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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Path integral molecular dynamics: A high-fidelity approach to quantum dynamics of electrons

We investigate electron transport in the uniform electron gas using ring-polymer molecular dynamics (RPMD). Working in the weakly coupled, non-degenerate regime, we use RPMD to probe how the onset of quantum diffraction effects at high temperature reshapes electron–electron collisions and leads to a classical-to-quantum crossover in macroscopic transport properties. Static thermodynamics obtained with RPMD are consistent with the weak-coupling equation of state, confirming correct quantum Boltzmann sampling. Real-time transport extracted from mean square displacements exhibits the expected ballistic-to-diffusive transition and a systematic reduction of the electronic self-diffusivity as quantum effects strengthen, due to quantum diffraction modifying electron–electron collisions. Direct ring-polymer scattering simulations reveal diffractive “softening” of binary deflections, providing a micro-to-macro link between collision physics and diffusion. The present study establishes RPMD as a quantitative, trajectory-based tool for electron transport across the classical–quantum crossover and furnishes benchmarks for improving Coulomb-log interpolation models. We outline extensions to multi-component plasmas and a path to incorporate Fermi–Dirac statistics within path-integral dynamics.

Electronic transport↗
DOE OSTI2026

Locally purified maximally mixed states at scale: Entanglement pruning and symmetries

Locally Purified Density Operators (LPDOs) are state-of-the-art tensor network ansatze candidates that efficiently represent mixed quantum states at scale. However, given their non-uniqueness, their representational complexity is generally sub-optimal in practical computations. Here, in this work we perform a comprehensive numerical and analytical analysis and resolve this issue in the experimentally relevant limit where noise depolarizes the density operator into a maximally mixed state. To resolve the sub-optimality issue, we analyze two numerical tools, one analytic method, and detail the relations between them. The numerical tools used are fidelity-preserving truncations and isometric gauge transformations leveraging Riemannian optimizations over entropic objective functions. In addition, by invoking the injectivity and symmetry constraints of the maximally mixed LPDO, we also present analytical closed-form expressions for the disentangler and discuss their relation to numerical optimizers. Further, away from the maximally mixed state, our simulations highlight how the truncation threshold smoothly interpolate, as a function of depolarization, between established matrix product results and our new results. Our work shows how, by minimizing the resources required to represent key states of practical interest in experiment, the efficiency of tensor network algorithms can be substantially increased. This paves the path for uncovering tensor network’s fundamental scalability limits and latent potential in representing the wide locus of mixed quantum states that are accessible on near-term quantum devices.

Gangapuram, Amit Jamadagni [Oak Ridge National Lab↗
DOE OSTI2015

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Estimating biotic integrity to capture existence value of freshwater ecosystems

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate benefits of proposed Clean Water Act regulations. The WQI is relevant to human use value, such as recreation, but may not fully capture aspects of nonuse value, such as existence value. Here, we identify an index of biological integrity to supplement the WQI in a forthcoming national stated preference survey that seeks to capture existence value of streams and lakes more accurately within the conterminous United States (CONUS). We used literature and focus group research to evaluate aquatic indices regularly reported by the EPA’s National Aquatic Resource Surveys. We chose an index that quantifies loss in biodiversity as the observed-to-expected (O/E) ratio of taxonomic composition because focus group participants easily understood its meaning and the environmental changes that would result in incremental improvements. However, available datasets of this index do not provide the spatial coverage to account for how conditions near survey respondents affect their willingness to pay for its improvement. Therefore, we modeled and interpolated the values of this index from sampled sites to 1.1 million stream segments and 297,071 lakes across the CONUS to provide the required coverage. The models explained 13 to 36% of the variation in O/E scores and demonstrate how modeling can provide data at the required density for benefits estimation. We close by discussing future work to improve performance of the models and to link biological condition with water quality and habitat models that will allow us to forecast changes resulting from regulatory options.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Degenerate domain walls in supersymmetric theories

In supersymmetric Yang–Mills theories tension-degenerate domain walls are typical. Adding matter fields in fundamental representation, we arrive at supersymmetric quantum chromodynamics (SQCD) supporting similar walls. We demonstrate that the degenerate domain walls can belong to one of two classes: i) locally distinguishable, i.e. those which differ from each other locally (which could be detected in local measurements); and ii) those which have identical local structure and are differentiated only topologically, through a judicially chosen compactification of R 4 . Depending on the number of flavors F and the pattern of Higgsing, both classes can coexist among SQCD k walls interpolating between the vacua n and n + k . We prove that the overall multiplicity of the domain walls obtained after accounting for both classes is ν N , k walls = N ! / [ ( N − k ) ! k ! ] , as was discovered previously in limiting cases. (Here, N is the number of colors.) Thus, ν N , k walls is a peculiar index. For the locally distinguishable degenerate domain walls, we observe two-wall junctions, a phenomenon specific for supersymmetry with central extensions. This phenomenon does not exist for topological replicas.

