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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Interpretable Models for Workflow Differentiation in High-Performance Scientific Networks

Scientific workflows in high-performance networks spawn hundreds of interdependent flows that must be managed collectively—yet existing network classifiers treat each flow in isolation, leading to fragmented QoS decisions and missed interflow patterns. We present a novel traffic classification solution that operates at the workflow level, distinguishing entire filetransfer operations from streaming analytics by capturing how concurrent flows interact and burst together. We introduce a workflow identification window (WIW) that ingests raw packet headers from parallel flows into unified tensors, preserving the spatial-temporal patterns that differentiate scientific workflows. This approach achieves 98.7% accuracy using CNN, LSTM, and hybrid architectures, while maintaining 84% accuracy on production traffic collected a week later—demonstrating robustness to temporal drift. By integrating SHAP and GradCAM explainability, we reveal that early-packet timing patterns and cross-flow correlations drive classification decisions, providing operators with interpretable insights. Our system enables coherent workflow-level QoS enforcement and dynamic bandwidth allocation in scientific networks, eliminating manual per-flow configuration while maintaining classification latency at millisecond level.

Giannakou, Anna [LBL, Berkeley]↗
DOE OSTI2022

How Many Elements Matter?

Some studies of stars' multielement abundance distributions suggest at least 5–7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We show that both propositions can be, and are, simultaneously true. We adopt a machine-learning technique known as normalizing flow to reconstruct the probability distribution of Milky Way disk stars in the space of 15 elemental abundances measured by APOGEE. Conditioning on T eff and $\mathrm{log}\,g$ minimizes the differential systematics. After further conditioning on [Fe/H] and [Mg/Fe], the residual scatter for most abundances is σ [X/H] ≲ 0.02 dex, consistent with APOGEE's reported statistical uncertainties of ~0.01–0.015 dex and intrinsic scatter of 0.01–0.02 dex. Despite the small scatter, residual abundances display clear correlations between elements, which we show are too large to be explained by measurement uncertainties or by the finite sampling noise. We must condition on at least seven elements to reduce the correlations to a level consistent with the observational uncertainties. Our results demonstrate that cross-element correlations are a much more sensitive probe of a hidden structure than dispersion, and they can be measured precisely in a large sample even if the star-by-star measurement noise is comparable to the intrinsic scatter. We conclude that many elements have an independent story to tell, even for the mundane disk stars and elements produced by the core-collapse and Type Ia supernovae. The only way to learn these lessons is to measure the abundances directly, and not merely infer them.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Measuring short-range correlations and quasi-elastic cross sections in A(e,e’) at x > 1 and modest Q 2

We present results from the Jefferson Lab E08-014 experiment, investigating short-range correlations (SRC) through measurements of inclusive quasi-elastic scattering from 2 H, 3 He, 4 He, 12 C, 40 Ca, and 48 Ca. The kinematics were selected to isolate scattering from SRCs, yielding a plateau in the A/ 2 H cross-section ratios due to the universal two-body structure of the 2N-SRCs in light and heavy nuclei. We observe approximate plateaus in the A/ 2 H ratios and provide the first extractions of the A/ 2 H ratio for 40 Ca and 48 Ca. We also examine the A/ 3 He ratio, aiming to identify three-nucleon SRCs (3N-SRCs). Following the approach for isolating 2N-SRCs, searching for 3N-SRC dominance involved measuring the A/ 3 He cross section ratio at modest-to-large Q 2 values and looking for a plateau ratios for x ≳ 2.5. This was not observed in the data, and in fact increasing Q 2 values moved the data further away from the predicted plateau. We show here that, when analyzed in terms of the struck nucleon’s light-cone momentum, the data exhibited the expected trend, progressively approaching the predicted 3N-SRC plateau. These observations suggest that future measurements at higher energies may facilitate a definitive isolation and identification of 3N-SRCs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Coupled Boltzmann transport equations of heavy quarks and quarkonia in quark-gluon plasma

We develop a framework of coupled transport equations for open heavy flavor and quarkonium states, in order to describe their transport inside the quark-gluon plasma. Our framework is capable of studying simultaneously both open and hidden heavy flavor observables in heavy-ion collision experiments and can account for both, uncorrelated and correlated recombination. Our recombination implementation depends on real-time open heavy quark and antiquark distributions. We carry out consistency tests to show how the interplay among open heavy flavor transport, quarkonium dissociation and recombination drives the system to equilibrium. We then apply our framework to study bottomonium production in heavy-ion collisions. We include Υ(1S), Υ(2S), Υ(3S), χb (1P) and χb (2P) in the framework and take feed-down contributions during the hadronic gas stage into account. Cold nuclear matter effects are included by using nuclear parton distribution functions for the initial primordial heavy flavor production. A calibrated 2 + 1 dimensional viscous hydrodynamics is used to describe the bulk QCD medium. We calculate both the nuclear modification factor R AA of all bottomonia states and the azimuthal angular anisotropy coefficient v 2 of the Υ(1S) state and find that our results agree reasonably with experimental measurements. Our calculations indicate that correlated cross-talk recombination is an important production mechanism of bottomonium in current heavy-ion experiments. The importance of correlated recombination can be tested experimentally by measuring the ratio of R AA ( χb (1P)) and R AA (Υ(2S)).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Design of a correlation reflectometer radiometer diagnostic and measurements of the electron density–temperature cross-phase angle in the H-mode pedestal with small edge localized modes at ASDEX Upgrade

