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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data

Here, we implement Crossing Statistics to reconstruct in a model-agnostic manner the expansion history of the universe and properties of dark energy, using DESI Data Release 1 (DR1) BAO data in combination with one of three different supernova compilations (PantheonPlus, Union3, and DES-SN5YR) and Planck CMB observations. Our results hint towards an evolving and emergent dark energy behaviour, with negligible presence of dark energy at z ≳ 1, at varying significance depending on data sets combined. In all these reconstructions, the cosmological constant lies outside the 95% confidence intervals for some redshift ranges. This dark energy behaviour, reconstructed using Crossing Statistics, is in agreement with results from the conventional w 0 –w a dark energy equation of state parametrization reported in the DESI Key cosmology paper. Our results add an extensive class of model-agnostic reconstructions with acceptable fits to the data, including models where cosmic acceleration slows down at low redshifts. We also report constraints on H 0 r d from our model-agnostic analysis, independent of the pre-recombination physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Templates of expected measurement uncertainties

The covariance committee of CSEWG (Cross Section Evaluation Working Group) established templates of expected measurement uncertainties for neutron-induced total, (n,γ), neutron-induced charged-particle, and (n,xn) reaction cross sections as well as prompt fission neutron spectra, average prompt and total fission neutron multiplicities, and fission yields. Templates provide a list of what uncertainty sources are expected for each measurement type and observable, and suggest typical ranges of these uncertainties and correlations based on a survey of experimental data, associated literature, and feedback from experimenters. Information needed to faithfully include the experimental data in the nuclear-data evaluation process is also provided. These templates could assist (a) experimenters and EXFOR compilers in delivering more complete uncertainties and measurement information relevant for evaluations of new experimental data, and (b) evaluators in achieving a more comprehensive uncertainty quantification for evaluation purposes. This effort might ultimately lead to more realistic evaluated covariances for nuclear-data applications. In this topical issue, we cover the templates coming out of this CSEWG effort–typically, one observable per paper. This paper here prefaces this topical issue by introducing the concept and mathematical framework of templates, discussing potential use cases, and giving an example of how they can be applied (estimating missing experimental uncertainties of 235 U(n,f) average prompt fission neutron multiplicities), and their impact on nuclear-data evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Mammalian Chromosome Analysis and Sorting by Flow Cytometry

The analysis of chromosomes by flow cytometry is termed flow cytogenetics, and it involves the analysis and sorting of single mitotic chromosomes in suspension. The study of flow karyograms provides insight into chromosome number and structure to provide information on chromosomal DNA content and can enable the detection of deletions, translocations, or any forms of aneuploidy. Beyond its clinical applications, flow cytogenetics greatly contributed to the Human Genome Project through the ability to sort pure populations of chromosomes for gene mapping, cloning, and the construction of DNA libraries. Maximizing the potential of these important applications of flow cytogenetics relies on precise instrument setup and optimal sample processing, both of which impact the accuracy and quality of the data that are generated. This article is a compilation of the existing protocols that describe the stepwise methodology of accumulating, isolating, and staining metaphase chromosomes to prepare single-chromosome suspensions for flow cytometric analysis and sorting. Although the chromosome preparation protocols have remained largely unchanged, cytometer technology has advanced dramatically since these protocols were originally developed. Advances in cytometry technologies offer new and exciting approaches for understanding and monitoring chromosomal aberrations, but the hallmark of these protocols remains their simplicity in methodologies and reagent requirements and the accuracy of data resolvable to every chromosome of the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Implementing Directive-Based Deferred Execution for Effective Network Aggregation

Remote direct memory access technology provides an efficient mechanism for one-sided communication that can be leveraged to implement a distributed shared memory programming model. However, when applications generate large numbers of small, irregular messages, network congestion often arises. Existing solutions address this small message problem by facilitating message aggregation but typically require disruptive code transformations that detract from the algorithmic intent of applications, or can be limited by dependent operations on aggregated data between synchronisation points. A solution is to use a directive-assisted approach that enables compilers to transform code dependent on aggregated communication for deferred execution. This paper presents an algorithm that a compiler can use to implement and optimise deferred execution for code dependent on aggregated data, based on an "aggregation context" extension for the OpenSHMEM partitioned global address space library. This new capability addresses a key challenge of message aggregation, allowing its full potential to reduce network congestion and enhance programmability to be realised.

