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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tritium Transport Phenomena in Molten-Salt Reactors

In this work, we review phenomena relevant to tritium transport in molten-salt reactors, which produce tritium from lithium and beryllium salts at significantly higher levels than other reactor types. Modeling of such phenomena began following MSRE operations, and these early models attempted to predict measured tritium distributions in the MSRE, accounting for turbulent mass-transport processes (using established heat transfer correlations), permeation through a variety of metal structures such as heat exchanger tubes, and transport to and from bubbles introduced into the salt by gas sparging. The models reasonably reproduced the MSRE data, but did so best when the permeability of structures was reduced by about a factor of 1,000. This issue does not appear to have been conclusively resolved, and all of the more recent attempts to model tritium transport in molten salts appear to make use of the same methodology. MSRE remains, however, essentially our only source of integral tritium transport data relevant to MSRs. Here, we generalize the MSRE approach to permeation in order to include potential rate-limiting effects at interfaces, as well the effects of added hydrogen. Appropriately non-dimensionalized, this system of equations identifies two dimensionless numbers whose relative values clearly delineate the conditions under which mass transport, surface effects, permeation, and hydrogen swamping are expected to become rate-limiting. We also describe the preliminary conceptual design of a forced convection FLiBe loop, into which tritium would be introduced for the purpose of providing validation data for such a model. The primary purpose of this is to investigate the coupled transport phenomena described above and identify those that are rate-limiting in MSRs. Additional test section configurations are described that would address other transport phenomena relevant to MSRs, including bubbly flows and graphite interactions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. 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. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M 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.

Kincher-Winoto, Kina↗
DOE OSTI2020

Multi-GPU immersed boundary method hemodynamics simulations

Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Role of the U.S. Electric Distribution System in Serving Data Center and Other Large Loads

The rapid expansion of data centers in the United States is reshaping how the electric distribution system must plan for and accommodate large load interconnections. This report evaluates the role of the distribution grid in serving these loads, from small edge facilities to hyperscale campuses. Using national datasets, utility filings, and industry studies, we assess demand growth, reliability requirements, interconnection thresholds, and infrastructure needs at substations and feeders. The analysis highlights the mismatch between fast data center development timelines and slower utility planning and construction cycles, as well as strategies such as phased energization, on-site generation, hosting capacity maps, and structured interconnection frameworks. While focused on data centers, the insights also apply to other high-density loads such as advanced manufacturing, hydrogen production, and electrified transportation. The report concludes with approaches to align planning processes, transparency tools, and regulatory frameworks so utilities can manage new large loads in ways that support a reliable and resilient grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

The Complete Calibration of the Color–Redshift Relation (C3R2) Survey: Survey Overview and Data Release 1

A key goal of the Stage IV dark energy experiments Euclid , LSST, and WFIRST is to measure the growth of structure with cosmic time from weak lensing analysis over large regions of the sky. Weak lensing cosmology will be challenging: in addition to highly accurate galaxy shape measurements, statistically robust and accurate photometric redshift (photo- z ) estimates for billions of faint galaxies will be needed in order to reconstruct the three-dimensional matter distribution. Here we present an overview of and initial results from the Complete Calibration of the Color–Redshift Relation (C3R2) survey, which is designed specifically to calibrate the empirical galaxy color–redshift relation to the Euclid depth. These redshifts will also be important for the calibrations of LSST and WFIRST . The C3R2 survey is obtaining multiplexed observations with Keck (DEIMOS, LRIS, and MOSFIRE), the Gran Telescopio Canarias (GTC; OSIRIS), and the Very Large Telescope (VLT; FORS2 and KMOS) of a targeted sample of galaxies that are most important for the redshift calibration. We focus spectroscopic efforts on undersampled regions of galaxy color space identified in previous work in order to minimize the number of spectroscopic redshifts needed to map the color–redshift relation to the required accuracy. We present the C3R2 survey strategy and initial results, including the 1283 high-confidence redshifts obtained in the 2016A semester and released as Data Release 1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Path-space variational inference for non-equilibrium coarse-grained systems

In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Structure and Oxygen Evolution Activity of β-NiOOH: Where Are the Protons?