This work presents the hardware design and first results from a newly commissioned correlation reflectometer radiometer diagnostic that measures the cross-phase angle between electron density and temperature fluctuations in ASDEX Upgrade plasmas. This diagnostic employs cross correlations between signals measured by a tunable, continuous wave, perpendicular incidence, fluctuation reflectometer, and a 24-channel radiometer sharing the same line of sight. Novel measurements in the pedestal of a helium H-mode plasma with small edge localized modes show changes in the cross-phase angle between the electron density and temperature fluctuations from ∼90° to 120°, suggesting changes in the properties of the turbulence driving transport in the plasma edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Data Driven Correlated Noise Simulation for the ICEBERG LArTPC

Accurate electronic-noise simulation is essential for low-energy physics in liquid-argon TPCs. More realistic noise modeling allows us to better tune reconstruction algorithms and more reliably assess and optimize signal-detection thresholds. We present a data-driven noise simulation framework developed for the ICEBERG test stand for DUNE that generates synthetic noise waveforms that reproduce both (i) the measured per-channel magnitude of the Fast Fourier Transform (FFT) and (ii) frequency-dependent channel-to-channel correlations observed in ICEBERG noise data. Using a dedicated noise-only dataset, we build a compact noise model containing per-channel FFT-magnitude targets together with a small set of band-wise cross-wire color matrices. White noise is generated in the frequency domain by drawing circular-symmetric complex Gaussian coefficients with random phases and scaling them to match the measured FFT-magnitude targets, and cross-wire correlations are subsequently imposed using the stored color matrices. The model and algorithm were integrated into the LArSoft + Wire-Cell Toolkit simulation chain and validated by comparing waveform structure, frequency-domain spectra, and band-limited correlation matrices from simulated noise and ICEBERG data. This approach can be extended to other LArTPC operating conditions.

Ghosh, Avik [Iowa State U.]↗
DOE OSTI2026

Molten Halide Salt Surface Tension: Methods and Correlations

Here, this paper reviews various methods for studying surface tension and their applicability to fluoride and chloride molten salt systems, including a comparison of benefits and drawbacks. Such a comparison aids in experiment design based on desired factors such as scale, accuracy, and repeatability. A detailed review is presented for existing literature data regarding the surface tension of molten fluoride and chloride salts. These reference data were compiled and analyzed to determine cross-validated correlation equations for several alkali and alkaline earth fluoride and chloride salts as functions of temperature. These correlations are necessary for reliable multiphysics modeling approaches as well as accurate design and analysis of multiphase molten salt phenomena such as gas sparging and bubble formation/transport. This analysis supports the development of the thermophysical arm of the Molten Salt Thermal Properties Database (MSTDB-TP) managed by Oak Ridge National Laboratory.

Chloride↗
DOE OSTI2022

Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

Abstract Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross-chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Turbulence and E × B flow correlations across the L-H transition in DIII-D deuterium and hydrogen plasmas

The isotope dependence of the low- to high confinement-mode (L- to H-mode) transition power threshold P LH presents significant challenges for the initial (non-nuclear) hydrogen operations phase of the international thermonuclear experimental reactor (ITER). Here, we examine the isotope dependence of turbulence and E × B flow correlation properties in the L-mode edge plasma, leading up to the L-H transition. At marginal auxiliary power (near P LH ), turbulence is initially suppressed periodically during limit cycle oscillations (LCO) that precede the transition to sustained H-mode confinement. Here, we present evidence that the long-range (toroidal) correlation of the E × B edge plasma flow across the LCO phase is much weaker in hydrogen than in deuterium in the DIII-D tokamak, congruent with the higher threshold power P LH required to access LCO and H-mode in hydrogen. Concomitantly, the time required to initially quench edge turbulence via localized edge E × B flow shear is significantly longer in hydrogen (1–1.5 ms) than in deuterium (approx. 100 µs). No toroidal long-range correlation of the turbulence amplitude is observed, in agreement with expectations based on the relatively short poloidal turbulence correlation length. Radial edge turbulence and flow correlation lengths are longer in deuterium than in hydrogen plasmas as one would expect from ‘naïve’ gyro-Bohm isotope transport scaling, despite the substantially higher thermal flux across the last closed flux surface in hydrogen before the L-H transition. This article is part of a discussion meeting issue 'H-mode transition and pedestal studies in fusion plasmas'.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Importance of cross reaction covariance data for user applications