Welch, Aaron [ORNL]↗
DOE OSTI2021

Analysis of Selected Publicly Available Geothermal Exploration Data Gaps

As part of a United States Department of Energy (DOE) supported retrospective analysis of DOE's Play Fairway Analysis (PFA) projects, the National Renewable Energy Laboratory (NREL) compiled and analyzed publicly available geothermal exploration datasets to identify and highlight data gaps in areas prospective for hosting geothermal resources. The analysis was intended to understand the existing geographic coverage of selected datasets commonly utilized both by the PFA projects and geothermal developers during resource assessments including geologic mapping, temperature gradient drilling, and aeromagnetic, gravimetric, and lidar surveys. Results indicate that broad areas of the western United States estimated to have geothermal potential lack sufficient geologic and geophysical coverage necessary for even regional resource exploration. The study directly informed the recent Geoscience Data Acquisition for Western Nevada, or GeoDAWN - which united DOE's Geothermal Technologies Office (GTO) with the U.S. Geological Survey (USGS) of the U.S. Department of the Interior to assist U.S. needs for energy and critical minerals. The study also has the potential to inform public investment in further data acquisition for characterization of the Earth both for geothermal and other natural resource assessments.

data↗
DOE OSTI2025

Correlation Between Weather Alerts and Grid Component Failures for Grid Alert

Weather events cause most grid failures. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Recent research at Idaho National Laboratory into electric grid risk analysis methods resulted in a tool that allows for the development of the most likely scenarios given failure probabilities of grid components. INL has a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response (CESER) program to develop a Grid Alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios uses MASTERRI and then notifies the utility if there is significant risk. Historical failure data of elements that comprise the U.S. electric grid have been compiled by utilities and organizations such as the international regulatory body North American Electric Reliability Corporation (NERC). Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates are needed for different component types given the alert type, severity, and location. Historic weather-related grid element failures are correlated with historic weather events from Integrated Public Alert & Warning System (IPAWS). These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. This discusses the Grid Alert project plan but focuses on the data gathered and process used in determining failure rates for possible grid failure scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improving Runtime Performance of Tensor Computations using Rust From Python

In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API—e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor kernels of increasing complexity, from simple kernels involving sums of products over data accessed through single and nested loops to more advanced tensor multiplication kernels that are key in low-rank tensor decomposition and tensor regression algorithms. In numerical experiments involving synthetically generated tensor data of various sizes and these tensor kernels, we demonstrate consistent improvements in runtime performance when using Rust from Python over 1) using Python alone, 2) using Python and the Numba just-in-time Python compiler (for loop-based kernels), and 3) using the NumPy Python package for scientific computing (for pyttb kernels).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optical spectroscopy of molten fluorides: Methods, electronic and vibrational data, structural interpretation, and relevance to radiative heat transfer

In this study, to help address the need for predicting radiative heat transfer (RHT) behavior of molten salts, we conducted a comprehensive review of methods and data from optical spectroscopic measurements on molten fluoride salts. Transmittance, reflectance, and trans-reflectance experimental methods are discussed, along with the corresponding data reduction methodology and the limitations of each technique. Optical spectroscopy is a convenient indirect probe for changes in structural parameters with temperature and composition. Electronic and vibrational absorption data for transition-metal, lanthanide, and actinide solutes and vibrational absorption data for alkali and alkaline earth fluoride solvents are compiled, and the corresponding structural interpretation is discussed and compared with other experimental and theoretical work. We find that solvent and solute vibrational absorption can be significant in the mid-infrared, resulting in near-infrared edges of significance to RHT. Extrapolation and averaging of existing edge data leads to estimated gray absorption coefficient values at 700 °C of 546 m —1 for FLiBe and 276 m —1 for FLiNaK, both within the range of 1 – 6000 m —1 identified to be of engineering relevance for radiative heat transfer analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evolution of the ROOT Tree I/O

