capable, the control system itself becomes ever more constrained by the inherent environmental conditions of the engine; a relationship forced by the continued reliance on commercial electronics technology. A revolutionary change in the architecture of turbine-engine control systems will change this paradigm and result in fully distributed engine control systems. Initially, the revolution will begin with the physical decoupling of the control law processor from the hostile engine environment using a digital communications network and engine-mounted high temperature electronics requiring little or no thermal control. The vision for the evolution of distributed control capability from this initial implementation to fully distributed and embedded control is described in a roadmap and implementation plan. The development of this plan is the result of discussions with government and industry stakeholders

Culley, Dennis↗
DOE OSTI2023

Secure Roaming with 5G

This invited talk in the 2023 6th IEEE 5G Workshop on First Responder and Tactical Networks discusses security improvement introduced by the 3GPP standards to the 5G roaming capabilities over 4G/LTE (Long Term Evolution) standards.

5G↗
DOE OSTI2022

GSAS Tools

SAND2023-06684O GSAS Tools is a web application that manages user access to modeling and simulation tools and promotional material. This software, which is a spiking neural network (SNN) simulator, represents an SNN as a graph of stochastic differential equations and simulates the time-evolution of these equations. It has the capability of reading inputs from file and writing outputs to file, and generally supports experimentation, analysis, and algorithm development using SNNs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Noel, Todd↗
NASA NTRS1987

The structure and evolution of seasonal wind anomalies over the near-equatorial eastern Indian and western Pacific Oceans

The longitude-height-time structure and evolution of near-equatorial wind variability over the eastern Indian and western Pacific Oceans are studied using data obtained from a network of eight radiosonde stations extending from southern India to the central Pacific Ocean. The seasonal zonal wind anomalies observed at the cross section beween Trivandrum and Majuro stations are analyzed using an empirical orthogonal function. The Walker Circulation fluctuations are described in terms of standing oscillations in the longitude-height plane, and it is determined that Southern Oscillation propagation anomaly best represents the wind fluctuations. A complex empirical orthogonal function (CEOF) analysis and an El Nino compositing methodology are applied to the seasonal zonal wind anomalies. It is determined that the composite El Nino anomalies correspond to the spatial structure and temporal evolution of anomalies implied by the CEOF analysis.

Gutzler, David S.↗
DOE OSTI2026

Universal progression of structure and dynamics in colloidal nanocrystal gels during salt-accelerated aging

Controlling the structure and function of colloidal gels requires a detailed understanding of how the various components govern network formation and aging. In particular, molecular additives like salts are widely used to tune interparticle interactions, yet their influence on gelation pathways in complex systems such as colloidal nanocrystal gels remains inadequately understood. Here, we investigate how noncoordinating salts modulate the evolution of gels formed using chemically linked tin-doped indium oxide nanocrystals. Through combined structural, dynamic, and kinetic analyses, we demonstrate that increasing salt concentration accelerates gelation. When rescaled by salt-dependent characteristic times, the evolution collapses onto universal trajectories, revealing a time-salt superposition principle. The universality extends across length scales, suggesting a consistent salt-dependent mechanism that controls both local structuring and macroscopic network formation. This observed salt modulation of structure and dynamics provides a predictive basis for controlling the kinetics of nonequilibrium nanocrystal gel assembly, enhancing the rational design of functional nanomaterials with tunable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Physics-aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower-dimensional feature space. We build upon our previous work on physics-aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure-aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower-dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data-driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale.

Mathematics and Computing↗
DOE OSTI2026

Modeling the contributions to acoustic nonlinearity from complex dislocation networks using 3D dislocation dynamics

Nonlinear ultrasonic parameters are highly sensitive to microstructural features that affect macroscale material behavior, providing a nondestructive means to characterize their evolution. Although dislocations are known to be a strong source of acoustic nonlinearity, establishing quantitative links between the acoustic nonlinearity parameter (β), measured via Second Harmonic Generation, and dislocation morphology—such as dislocation length and density—remains an open challenge. This work advances the numerical modeling of dislocation–β relationships using 3D dislocation dynamics (DD) simulations in two approaches: a “static” method computing strain and stress fields from dislocation configurations in the absence of external loading, and a “quasi-static” method to estimate β from the curvature of dislocation lines under applied load. First, the static method is combined with finite element analysis to investigate a recent assertion that heterogeneous initial strain fields can induce higher harmonic generation in a linear elastic medium; the present results do not corroborate this outcome. Then, the quasi-static method is applied to multiple-dislocation scenarios through parametric studies, revealing behaviors not predicted by analytical models, such as the competing interactions of edge and screw dislocations and the significant influence of applied stress on β. Finally, the simulations are used to model SHG experimental results and validate the hypothesis that β can decrease during plastic deformation, despite increasing dislocation density. As the DD code used here is open-source, it provides a practical platform for future investigation into microstructure–β relationships important to the interpretation of SHG results.