Ni oxides and oxyhydroxides (NiO x ) have been studied for a long time as cathode materials for alkaline batteries and electrocatalysts for the oxygen evolution reaction (OER). Yet, understanding of the connection between their atomic and electronic structures and electrochemical performance or stability is still incomplete. In this work, we use first-principles density functional theory (DFT) calculations to revisit the structure, electronic properties, and OER activity of β-NiOOH, the catalytically active phase of NiO x . Following extensive DFTbased screening, we identify a hitherto overlooked structure characterized by a uniform distribution of H atoms on the NiO 2 layers. All the Ni 3+ cations in this structure exhibit an identical t g 6 e g 1 electronic configuration with an occupied 3d z2 orbital. Comparison of the calculated bond lengths with extended X-ray absorption fine structure (EXAFS) data unequivocally supports this structure relative to all other low-energy configurations. Based on this structure, we uncover and detail defect-dominated OER mechanisms on the basal β-NiOOH (001) surface, with overpotentials as low as 0.39 V. The present results should provide a valuable contribution to ongoing efforts for understanding and developing enhanced transition-metal hydroxide catalysts for the OER.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A small core in Vesta inferred from Dawn’s observations

Vesta’s large-scale interior structure had previously been constrained primarily using the gravity and shape data from the Dawn mission. However, these data alone still allow a wide range of possibilities for the differentiation state of the body. The moment of inertia is arguably the most diagnostic parameter related to the radial density distribution of a planetary body, making it crucial for assessing the body’s state of internal differentiation. Determining the moment of inertia requires additional measurements of the amplitudes of small rotational motions, such as precession and nutation. Here we report an updated estimate of the moment of inertia of Vesta inferred from Dawn’s Doppler tracking via the Deep Space Network and onboard imaging data. The recovered value for Vesta’s normalized polar moment of inertia is $\overline{C}$/MR 2 = 0.4208 ± 0.0047 (where M is the mass of Vesta and R is the reference radius), which is only 6.6% lower than the homogeneous value of 0.4505. This value, combined with the gravity field and global shape, suggests that Vesta’s interior has limited density stratification beneath its howardite–eucrite–diogenite-dominated crust. We propose two possible origin scenarios that are consistent with the observed constraints. In the first scenario, Vesta’s interior did not undergo full differentiation due to late accretion. In the second scenario, Vesta originated as an impact remnant of a larger differentiated body re-accreted with non-chondritic bulk composition produced from a catastrophic impact. Vesta did not experience complete differentiation in either scenario, suggesting that its current state reflects a complex interplay between its accretion timing, thermal evolution, redistribution of 26 Al bearing melt and/or impact processes.

CNEOS 2014-01-08 bolide↗
DOE OSTI2025

Structural constraint integration in a generative model for the discovery of quantum materials

Billions of organic molecules have been computationally generated, yet functional inorganic materials remain scarce due to limited data and structural complexity. Here, in this work, we introduce Structural Constraint Integration in a GENerative model (SCIGEN), a framework that enforces geometric constraints, such as honeycomb and kagome lattices, within diffusion-based generative models to discover stable quantum materials candidates. SCIGEN enables conditional sampling from the original distribution, preserving output validity while guiding structural motifs. This approach generates ten million inorganic compounds with Archimedean and Lieb lattices, over 10% of which pass multistage stability screening. High-throughput density functional theory calculations on 26,000 candidates shows over 95% convergence and 53% structural stability. A graph neural network classifier detects magnetic ordering in 41% of relaxed structures. Furthermore, we synthesize and characterize two predicted materials, TiPd 0.22 Bi 0.88 and Ti 0.5 Pd 1.5 Sb, which display paramagnetic and diamagnetic behaviour, respectively. Our results indicate that SCIGEN provides a scalable path for generating quantum materials guided by lattice geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of rare earth size on network structure and glass forming ability in binary aluminum garnets

Rare earth aluminate glasses are potentially useful for optical, luminescence, and laser applications. As reluctant glass formers, these materials exhibit unconventional atomic structures. To better understand how their structures correlate with glass formation, we investigate two rare earth aluminum garnet melts, La 3 Al 5 O 12 (LAG) and Yb 3 Al 5 O 12 (YbAG), which represent the relative extremes of good and poor glass forming ability in rare earth aluminates. Structural models have been refined to high-energy X-ray diffraction data over 1340–2740 K. Both melts contain mixtures of AlO 4 , AlO 5 , and AlO 6 polyhedra, with larger fractions of [5] Al and [6] Al in YbAG. Extrapolation of the Al–O coordination distributions to the glass transition match closely with 27 Al nuclear magnetic resonance measurements of (La 1−z Y z ) 3 Al 5 O 12 glasses, z = 0 to 1. During cooling, the mean coordination numbers increase for La–O in LAG from 6.45(8) to 6.98(8) and for Yb–O in YbAG from 6.02(8) to 6.21(8). Linkedness among Al–O polyhedra at ∼2450 K is mostly corner-sharing, with 9% edge-sharing in LAG and 19% in YbAG. Among [4] Al units, both melts have 6% edge-sharing that convert to all corner-sharing upon cooling. Network connectivity is compared using a newly defined metric, K n , that is similar to the Q n distribution but that accounts for the edge-sharing and triply bonded oxygen present in these melts. The lower glass forming ability in YbAG as compared to LAG correlates with more edge-sharing, associated with the larger fractions of [5] Al and [6] Al, and lower connectivity among [4] Al units.