The ROOT TTree data format encodes hundreds of petabytes of High Energy and Nuclear Physics events. Its columnar layout drives rapid analyses, as only those parts (“branches”) that are really used in a given analysis need to be read from storage. Its unique feature is the seamless C++ integration, which allows users to directly store their event classes without explicitly defining data schemas. In this contribution, we present the status and plans of the future ROOT 7 event I/O. Along with the ROOT 7 interface modernization, we aim for robust, where possible compile-time safe C++ interfaces to read and write event data. On the performance side, we show first benchmarks using ROOT’s new experimental I/O subsystem that combines the best of TTrees with recent advances in columnar data formats. A core ingredient is a strong separation of the high-level logical data layout (C++ classes) from the low-level physical data layout (storage backed nested vectors of simple types). We show how the new, optimized physical data layout speeds up serialization and deserialization and facilitates parallel, vectorized and bulk operations. This lets ROOT I/O run optimally on the upcoming ultra-fast NVRAM storage devices, as well as file-less storage systems such as object stores.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Development and Evaluation of a General Drag Model for Gas-Solid Flows via Deep Learning

This project presents the development and evaluation of a general drag model for gas–solid multiphase flows using deep learning techniques. A comprehensive database of more than 4,000 experimental and numerical data points for spherical and non spherical particles was compiled, incorporating geometric features such as sphericity, aspect ratio, and orientation. Several predictive approaches—including traditional em pirical correlations, machine learning, and deep neural networks—were benchmarked, with the proposed Drag Coefficient Correlation-aided Deep Neural Network (DCC DNN) demonstrating superior accuracy. To account for particle–particle interactions, additional drag data were generated using CFD-based simulations of packed and flu idized beds, leading to the development of a retrained model capable of incorporat ing volume fraction effects. Integration of the trained model with the MFiX CFD solver was achieved using FTorch, enabling drag predictions during discrete element method (DEM) simulations. Validation against experimental data for single particles and fluidized beds confirmed the model’s improved predictive ability, particularly for non-spherical geometries. While the model performed strongly under fluidized con ditions, limitations remained in unfluidized regimes, suggesting a need for expanded datasets. Overall, this study demonstrates the feasibility of combining deep learning with physics-informed CFD to improve drag modeling for gas–solid flows, with promis ing implications for scaling multiphase simulations in industrial applications.

42 ENGINEERING↗
DOE OSTI2023

Robust inference of ecosystem soil water stress from eddy covariance data

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. Here, we develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A High Performance Sparse Tensor Algebra Compiler in MLIR

Sparse tensor algebra is widely used in many applications, including scientific computing, machine learning, and data analytics. The performance of sparse tensor algebra kernels strongly depends on the intrinsic characteristics of the input tensors, hence many storage formats are designed for tensors to achieve optimal performance for particular applications/architectures, which makes it challenging to implement and optimize every tensor operation of interest on a given architecture. We propose a tensor algebra domain-specific language (DSL) and compiler framework to automatically generate kernels for mixed sparse-dense tensor algebra operations. The proposed DSL provides high-level programming abstractions that resemble the familiar Einstein notation to represent tensor algebra operations. The compiler introduces a new Sparse Tensor Algebra dialect built on top of LLVM's extensible MLIR compiler infrastructure for efficient code generation while covering a wide range of tensor storage formats. Our compiler also leverages input-dependent code optimization to enhance data locality for better performance. Our results show that the performance of automatically generated kernels outperforms the state-of-the-art sparse tensor algebra compiler, with up to 20.92x, 6.39x, and 13.9x performance improvement over state-of-the-art tensor algebra compilers, for parallel SpMV, SpMM, and TTM, respectively.