The characterization of the uncertainty in radiation damage metrics presents many challenges. This paper examines the current approaches to characterizing radiation damage metrics such as hydrogen and helium gas production, material heating, trapped charge in microelectronics, and lattice displacement damage. Critical uncertainty aspects go beyond just the material cross sections and involve the consideration of energy-dependent cross reaction correlations, the recoil ion energy spectrum, and models used for the partitioning of the recoil ion energy into various forms of energy deposition. This paper starts with a review of terminology and then examines the current approaches in the characterization of uncertainty in radiation damage metrics for several applications. The major deficiencies in the uncertainty of the damage metric characterization are also identified.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Weighted FFT estimators for 1D and 3D correlations of the Lyman- α forest

Correlations in the Lyman-α (Lyα) forest, both as a function of line of sight separation (1D) and 3D separation, provide a unique window to the distribution of matter at redshifts not accessible by current galaxy surveys. While optimal quadratic estimators have been used to measure 1D correlations, they are computationally expensive and difficult to extend to 3D analyses. On the other hand, estimators based on the Fast Fourier Transform (FFT) are significantly faster, but are affected by missing data in the spectra (masked pixels) and so far have not used pixel weights to reduce the uncertainties in the measurement. In this publication we describe how to compute the window matrix that enables forward-modelling the impact of masked pixels and weights on the FFT-based estimators. Here, we use Gaussian and hydrodynamical simulations with artificially masked pixels to validate the method on the measurement of 1D correlations. Finally, we show that the formalism can be extended to model the impact on 3D correlations, in particular on the cross-spectrum, the correlation of 1D Fourier modes as a function of transverse separation. This work will enable more precise clustering measurements with the Lyα forest dataset recently collected by the Dark Energy Spectroscopic Instrument (DESI).

Lokken, Martine [Univ. Autonoma de Barcelona (Spai↗
DOE OSTI2025

Resolving turbulent magnetohydrodynamics: a hybrid operator-diffusion framework

We present a hybrid machine learning framework that combines physics-informed neural operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic turbulence across a broad range of Reynolds numbers (Re). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with Re ϵ {100, 250, 500, 750, 1000, 3000, 10000}, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At Re = 1000 and 3000, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels (Re = 10 000), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

Diffusion-Integrated Neural Operators↗
DOE OSTI2022

Stellar proper motions in the outskirts of classical dwarf spheroidal galaxies with Gaia EDR3

ABSTRACT We use Gaia EDR3 data to identify stars associated with six classical dwarf spheroidals (dSphs) (Draco, Ursa Minor, Sextans, Sculptor, Fornax, Carina) at their outermost radii, beyond their nominal King stellar limiting radius. For all of the dSphs examined, we find radial velocity matches with stars residing beyond the King limiting radius and with ${\gt}50{{\ \rm per\ cent}}$ astrometric probability (four in Draco, two in Ursa Minor, eight in Sextans, two in Sculptor, 12 in Fornax, and five in Carina), indicating that these stars are associated with their respective dSphs at high probability. We compare the positions of our candidate ‘extra-tidal’ stars with the orbital tracks of the galaxies, and identify stars, both with and without radial velocity matches, that are consistent with lying along the orbital track of the satellites. However, given the small number of candidate stars, we cannot make any conclusive statements about the significance of these spatially correlated stars. Cross matching with publicly available catalogues of RR Lyrae, we find one RR Lyrae candidate with ${\gt}50{{\ \rm per\ cent}}$ astrometric probability outside the limiting radius in each of Sculptor and Fornax, two such candidates in Draco, nine in Ursa Minor, seven in Sextans, and zero in Carina. Follow-up spectra on all of our candidates, including possible metallicity information, will help confirm association with their respective dSphs, and could represent evidence for extended stellar haloes or tidal debris around these classical dSphs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites

Here, land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8–23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Vector elastic deconvolution migration with dual wavefield decomposition

Here, we report elastic-wave imaging using multicomponent data can provide more useful subsurface information than acoustic-wave imaging, but it is usually algorithmically challenging. We have developed a vector elastic deconvolution migration method for high-resolution imaging of subsurface structures in isotropic and anisotropic elastic media. Our new method uses a vector deconvolution imaging condition based on dual wavefield decomposition, including an explicit directional wavefield separation using the Hilbert transform and a P/S vector wavefield decomposition using the low-rank decomposition method. Using three elastic models, we numerically determine that our new method produces notably higher resolution and more amplitude-balanced elastic images compared with a crosscorrelation-based vector elastic reverse time migration method.

58 GEOSCIENCES↗
DOE OSTI2026

Technical Report on Adjoint Waveform Tomography of East Asia for Improved Waveform Prediction

We present a preliminary version of the East Asia Tomography (EAT) model, an adjoint waveform tomography model of East and Southeast Asia. We used SPiRaL (Simmons et al., 2021) as our starting model and source parameters for 250 earthquakes from the Global Centroid Moment Tensor catalogue (Ekström et al., 2012). Over 198 iterations, we have iterated the EAT model down to a minimum period of 35 seconds. We plan on continuing our iteration technique down to 30 seconds period before updating our misfit function to use a normalized cross correlation-based misfit functions (e.g., Tao et al., 2018) to better constrain Earth structure. We hope to iterate the current extent of the model to 25 seconds minimum period before iterating to shorter periods for a smaller subregion of the full model.

58 GEOSCIENCES↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