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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

200-IA-1 Operable Unit Human Health Risk and Kd Screen

The purpose of this environmental calculation file (ECF) is to provide the following: Document the data processing and data reduction steps taken to prepare the 200-IA-1 Operable Unit (OU) data set that will be used to calculate the sample-specific screening level human health risk evaluation; Document the data processing and data reduction steps taken to prepare the 200-IA-1 OU data set that will be used to identify analytes that could potentially impact groundwater in the future beneath the 200-IA-1 OU representative waste sites; Document the assumptions, equations, and methodologies used to calculate the screening levels for human health cancer risks and noncancer hazards for each representative waste site assigned to the 200-IA-1 OU. Individual measured soil concentrations from 0 to 4.6 m (15 ft) below ground surface (bgs) (shallow vadose zone) are used to calculate the total excess lifetime cancer risk (ELCR) and hazard index (HI) for the outdoor worker scenario to determine if there is a basis for remedial action. Individual measured soil concentrations from the ground surface to the groundwater table are used for the distribution coefficient (Kd) screen to identify analytes that could potentially impact groundwater in the future beneath the 200-IA-1 OU representative waste sites. This ECF supports DOE/RL-2020-51, 200-IA-1 OU Focused Feasibility Study, under the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). A risk characterization based upon the evaluation of the health risk estimates developed in this ECF will be presented in the focused feasibility study report.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

We present details on the observing strategy, data-processing techniques, and spectroscopic targeting algorithms for the first three years of operation for the Dark Energy Survey Supernova Program (DES-SN). This five-year program using the Dark Energy Camera mounted on the 4 m Blanco telescope in Chile was designed to discover and follow supernovae (SNe) Ia over a wide redshift range (0.05 < z < 1.2) to measure the equation-of-state parameter of dark energy. We describe the SN program in full: strategy, observations, data reduction, spectroscopic follow-up observations, and classification. From three seasons of data, we have discovered 12,015 likely SNe, 308 of which have been spectroscopically confirmed, including 251 SNe Ia over a redshift range of 0.017 < z < 0.85. We determine the effective spectroscopic selection function for our sample and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. The data presented here are used for the first cosmology analysis by DES-SN (“DES-SN3YR”), the results of which are given in Dark Energy Survey Collaboration et al. The 489 spectra that are used to define the DES-SN3YR sample are publicly available at https://des.ncsa.illinois.edu/releases/sn.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Adaptive Methods for Radial Basis Functions

Radial basis functions (RBFs) are a powerful tool for constructing high-order accurate reduced representations of scattered data in arbitrary dimension and on manifolds. We present a method of constructing data approximations in which we utilize a functional tail to capture a global background profile and a RBF neural network (NN) to capture the smaller-scale features. In the RBF NN the RBF centers, matrix shape parameters were selected adaptively for each RBF. We also utilized a geodesic notion of distance on the manifold on which the data lies, e.g., the spherical geodesic for data on the sphere. Although each of these ideas have been been investigated separately in previous works, their combination into a single algorithm is novel. We defined a machine learning problem in which these properties are learned to minimize the data reduction error. We demonstrate the algorithm for applications of scattered data reduction in the plane and on the sphere.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Replication Data for: Reduction of Oxidized Pd/Ag(111) Surfaces by H2: Sensitivity to PdO Island Size and Dispersion

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data Release 1 of the Dark Energy Spectroscopic Instrument

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5 yr spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the Universe between z = 0 and z ≈ 4. DESI’s principal scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI DR1, which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data, which were previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗
DOE OSTI2022

More Tools for Visualization and Analysis of Small-Angle Neutron Scattering Data: Descriptions and Examples

With the adoption of drtsans as the data reduction software for the GP-SANS, Bio-SANS and EQ-SANS instruments at ORNL, tools for data visualization and analysis that can be integrated into drtsans scripts are needed to further improve the user experience. New tools that do not need to be incorporated directly into data reduction scripts can also positively impact users during their experiments. In this report, a new set of tools is presented that complements the previous set released. The set includes tools for both fitting data and for visualizing data.

42 ENGINEERING↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SNAPRed: Reduction of multidimensional neutron time-of-flight diffraction data

SNAP is a neutron time-of-flight diffractometer at the Spallation Neutron Source operated by Oak Ridge National Laboratory. It generates large arrays of neutron detection events that encode the crystalline atomic structure of materials under study. SNAPRed is an application that makes these datasets accessible to end users by orchestrating the process of data reduction while automatically managing the variable neutron instrumentation configuration. It supports arbitrary grouping and masking of individual detector pixels and includes custom-developed data compression approaches to accommodate the large volumes of data generated by the SNAP instrument.

