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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 OSTI2022

King Ghidorah Supercluster: Mapping the light and dark matter in a new supercluster at z = 0.55 using the subaru hyper suprime-cam

ABSTRACT This paper reports our discovery of the most massive supercluster, termed the King Ghidorah Supercluster (KGSc), at z = 0.50–0.64 in the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) over 690 deg2, as well as an initial result for a galaxy and dark matter mapping. The primary structure of the KGSc comprises triple broad weak-lensing (WL) peaks over 70 comoving Mpc. Such extensive WL detection at z > 0.5 can only currently be achieved using the wide-field high-quality images produced by the HSC-SSP. The structure is also contiguous with multiple large-scale structures across a ∼400 comoving Mpc scale. The entire field has a notable overdensity (δ = 14.7 ± 4.5) of red-sequence clusters. Additionally, large-scale underdensities can be found in the foreground along the line of sight. We confirmed the overdensities in stellar mass and dark matter distributions to be tightly coupled and estimated the total mass of the main structure to be 1 × 1016 solar masses, according to the mock data analyses based on large-volume cosmological simulations. Further, upcoming wide-field multi-object spectrographs such as the Subaru Prime Focus Spectrograph may aid in providing additional insights into distant superclusters beyond the 100 Mpc scale.

Shimakawa, Rhythm (ORCID:0000000344422750)↗
DOE OSTI2024

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Abstract Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Toxicology↗
DOE OSTI2024

Artificial Neural Network-based State Estimation for Low Observable, Unbalanced Microgrids for Microgrid Building Blocks

The microgrid building blocks (MBB) were proposed as microgrid components with combined sub-components with power conversion, communication, and microgrid control capability, or a subset of such sub-components. This work addresses the microgrid controller, present in an MBB, which requires accurate state estimation to perform its tasks, including for monitoring, power flow (dispatch), fault detection, etc. In this paper, an artificial neural network (ANN)-based framework for state estimation is proposed for an MBB, especially for unbalanced and low observable microgrids. To overcome the challenge of low observability in unbalanced systems, a concept of extended adjacent matrix is introduced to reduce the required number of measurements for state estimation. Addressing the challenges, a feed forward neural network (FNN) is utilized to enhance estimation accuracy and reliability with the reduced number of measurements. The proposed state estimation is validated through extensive simulations on a microgrid, which was achieved from the modified IEEE 34-bus distribution test feeder with multiple distributed energy resources (DERs) and demonstrated superior performance in estimation accuracy and low observability.

Choi, Jongchan↗
DOE OSTI2025

Voltage Probability Density Function Shaping Control Strategy Considering Grid Operational Uncertainties

It is well-known that power systems operation always affected by various uncertainties which make the bus voltage a random process that can be characterized by its probability density function (PDF) at any time instant. In this context, this paper presents a novel PDF-based voltage control framework for power systems. By modeling voltage as a stochastic process, we formulate a stochastic differential equationthat captures grid uncertainties. The associated Fokker-Planck-Kolmogorov equation is derived to describe the evolution of the voltage PDF, which enables the formulation of a PDF-shaping control strategy. To simplify the PDF control formulation, a B-spline neural network is introduced for real-time estimation and regulation of the voltage distribution. The proposed PDF control law updates voltage references for energy storage systems and synchronous generators using real-time PDF measurements and feedback signals. The proposed method is validated on a modified Kundur’s two-area system. Simulation results demonstrate that the controller can significantly improve the voltage stability under stochastic conditions, highlighting its effectiveness in modern inverter-rich grids.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2025

On the bulk compaction of brittle granular materials, Part II: Representing interparticle comminution by K COMP

An applied and empirical compaction fracture resistance parameter, K COMP , was developed to represent the compression-induced interparticle-comminution response of granular brittle materials (e.g., ceramic or glass particles). The development of K COMP is an outcome from Part I of this three-paper series. The K COMP represents a continuum response of compaction fracture resistance and was adapted from established Griffith linear elastic fracture mechanics theory. From that, the K COMP relates macroscopically applied compressive stress to a corresponding inverse-square-root representative particle size, where the latter is estimated from an entire particle size distribution or specific surface area measurement. In conclusion, the K COMP value was not constant over a wide range of stresses for all examined brittle granular materials, and this is indicative of a change in the dominant mode of permanent deformation at higher compaction stresses.

brittle↗
DOE OSTI2022

Conversion degree and heat transfer in the cold cap and their effect on glass production rate in an electric melter