Tian, Ruiqin↗
DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗
DOE OSTI2022

Hydropower Capital and O&M Costs: An Exploration of the FERC Form 1 Data

This report explores the potential for using responses from the Federal Energy Regulatory Commission’s (FERC’s) “Electric Utility Annual Report,” also known as FERC Form 1, as a cost database for conventional and pumped storage hydropower (PSH). The report outlines the process used to compile an easy-to-access database from the original FERC Form 1 data and discusses the historical cost/performance data. The steps for developing the database include downloading the annual FoxPro files from the FERC website (FERC, 2020), reading them into an Excel format, and reconciling naming issues across years, repeated entries, and shared assets, among others. The cleaned and compiled Form 1 database is available on Oak Ridge National Laboratory’s (ORNL’s) HydroSource website. For this report, the compiled Form 1 database was linked to two other hydropower databases, the National Inventory of Dams and the ORNL Existing Hydropower Assets, that provide additional information on the characteristics and other features of plants in the Form 1 data. Explorations of the plant characteristics, performance, capital costs, and operation and maintenance (O&M) cost data in the combined database were presented separately for conventional hydropower and PSH projects. Form 1 has several advantages as a hydropower cost database. First, the cost data is reported directly by plant owners. Second, Form 1 provides cost breakdowns of capital and operating costs for large conventional and PSH plants enabling more detailed tracking of hydropower costs compared with typical total cost estimates. Third, although the Form 1 data is not reported by all operational hydropower plants in the United States, the available data represents a wide range of plant characteristics and a sizable proportion of the hydropower fleet (61% of PSH plants and 22% of conventional hydropower plants by capacity). The lower proportion of the conventional hydropower fleet is because Form 1 reporting requirements apply only to private utilities that meet given size thresholds, leaving out federally owned facilities and many smaller hydropower plants. Overall, the Form 1 cost database represents a unique, publicly available database on hydropower asset capital and O&M costs. This report leads to a compiled database for the reporting years 1994 to 2020 that helps resolve issues with accessibility and use of the FERC Form 1 data by hydropower plant owners, project developers, technology developers, and regulators.

13 HYDRO ENERGY↗
DOE OSTI2020

The Incorporation of Pump-and-Treat Data in the New Tracking Restoration and Closure (TRAC)Web-Based Mapping Tool - 20339

Groundwater pump-and-treat (P and T) systems are a common remediation strategy for sites with contaminated groundwater within the U.S. Department of Energy (DOE) Office of Environmental Management (EM). Currently, there are six DOE-EM sites with active P and T systems, but integrated information on individual systems is only primarily available in separate annual reports. To provide a broad view of both current and historical P and T operations, data has been collected on all of the P and T systems. This summary information not only includes capital and average annual costs, but also identifies contaminants treated and forecasted P and T closure dates. Principal contaminants of concern treated with P and T systems include trichloroethylene, chromium (VI), strontium-90, technetium-99, and uranium. DOE-EM is continuing to create a web-based mapping tool that compiles information on groundwater contaminants, remediation strategies and, current data related to plumes and cleanup progress within all DOE-EM sites. The information collected on P and T systems will be added to the TRAC (Tracking Restoration and Closure) tool using interactive maps to quickly access and share information. This information sharing supports the transition from active to passive remediation methods and long-term monitoring approaches. TRAC can ensure that managers, stakeholders, regulators, and contractors remain up to date on cleanup progress for all sites within the complex. The information on P and T systems expands the use of TRAC and can assist in project monitoring and budget planning and support potential shifts in management plans based on plume data. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Boosting RDataFrame performance with transparent bulk event processing

RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. Thanks to its design inspired by declarative programming principles, RDataFrame enables the development of highperformance, highly parallel analyses without requiring expert knowledge of multi-threading and I/O: user logic is expressed in terms of self-contained, small computation kernels tied together by a high-level API. This design completely decouples analysis logic from its actual execution, and opens several interesting avenues for workflow optimization. In particular, in this work we explore the benefits of moving internal data processing from an event-by-event to a bulkby-bulk loop. This refactoring dramatically reduces the framework’s runtime overheads; in collaboration with the I/O layer it improves data access patterns; it exposes information that optimizing compilers might use to auto-vectorize the invocation of user-defined computations; finally, while existing user-facing interfaces remain unaffected, it becomes possible to additionally offer interfaces that explicitly expose bulks of events, useful e.g. for the injection of GPU kernels into the analysis workflow. In order to inform similar future R&D, design challenges will be presented, as well as an investigation of the relevant timememory trade-off backed by novel performance benchmarks.

Guiraud, Enrico↗
DOE OSTI2025

New K-feldspar Pb isotope results for Mesozoic arc crust in the Pacific Northwest, U.S.A. and Canada: comparison with the Mojave-Salinia province of southern California and Implications for Baja-BC

Measurements of lead isotopic compositions in detrital K-feldspar have been increasingly used as a tool to assess sediment provenance. We compiled a database of previously published Pb isotope data from 700 bedrock K-feldspar samples and 1,423 age-corrected bedrock whole rock samples from western North American igneous and metamorphic bodies. Additionally, we report 66 new K-feldspar Pb isotope data for plutons throughout the Pacific Northwest region of the United States and British Columbia. Results show that the Pb isotope values of plutonic K-feldspar depend on the isotopically juvenile or evolved nature of underlying crust. Samples obtained from the mid Cretaceous – mid Eocene Coast Plutonic Complex, North Cascades, and Intermontane superterrane that occur west of the initial 87 Sr/ 86 Sr (Sri) = 0.706 isopleth exhibit a highly restricted 207 Pb/ 206 Pb and 208 Pb/ 206 Pb values centred upon 0.83 and 2.03, respectively. Conversely, rocks overlying older continental crust further east such as the Middle Jurassic – Late Cretaceous Omineca crystalline belt, Idaho batholith, and Boulder Batholith exhibit far greater variation of Pb isotope values that parallel the 100 Ma isochron calculated from a two-stage Pb evolution model. We demonstrate that Pb isotopic results from the Idaho and Boulder Batholith region can be used to define distinctive subregions for Pb isotopic provenance analysis, and compare these signatures to the Mojave-Salinian batholith of southern California and western Arizona, as these two areas have previously been proposed as source regions for extraregional sediment that was deposited within the Nanaimo Basin during the Campanian – Maastrichtian. Future Pb isotopic analysis of detrital K-feldspar from the Nanaimo Basin of southwestern British Columbia may effectively distinguish between potential extraregional sources separated by thousands of kilometres.

Coast Plutonic Complex↗
DOE OSTI2023

Exploring the stellar populations of backsplash galaxies

Backsplash galaxies are those that traverse and overshoot cluster cores as they fall into these structures. They are affected by environment, and should stand out in contrast to the infalling population. We target galaxies in the vicinity of clusters (R≳R 200 ) and select a sample in projected phase space (PPS), from the compilation of Sampaio et al. based on SDSS data. We present a statistical analysis, comparing two regions in PPS, with the same projected distance to the cluster but different velocity. The analysis relies on the presence of variations in the stellar population content of backsplash galaxies. We find a lower limit in the fractional contribution of ∼5 per cent with respect to the general sample of infalling galaxies at similar group-centric distance when using single line strength analysis, or ∼15-30 per cent when adopting bivariate distributions. The stellar populations show a subtle but significant difference towards older ages, and a higher fraction of quiescent galaxies. We also compare this set with a general field sample, where a substantially larger difference in galaxy properties is found, with the field sample being consistently younger, metal poorer and with a lower fraction of quiescent galaxies. Noting that our ‘cluster’ sample is located outside of the virial radius, we expect this difference to be caused by pre-processing of the infalling galaxies in the overall higher density regions.

79 ASTRONOMY AND ASTROPHYSICS↗