Materials science↗
DOE OSTI2021

Evolution and Trends of Industrial Control System Cyber Incidents since 2017

The industrial control systems (ICSs) that manage our critical infrastructure are increasingly converging with corporate networks and the Internet as technology and businesses prioritize digital connectivity. These connections make them more vulnerable and available to malicious cyber actors who traditionally targeted the companies’ more public-facing information technology (IT) networks. This paper will review select publicly reported cyber incidents to highlight the continued and growing threat to ICS devices and operational technology (OT) environments. It will summarize the incident and when available, will provide information on the cyber actors, the vulnerabilities they exploited, and any publications the U.S. Government (USG) provided in response. Data belonging to the Department of Homeland Security (DHS) will be used to highlight quantitative trends concerning ICS incidents. This paper builds on “History of Industrial Control System Cyber Incidents” (Hemsley & Fisher 2018), a paper that highlighted select noteworthy threats and incidents to ICS systems up to 2017. This paper will similarly review select incidents occurring after the last previously reviewed incident, Triton/HatMan, December 2017, and will note ICS incident trends including IT/OT convergence and advances in cyber-threat actors’ capabilities in observed in the examined incidents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Irradiation-Induced Structural Disorder and Its Influence on the Mechanical Response of Polycrystalline MoS2

Molybdenum disulfide (MoS2) thin films are widely used as dry-film lubricants and protective coatings in aerospace and other radiation-exposed environments. Conventional synthesis routes produce polycrystalline films whose grain boundaries and other native defects cause their mechanical and tribological behavior to differ substantially from that of ideal single crystals. Under irradiation, these films progressively evolve from polycrystalline structures, composed of layered MoS2 grains, into highly disordered and eventually amorphous structures, altering both their tribological performance and mechanical integrity. Here, we employ reactive atomistic simulations to investigate irradiation-driven structural evolution in bulk polycrystalline MoS2. Using controlled primary knock-on atom (PKA) events, we characterize the progressive transition from a polycrystalline microstructure to an amorphous network by tracking defect accumulation and structural disorder. We then establish how this transition modifies the dominant deformation mechanisms and the temperature-dependent tensile response. Specifically, irradiation suppresses interlayer sliding and delamination, mechanisms which facilitate the deformation of the pristine polycrystal, resulting in defect-induced hardening. Broadly, our results establish direct process–structure–property relationships linking irradiation-induced defect accumulation, microstructural evolution, deformation mechanisms, and mechanical behavior, providing an atomistic framework for understanding the structural integrity and long-term reliability of irradiated MoS2 coatings.

Moore, Daniel [Sandia National Laboratories (SNL)]↗
DOE OSTI2024

Structural evolution of liquid silicates under conditions in Super-Earth interiors

Molten silicates at depth are crucial for planetary evolution, yet their local structure and physical properties under extreme conditions remain elusive due to experimental challenges. In this study, we utilize in situ X-ray diffraction (XRD) at the Matter in Extreme Conditions (MEC) end-station of the Linear Coherent Linac Source (LCLS) at SLAC National Accelerator Laboratory to investigate liquid silicates. Using an ultrabright X-ray source and a high-power optical laser, we probed the local atomic arrangement of shock-compressed liquid (Mg,Fe)SiO 3 with varying Fe content, at pressures from 81(9) to 385(40) GPa. We compared these findings to ab initio molecular dynamics simulations under similar conditions. Results indicate continuous densification of theO-O and Mg-Si networks beyond Earth’s interior pressure range, potentially altering melt properties at extreme conditions. This could have significant implications for early planetary evolution, leading to notable differences in differentiation processes between smaller rocky planets, such as Earth and Venus, and super-Earths, which are exoplanets withmasses nearly three times that of Earth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