A predictive model of melt rate in waste glass vitrification operations is needed to inform melter operations during normal and off-normal operations. This paper describes the development of a model of the cold cap (the reacting melter feed floating on molten glass in a glass melter) that couples heat transfer with the feed-to-glass conversion kinetics. The model was applied to four melter feeds designed for high-level and low-activity nuclear waste feeds using the material properties, either measured or estimated, to obtain temperature and conversion distribution within the cold cap. The cold cap model, when coupled with a computational fluid dynamics model of a Joule-heated glass melter, allows the prediction of the glass production rate and power consumption. The results show reasonable agreement with the melting rates measured during pilot-scale melter tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Search for New Bosons in Gluon-Gluon and Vector Boson Fusion Processes at the LHC and Development of Silicon Module Assembly Techniques for the CMS High Granularity Calorimeter

A search for heavy resonances decaying to a pair of vectors bosons is presented which utilizes events in which a hadronically decaying Z or W boson is identified using jet substructure techniques and large missing transverse momentum is found. Data analyzed were recorded by the Compact Muon Solenoid experiment at the CERN Large Hadron Collider in 2016, 2017, and 2018, and correspond to 137 fb$^{-1}$ . The events are categorized as having arisen from Vector Boson Fusion process or not, and are characterized by their transverse mass distribution. The standard model backgrounds are estimated based on observed yields in control regions. No excess of events above the expected SM background are observed and limits are placed on the production cross section of Radion (spin-0), Wprime (spin-1) and Bulk graviton (spin2) particles. In adiditon, the high luminosity LHC and the CMS HGCAL upgrade are also presented. The assembly techniques and various tests for the Silicon module and the cassette are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Seasonal Modulation of Crustal Seismicity in Northeastern Japan Driven by Snow Load

Numerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface-load-induced stress perturbation relative to the long-term-averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

58 GEOSCIENCES↗
DOE OSTI2022

A Geospatial Cost Comparison of CO2 Plume Geothermal (CPG) Power and Geologic CO2 Storage

CO 2 Plume Geothermal (CPG) power plants can use gigatonne-levels of CO 2 sequestration to generate electricity, but it is unknown if the resources that support low-cost CPG power align with the resources that support low-cost CO 2 sequestration. Here, we estimate and compare the geospatially-distributed cost of CPG and CO 2 storage across a portion of North America. We find that the locations with lowest-cost CO 2 storage are different than the locations with lowest-cost CPG. There are also locations with low-cost CO 2 storage (<$5/tCO 2 ) that do not support CPG power generation due to insufficient reservoir transmissivity or temperature. Thus, CPG development may require electricity prices that are greater than the levelized cost of electricity (LCOE) to offset the increased cost of sequestration. We introduce the “Additional Cost of Electricity (ACOE)” metric to account for this cost and add it to the LCOE to calculate breakeven electricity prices that are required for CPG development. We find that breakeven prices are lower when new CO 2 injection wells are drilled specifically for CPG (i.e., “greenfield” CPG development) compared to if only existing CO 2 sequestration injection wells are used (i.e., “brownfield” CPG development). This is because comparatively few wells are needed for sequestration-only, and the increased power capacity from having more CPG wells outweighs the increased costs from more drilling. We also find that sequestered CO 2 could be used to approximately triple the United States geothermal electricity power capacity via a single CPG “sweet spot” in South Dakota, but that breakeven electricity price for this development is on the order of $200/MW e h.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

DESI Mg II Absorbers: Extinction Characteristics and Quasar Redshift Accuracy

In this paper, we study how absorption-line systems affect the spectra and redshifts of quasi-stellar objects (QSOs), using catalogs of Mg II absorbers from the early data release and first data release of the Dark Energy Spectroscopic Instrument. We determine the reddening effect of an absorption system by fitting an unreddened template spectrum to a sample of 50,674 QSO spectra that contain Mg II absorbers. We find that reddening caused by intervening absorbers (v off > 3500 km s −1 ) has an average color excess of $\overline{E(B-V)}$ = 0.04 mag. We find that the E(B − V) tends to be greater for absorbers at low redshifts, or those having Mg II absorption lines with higher equivalent widths, but shows no clear trend with v off for intervening systems. However, the $\overline{E(B-V)}$ of associated absorbers, those at v off < 3500 km s −1 , shows a strong trend with v off , increasing rapidly with decreasing v off and peaking (∼0.15 mag) around v off = 0 km s −1 . We demonstrate that Mg II absorbers impact redshift estimation for QSOs by investigating the distributions of v off for associated absorbers. We find that at z > 1.5, these distributions broaden and bifurcate in a nonphysical manner. In an effort to mitigate this effect, we mask pixels associated with the Mg II absorption lines and recalculate the QSO redshifts. We find that we can recover voff populations in better agreement with those for z < 1.5 absorbers and in doing so typically shift background QSO redshifts by Δz ≈ ± 0.005.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2020

Study of the Cosmic Rays and Interstellar Medium in Local H I Clouds Using Fermi -LAT Gamma-Ray Observations