Wilke, Stephen K. [Materials Development, Inc., Ar↗
DOE OSTI2025

Pore2Chip: All-in-one python tool for soil microstructure analysis and micromodel design

The Pore2Chip Python package is designed to create 2D micromodels using extracted data from 3D X-ray computed tomography (XCT) images. This package helps analyze soil structure and function, allowing for the investigation of hydro-biogeochemical processes that impact mineral extraction and reactivity, oxygen concentrations, and nutrient availability in disturbed or managed soils. Key metrics encompass pore size distributions, pore throat size distributions, and connectivity (pore coordination numbers). The final output is a 2D scalable SVG design representing a core or aggregate. Designs can be fabricated with methods such as laser etching, 3D printing, and photolithography.

lab-on-chip↗
DOE OSTI2025

A Semi-supervised Hybrid Machine Learning Framework for the Qualification of Resistance Spot Welds

• Industries requiring high structural integrity, including automotive, aerospace, and construction, place considerable significance on weld quality classification. • The inspection normally involves human expertise through predefined quality metrics that are subjective, error-prone, and time-intensive • The challenge to classification model development is the scarcity of labeled data and imbalanced distributions in the data that are labeled. • This work develops a new hybrid methodology that achieves clustering using KMeans++ together with supervised classification to overcome these challenges. • The ensemble-based classifiers were identified as optimal, with accuracy enhancements of up to 8% using the pseudo-labeled dataset. • The work provides practical insight into feature engineering and machine learning integration in industrial quality assurance applications.

Rogers, Jeremy K. [Savannah River National Laborat↗
DOE OSTI2017

Multiwavelength study of the star formation in the S237 H ii region

We present a detailed multiwavelength study of observations from X-ray, near-infrared, and centimeter wavelengths to probe the star formation processes in the S237 region. Multiwavelength images trace an almost sphere-like shell morphology of the region, which is filled with the 0.5–2 keV X-ray emission. The region contains two distinct environments—a bell-shaped cavity-like structure containing the peak of 1.4 GHz emission at center, and elongated filamentary features without any radio detection at edges of the sphere-like shell—where Herschel clumps are detected. Using the 1.4 GHz continuum and {sup 12}CO line data, the S237 region is found to be excited by a radio spectral type of B0.5V star and is associated with an expanding H ii region. The photoionized gas appears to be responsible for the origin of the bell-shaped structure. The majority of molecular gas is distributed toward a massive Herschel clump (M{sub clump} ∼ 260 M{sub ⊙}), which contains the filamentary features and has a noticeable velocity gradient. The photometric analysis traces the clusters of young stellar objects (YSOs) mainly toward the bell-shaped structure and the filamentary features. Considering the lower dynamical age of the H ii region (i.e., 0.2–0.8 Myr), these clusters are unlikely to be formed by the expansion of the H ii region. Our results also show the existence of a cluster of YSOs and a massive clump at the intersection of filamentary features, indicating that the collisions of these features may have triggered cluster formation, similar to those found in the Serpens South region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Angular Dependence of Drell-Yan in the SeaQuest Experiment using Deep Neural Network-Based Reconstruction

The SeaQuest and SpinQuest experiments at Fermilab were designed to probe the internal dynamics of protons and neutrons via the angular dependence of muons created by colliding 120 GeV protons into stationary targets. To do this, it is necessary to translate the detector data into coherent information about the particles detected in the experiment. This dissertation describes the development and implementation of QTracker, a neural network-based algorithm designed to reconstruct muons. The application of QTracker to experimental data yields improved reconstruction of muon tracks, allowing for a more precise investigation of the transverse momentum distributions and angular modulations in the Drell-Yan process. This work highlights the potential of neural network-based methods to advance particle tracking and enhance our understanding of nucleon structure.