Chen, Shi↗
DOE OSTI2018

Upgrading Limiting Peak-Power Analysis Techniques with Modern Validation and Uncertainty Quantification for the Advanced Test Reactor

Here, this work demonstrates the acceptability of the 2D deterministic transport code, HELIOS, to replace the legacy diffusion code, PDQ, for computing the peak-power performance parameters of the Advanced Test Reactor (ATR). The 95% Confidence Rule, commonly used in the commercial reactor sector, is explored to develop the so-called “reliability factors” which provide statistical confidence that the peak-power limits within the hottest location along a fuel plate, referred to as the hot-stripe, will not be exceeded. Additionally, an alternative “legacy” methodology was explored that attempts to mimic the exact PDQ analysis process used for defining the peak-power limits. The legacy methodology, involves interpolating power between regions at azimuthal boundaries subtending the regions of interest. In order to apply the 95% Confidence Rule, a statistically significant calculation-to-measurement bias must first be established. Unlike the commercial world where thousands of power observations can be collected every cycle using on-line flux mapping instrumentation, the ATR power distribution must be measured during “depressurized” zero-power measurements using fission wires in polyethylene wands. In 2012, fission wire activation data was collected during a flux run in the Advanced Test Reactor – Critical facility. Also to improve statistical validity, archival data from ATR zero power flux runs from 1977, 1986, and 1994 were digitized from scanned reports and used to create new benchmark models. Borrowing from least-squares adjustment methods commonly used for neutron activation spectroscopy, adjusted fission wire powers were calculated for all four datasets. The mean and standard deviation of the bias between a priori calculated and adjusted wire-powers was then taken as the bias and uncertainty used in the 95% Confidence Rule.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition for Reduced-Order Modeling of S n Radiation Transport

Deterministic solutions to the Sn radiation transport equation can be computationally expensive to calculate. Reduced-order modeling enables efficient approximation of the full-order model (FOM) solution. We propose a novel method for constructing reduced-order models (ROMs) of the S n radiation transport equation, offline maximizing minimally invasive (OMMI) proper orthogonal decomposition (POD). POD uses the method of snapshots to create a reduced-order basis for constructing an ROM. Minimally invasive POD leverages the sweep infrastructure existing in deterministic transport codes to create a POD-based ROM, even when infeasible by traditional methods. Offline maximizing minimally invasive proper orthogonal decomposition (OMMI-POD) extends minimally invasive POD by performing sweeps offline, therefore maximizing the potential speedup. OMMI-POD does so by creating a library of reduced systems from a training set. This library of reduced systems is then interpolated to provide a rapid approximate solution of the S n radiation transport equation. The model is evaluated on a set of test problems, achieving a low error with a 466 times speedup over the FOM. Also presented is a study of the effect of sampling method on the performance of OMMI-POD, specifically comparing naive uniform sampling to the more accurate and computationally expensive greedy sampling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Improved learning from data competitions through strategic design of training and test data sets

Leveraging the depth and breadth of solutions generated through crowdsourcing can be a powerful accelerator to method development for high consequence problems. While data science competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the general underlying problem of interest to the host. This paper outlines important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition. These include: precisely defining the scope of the problem, encouraging participation by competitors from diverse technical backgrounds, specifying the most interesting solution space in order to encourage improvement and distinguish between competitors, strategically generating data sets that enable testing for interpolation and extrapolation to new scenarios of interest, leveraging design of experiment principles for strategic data design while preventing unintentional artifacts in the competition data set that an algorithm could exploit without addressing the real problem of interest, and carefully designing the leaderboard scoring metric to select top solutions that exactly match the overall competition goals. The methods are illustrated with a recently completed competition in the context of urban radiological search to evaluate algorithms capable of detecting, identifying, and locating radioactive materials. Simulated data were used in the urban search competition. Ideas for using measured real data in competitions are also suggested

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Topological defects and topological materials

We report that defects in materials that interpolate between two orientation states such as domain walls and those which relate to some kind of material discontinuity, e.g. dislocations and disclinations, comprise topological defects. Noncoplanar vector arrangements, e.g. vortices, skyrmions, and possibly monopole-like excitations, also belong to this family of defects. Presence of such defects affects many of the macroscopic properties of materials, for example electronic transport and strength. Materials in which topological aspects, e.g. edge or surface states, affect the electronic, transport and other properties can be described as topological materials. Topological insulators, topological superconductors and certain Dirac materials, e.g. graphene and Weyl semimetals, belong to this class of materials. Lastly, these materials possess unusual functionalities potentially useful in a variety of information processing and quantum computation based applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep learning‑based metal artefact reduction in X-ray computed tomography of TRISO fuel compacts