An accurate estimate of the interstellar gas density distribution is crucial to understanding the interstellar medium (ISM) and Galactic cosmic rays (CRs). To comprehend the ISM and CRs in a local environment, a study of the diffuse $γ$-ray emission in a midlatitude region of the third quadrant was performed. Specifically, the $γ$-ray data in the 0.1–25.6 GeV energy range of the Fermi Large Area Telescope (LAT) and other interstellar gas tracers such as the HI4PI survey data and the Planck dust thermal emission model were used, and the northern and southern regions were analyzed separately. The variation of the dust emission D em with the total neutral gas column density N H was studied in high dust temperature areas, and the N H /D em ratio was calibrated using $γ$-ray data under the assumption of a uniform CR intensity in the studied regions. The measured integrated $γ$-ray emissivities above 100 MeV are (1.58 ± 0.04) x 10 -26 photons s -1 sr -1 H-atom-1 and (1.59 ± 0.02) x 10-26 photons s-1 sr-1 H-atom -1 in the northern and southern regions, respectively, supporting the existence of a uniform CR intensity in the vicinity of the solar system. While most of the gas can be interpreted to be H I with a spin temperature of T S = 125 K or higher, an area dominated by optically thick H I with T S ~ 40 K was identified.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modeling the Galactic Neutron Star Population for Use in Continuous Gravitational-wave Searches

Searches for continuous gravitational waves (GW) from unknown Galactic neutron stars provide limits on the shapes of neutron stars. A rotating neutron star will produce GW if asymmetric deformations exist in its structure that are characterized by the star's ellipticity. Here, in this study, we use a simple model of the spatial and spin distribution of Galactic neutron stars to estimate the total number of neutron stars probed, using GW, to a given upper limit on the ellipticity. This may help optimize future searches with improved sensitivity. The improved sensitivity of third-generation GW detectors may increase the number of neutron stars probed, to a given ellipticity, by factors of 100 to 1000.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Simultaneous Observation of Ion-scale Wave Packets with Opposite Polarizations and Their Implications on the Generation Region in the Inner Heliosphere

This paper reports a dispersion analysis of two wave packets simultaneously observed near the local proton gyrofrequency by the Parker Solar Probe. The observed wave event exhibits clear two-banded wave packets both propagating along the magnetic field, characterized by left-handed (L-mode) and right-handed (R-mode) polarizations simultaneously. By incorporating the Doppler shift effect into a linear dispersion analysis, we find two possible scenarios that explain these simultaneous opposite polarizations: (1) Two inherently L-mode waves in the plasma frame, propagate parallel and antiparallel to the solar wind velocity, with similar wave frequencies and wave numbers. The polarization of the antiparallel propagating wave reverses as it moves sunward in the plasma frame while still comoving with the solar wind in the stationary frame. This reversal manifests the polarization of the wave as an R-mode in the spacecraft frame. (2) Simultaneous L-mode and R-mode waves propagate parallel to the solar wind velocity, with different wave frequencies and wave numbers. Concurrent proton observations during the wave event reveal a dominant anisotropic ($T$⟂/$T$ ∥ > 1) core distribution with a drifting beam population. Estimation of the linear growth rate for both L-mode and R-mode waves suggests that both scenarios are plausible, indicating that the observation is near the wave-generation region. We explore the potential impact of these simultaneous waves on solar wind heating and scattering effects, hypothesizing that such waves might enhance efficiency compared to waves with a single wave packet, contingent upon the statistical significance of such waves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Three-dimensional Collapse of a Rapidly Rotating 16 M ⊙ Star

I report on the three-dimensional (3D) hydrodynamic evolution of a rapidly rotating 16 M ⊙ star to iron core collapse. For the first time, I follow the 3D evolution of the angular momentum (AM) distribution in the iron core and convective shell burning regions for the final 10 minutes up to and including gravitational instability and core collapse. In 3D, convective regions show efficient AM transport that leads to an AM profile that differs in shape and magnitude from MESA within a few shell convective turnover timescales. For different progenitor models, such as those with tightly coupled Si/O convective shells, efficient AM transport in 3D simulations could lead to a significantly different AM distribution in the stellar interior affecting estimates of the natal neutron star or black hole spin. The results suggest that 3D AM transport in convective and rotating shell burning regions are critical components in models of massive stars and could qualitatively alter the explosion outcome and inferred compact remnant properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quantifying uncertainty in Pareto estimates of global lake area

This software contains the code for Bayesian uncertainty analysis of global lake area. Computed uncertainties are compared against more conventional estimation using lake size-abundance distributions. Routines are included to explore sensitivity to observational errors and ad-hoc "censoring" strategies.

Stachelek, Jemma↗