Diffraction↗
DOE OSTI2022

Laser-Induced Spectrochemical Assay for Uranium Enrichment (LISA-UE)

Uranium hexafluoride (UF6) is the uranium compound typically involved in uranium enrichment process. As the first line of defense against nuclear proliferation, accurate determinations of the uranium enrichment ratio in UF6 are critical for materials verification, accounting and safeguards. Shipping gaseous UF6 samples off-site for analysis with mass spectrometry is cumbersome and costly, and results are not available for some time (months). In-field UF6 enrichment assay has the potential to substantially reduce the time, logistics and expense of sample handling. At present, COMPUCEA is the only accepted method for UF6 enrichment assay in the field. Laser-Induced Spectrochemical Assay for Uranium Enrichment (LISA-UE) is an all-optical (based on laser induced plasma emission) analytical technique intended for fieldable, accurate, precise and rapid UF6 enrichment assay. In its operation, laser induced plasma is created directly in the gaseous UF6 sample. Because different U isotopes emit at slightly different wavelengths, the isotopic information of the UF6 sample is inherently encoded in the atomic emission from the plasma. Isotopic emissions from 235U and 238U are measured simultaneously, which eliminate correlated noise from the laser induced plasma. Isotopic information of the UF6 sample can be extracted from the acquired spectrum with theoretical multi-variable non-linear spectral fitting. To date, advances made by the LISA-UE research team include optimization of the spectral window for direct gaseous UF6 enrichment assay with laser induced plasma, development of data reduction algorithms, and demonstrations of the LISA-UE technique with gaseous UF6 samples. In this presentation, the technical aspect of LISA-UE will be overviewed, the data reduction algorithm will be described, and performance of the technique will be discussed.

Chan, George↗
DOE OSTI2023

Developing Methodology to Determine Pu Isotopic Composition by Laser Ablation MC-ICP-MS

This project will develop methodology to analyze the Pu isotope ratio in mixed U-Pu particles by laser ablation MC-ICP-MS. This will involve: 1) testing and validation of the Pu analytical method using mixed U-Pu solutions and Pu doped glasses, 2) isotopic analysis of mixed U-Pu particles by laser ablation MC-ICP-MS, and 3) continued development of the R-based data reduction program. Details regarding the analytical method development and results of QC testing will be output as a deliverable to the IAEA, along with an updated version of the LARA data reduction software.

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

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2023

SonicPy: a suite of programs for ultrasound pulse-echo data acquisition and analysis

Sound speed and elastic constants measurements in solids and liquids are commonly performed using the ultrasound pulse-echo technique. Recent advances have expanded the use of this technique at numerous high pressure synchrotron beamlines and offline laboratories. However, the increased experimental throughput has revealed many limitations in existing software for handling the rapid measurement and the subsequent data-reduction. Here, we report the development of a collection of computer programs for sound speed measurements using the ultrasound pulse-echo technique, compatible with stepped multi-frequency, as well as broadband-pulse, couplant-corrected methods. The programs provide a highly interactive graphical interface, enable efficient measurement, exploration and near real-time analysis of the ultrasound data, and contain features useful for working with samples under high pressure and/or high temperature. The included analysis programs can alleviate the time required for data reduction from hours to less than a minute, allowing users to make timely and informed decisions regarding the appropriate experimental parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Subaru Hyper Suprime-Cam Survey of Cygnus OB2 Complex – I. Introduction, photometry, and source catalogue

ABSTRACT Low-mass star formation inside massive clusters is crucial to understand the effect of cluster environment on processes like circumstellar disc evolution, planet, and brown dwarf formation. The young massive association of Cygnus OB2, with a strong feedback from massive stars, is an ideal target to study the effect of extreme environmental conditions on its extensive low-mass population. We aim to perform deep multiwavelength studies to understand the role of stellar feedback on the IMF, brown dwarf fraction and circumstellar disc properties in the region. We introduce here, the deepest and widest optical photometry of 1.5○ diameter region centred at Cygnus OB2 in r2, i2, z, and Y-filters, using Subaru Hyper Suprime-Cam (HSC). This work presents the data reduction, source catalogue generation, data quality checks, and preliminary results about the pre-main sequence sources. We obtain 713 529 sources in total, with detection down to ∼28, 27, 25.5, and 24.5 mag in r2, i2, z, and Y-band, respectively, which is ∼3 – 5 mag deeper than the existing Pan-STARRS and GTC/OSIRIS photometry. We confirm the presence of a distinct pre-main sequence branch by statistical field subtraction of the central 18 arcmin region. We find the median age of the region as ∼5 ± 2 Myr with an average disc fraction of ∼9 per cent. At this age, combined with A $_V\, \sim$ 6 – 8 mag, we detect sources down to a mass range of ∼0.01–0.17 M⊙. The deep HSC catalogue will serve as the groundwork for further studies on this prominent active young cluster.

Gupta, Saumya (ORCID:0000000161843958)↗