TRISO (TRi-structural ISOtropic) – compact-type micro-particle fuels – are next generation nuclear fuel compacts designed with safety in mind. Structural integrity and characterisation before and after irradiation are important to determine the performance of the fuel compacts under real reactor conditions. X-ray computed tomography can be an important tool for non-destructive evaluation of these fuel compacts. The fuel particles are highly attenuating for X-rays which creates metal artefact, rendering the images unusable. Our artefact correction can mitigate these artefacts significantly. The proposed method works by first segmenting highly-attenuating structures (metal) and forward projecting to localise the source of artefacts in the projection domain, then, using a traditional or U-net-based deep learning architecture, contextually interpolating those regions to remove the artefacts. Finally the reconstruction from the modified projection is fused with the segmented metal. The proposed method shows a significant improvement in the image quality while significantly reducing the reconstruction time compared to the standard technique.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2019

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. For example, the availability of high-resolution geographic data and high-performance computing techniques together with deep learning fuel progress in fast and accurate object detection. Recent examples of GeoAI work include the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification (e.g. LiDAR point clouds), novel methods for spatial interpolation, and advances in traffic forecasting. Similarly, machine learning and natural language processing are facilitating the extraction of geographic information from unstructured (textual) data, such as news articles and Wikipedia as well as the matching of natural features in multiple gazetteers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

PANCHROMATIC HUBBLE ANDROMEDA TREASURY. XII. MAPPING STELLAR METALLICITY DISTRIBUTIONS IN M31

We present a study of spatial variations in the metallicity of old red giant branch stars in the Andromeda galaxy. Photometric metallicity estimates are derived by interpolating isochrones for over seven million stars in the Panchromatic Hubble Andromeda Treasury (PHAT) survey. This is the first systematic study of stellar metallicities over the inner 20 kpc of Andromeda’s galactic disk. We see a clear metallicity gradient of −0.020 ± 0.004 dex kpc{sup −1} from ∼4–20 kpc assuming a constant red giant branch age. This metallicity gradient is derived after correcting for the effects of photometric bias and completeness and dust extinction, and is quite insensitive to these effects. The unknown age gradient in M31's disk creates the dominant systematic uncertainty in our derived metallicity gradient. However, spectroscopic analyses of galaxies similar to M31 show that they typically have small age gradients that make this systematic error comparable to the 1σ error on our metallicity gradient measurement. In addition to the metallicity gradient, we observe an asymmetric local enhancement in metallicity at radii of 3–6 kpc that appears to be associated with Andromeda’s elongated bar. This same region also appears to have an enhanced stellar density and velocity dispersion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

TTVFast: An efficient and accurate code for transit timing inversion problems

Transit timing variations (TTVs) have proven to be a powerful technique for confirming Kepler planet candidates, for detecting non-transiting planets, and for constraining the masses and orbital elements of multi-planet systems. These TTV applications often require the numerical integration of orbits for computation of transit times (as well as impact parameters and durations); frequently tens of millions to billions of simulations are required when running statistical analyses of the planetary system properties. We have created a fast code for transit timing computation, TTVFast, which uses a symplectic integrator with a Keplerian interpolator for the calculation of transit times. The speed comes at the expense of accuracy in the calculated times, but the accuracy lost is largely unnecessary, as transit times do not need to be calculated to accuracies significantly smaller than the measurement uncertainties on the times. The time step can be tuned to give sufficient precision for any particular system. We find a speed-up of at least an order of magnitude relative to dynamical integrations with high precision using a Bulirsch-Stoer integrator.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Flexible and scalable methods for quantifying stochastic variability in the era of massive time-domain astronomical data sets

We present the use of continuous-time autoregressive moving average (CARMA) models as a method for estimating the variability features of a light curve, and in particular its power spectral density (PSD). CARMA models fully account for irregular sampling and measurement errors, making them valuable for quantifying variability, forecasting and interpolating light curves, and variability-based classification. We show that the PSD of a CARMA model can be expressed as a sum of Lorentzian functions, which makes them extremely flexible and able to model a broad range of PSDs. We present the likelihood function for light curves sampled from CARMA processes, placing them on a statistically rigorous foundation, and we present a Bayesian method to infer the probability distribution of the PSD given the measured light curve. Because calculation of the likelihood function scales linearly with the number of data points, CARMA modeling scales to current and future massive time-domain data sets. We conclude by applying our CARMA modeling approach to light curves for an X-ray binary, two active galactic nuclei, a long-period variable star, and an RR Lyrae star in order to illustrate their use, applicability, and interpretation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The physical parameters of the retired a star HD 185351