Conover, Arthur↗
DOE OSTI2026

Precision Kinematic Sunyaev--Zel'dovich Measurements Across Halo Mass and Redshift with DESI DR2 and ACT DR6: Part I. Luminous Red Galaxies

We present the most precise measurements of the kinetic Sunyaev-Zel'dovich (kSZ) effect around luminous red galaxies to date, detecting the signal at $18σ$ significance in both harmonic and configuration space. Our analysis cross-correlates 2.4 million spectroscopic LRGs from the Dark Energy Spectroscopic Instrument (DESI) DR2 sample with Data Release 6 (DR6) of the Atacama Cosmology Telescope (ACT). We develop a novel harmonic-space cross-correlation approach using momentum-weighted kSZ templates, yielding nearly uncorrelated bandpowers within a framework consistent with other large-scale structure analyses. By incorporating the LRG halo occupation distribution (HOD) and its uncertainty, we convert measured galaxy gas profiles into halo gas profiles and provide generalized Navarro-Frenk-White (GNFW) fitting profiles, providing empirical targets for tuning feedback efficiency in hydrodynamical simulations and for baryonic modeling in large-scale structure analyses. We find strong evidence that gas profiles do not trace dark matter, providing direct evidence for gas redistribution beyond gravitational collapse. Comparing to hydrodynamical simulations, our measurements favor feedback efficiencies exceeding those in the Battaglia profile, suggesting more efficient gas ejection in group-scale halos than previously predicted. Splitting by redshift, we detect the kSZ signal at SNR $\approx 5$--$10$ in each of four bins and find amplitude evolution consistent with the expected decline in mean halo mass at fixed comoving number density. Splitting by stellar mass, we study the scaling of kSZ amplitude with galaxy properties. Together with BGS and ELG measurements in Paper II, these results span $0.1 \lesssim z \lesssim 1.6$ across three galaxy populations, demonstrating the potential of spectroscopic kSZ to map circumgalactic gas and constrain baryonic feedback.

Qu, F. J. [KIPAC, Menlo Park; Stanford U., HEPL; S↗
DOE OSTI2018

Optimization of the Growth Technology for Crystals with Garnet Structure Based on X-Ray Diffraction Data

High efficiency of the methods of double-crystal X-ray diffractometry (DCXRD) and topography for improving the growth technology of highly homogeneous crystals has been demonstrated on the example of gadolinium gallium garnet (GGG) single crystals. The main types of structural defects observed in Czochralski-grown GGG crystals are found to be macroscopic inhomogeneity of composition distribution, caused by the facet effect manifestation; microinhomogeneous distribution of impurity and main components of the composition in striations; dislocations; and second-phase inclusions. The relationship between the type and density of newly formed defects and the technological conditions for crystal growth are considered. Optimization of the composition of crystals and their growth technology made it possible to obtain high-quality dislocation-free crystals of GGG and complex-substituted garnets on its basis for magneto-optical and microwave devices, elements of solid-state lasers, and other applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Quantifying a partial polyamorphic transition in a cerium-based metallic glass during cooling

Cerium-based metallic glasses are prototype polyamorphous systems with pressure-induced polyamorphic transitions extensively reported. Cooling typically has a similar effect on materials as compression with regard to reducing volume. However, previous studies show dramatically different behavior of Ce-based metallic glasses between cooling and compression, whose origin remains unclear. Here, using in situ low-temperature synchrotron high-energy x-ray diffraction, the structural evolution of a Ce68Al10Cu20Co2 metallic glass is accurately determined and analyzed by a structure factor and a reduced pair distribution function (PDF) during cooling from 298 to 83 K. An unusually large linear thermal expansion coefficient is revealed, which is associated with both continuous but inconsistent structural changes between the two subpeaks of the first atomic shell in terms of average bond lengths and coordination numbers. These phenomena are suggested to be attributed to a gradual 4f electron delocalization of only a minimal amount (∼2.6% at 83 K) of Ce atoms by quantitative analysis of the PDF data. However, a previously expected global polymorphic transition from a low-density amorphous state to a high-density amorphous state with an abrupt volume collapse is not observed. Moreover, electrical resistivity also shows a continuous increase during cooling without any sharp change. It is clarified that cryogenic temperatures could facilitate but are not powerful enough alone to trigger a global polymorphic transition in the Ce68Al10Cu20Co2 metallic glass, suggesting a wide distribution of its local atomic environment.

Chen, Zhi↗