We report here an analysis of the physical stellar parameters of the giant star HD 185351 using Kepler short-cadence photometry, optical and near infrared interferometry from CHARA, and high-resolution spectroscopy. Asteroseismic oscillations detected in the Kepler short-cadence photometry combined with an effective temperature calculated from the interferometric angular diameter and bolometric flux yield a mean density ρ{sub *} = 0.0130 ± 0.0003 ρ{sub ☉} and surface gravity log g = 3.280 ± 0.011. Combining the gravity and density we find R {sub *} = 5.35 ± 0.20 R {sub ☉} and M {sub *} = 1.99 ± 0.23 M {sub ☉}. The trigonometric parallax and CHARA angular diameter give a radius R {sub *} = 4.97 ± 0.07 R {sub ☉}. This smaller radius, when combined with the mean stellar density, corresponds to a stellar mass 1.60 ± 0.08 M {sub ☉}, which is smaller than the asteroseismic mass by 1.6σ. We find that a larger mass is supported by the observation of mixed modes in our high-precision photometry, the spacing of which is consistent only for M {sub *} ≳ 1.8 M {sub ☉}. Our various and independent mass measurements can be compared to the mass measured from interpolating the spectroscopic parameters onto stellar evolution models, which yields a model-based mass M {sub *,} {sub model} = 1.87 ± 0.07 M {sub ☉}. This mass agrees well with the asteroseismic value, but is 2.6σ higher than the mass from the combination of asteroseismology and interferometry. The discrepancy motivates future studies with a larger sample of giant stars. However, all of our mass measurements are consistent with HD 185351 having a mass in excess of 1.5 M {sub ☉}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Discovery of a ∼5 day characteristic timescale in the Kepler power spectrum of Zw 229–15

We present time series analyses of the full Kepler data set of Zw 229–15. This Kepler light curve—with a baseline greater than 3 yr, composed of virtually continuous, evenly sampled 30 minute measurements—is unprecedented in its quality and precision. We utilize two methods of power spectral analysis to investigate the optical variability and search for evidence of a bend frequency associated with a characteristic optical variability timescale. Each method yields similar results. The first interpolates across data gaps to use the standard Fourier periodogram. The second, using the CARMA-based time-domain modeling technique of Kelly et al., does not need evenly sampled data. Both methods find excess power at high frequencies that may be due to Kepler instrumental effects. More importantly, both also show strong bends (Δα ∼ 2) at timescales of ∼5 days, a feature similar to those seen in the X-ray power spectral densities of active galactic nuclei (AGNs) but never before in the optical. This observed ∼5 day timescale may be associated with one of several physical processes potentially responsible for the variability. A plausible association could be made with light-crossing dynamical or thermal timescales depending on the assumed value of the accretion disk size and on unobserved disk parameters such as α and H/R. This timescale is not consistent with the viscous timescale, which would be years in a ∼10{sup 7} M {sub ☉} AGN such as Zw 229–15. However, there must be a second bend on long (≳ 1 yr) timescales and that feature could be associated with the viscous timescale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Dynamical modeling of tidal streams

I present a new framework for modeling the dynamics of tidal streams. The framework consists of simple models for the initial action-angle distribution of tidal debris, which can be straightforwardly evolved forward in time. Taking advantage of the essentially one-dimensional nature of tidal streams, the transformation to position-velocity coordinates can be linearized and interpolated near a small number of points along the stream, thus allowing for efficient computations of a stream's properties in observable quantities. I illustrate how to calculate the stream's average location (its 'track') in different coordinate systems, how to quickly estimate the dispersion around its track, and how to draw mock stream data. As a generative model, this framework allows one to compute the full probability distribution function and marginalize over or condition it on certain phase-space dimensions as well as convolve it with observational uncertainties. This will be instrumental in proper data analysis of stream data. In addition to providing a computationally efficient practical tool for modeling the dynamics of tidal streams, the action-angle nature of the framework helps elucidate how the observed width of the stream relates to the velocity dispersion or mass of the progenitor, and how the progenitors of 'orphan' streams could be located. The practical usefulness of the proposed framework crucially depends on the ability to calculate action-angle variables for any orbit in any gravitational potential. A novel method for calculating actions, frequencies, and angles in any static potential using a single orbit integration is described in the Appendix.

79 ASTRONOMY AND ASTROPHYSICS↗