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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 289 records · Page 16

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NASA NTRS1974

Development of design data for graphite reinforced epoxy and polyimide composites

Processing techniques and design data were characterized for a graphite/epoxy composite system that is useful from 75 K to 450 K, and a graphite/polyimide composite system that is useful from 75 K to 589 K. The Monsanto 710 polyimide resin was selected as the resin to be characterized and used with the graphite fiber reinforcement. Material was purchased using the prepreg specification for the design data generation for both the HT-S/710 and HM-S/710 graphite/polyimide composite system. Lamina and laminate properties were determined at 75 K, 297 K, and 589 K. The test results obtained on the skin-stringer components proved that graphite/polyimide composites can be reliably designed and analyzed much like graphite/epoxy composites. The design data generated in the program includes the standard static mechanical properties, biaxial strain data, creep, fatigue, aging, and thick laminate data.

Scheck, W. G.↗

DOE OSTI2025

Electricity Baseline 2022

The Electricity Baseline (2022) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2022" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569193. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; data inventory↗

DOE OSTI2025

Electricity Baseline 2021

The Electricity Baseline (2021) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2021" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569576. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; LCI; Life Cycle↗

DOE OSTI2025

Electricity Baseline 2020

The Electricity Baseline (2020) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2020" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569605. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; LCI; data inventory↗

DOE OSTI2022

Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

NMDC, Microbiome↗

DOE OSTI2016

VERSION 1 OF THE HUBBLE SOURCE CATALOG

The Hubble Source Catalog is designed to help optimize science from the Hubble Space Telescope (HST) by combining the tens of thousands of visit-based source lists in the Hubble Legacy Archive (HLA) into a single master catalog. Version 1 of the Hubble Source Catalog includes WFPC2, ACS/WFC, WFC3/UVIS, and WFC3/IR photometric data generated using SExtractor software to produce the individual source lists. The catalog includes roughly 80 million detections of 30 million objects involving 112 different detector/filter combinations, and about 160,000 HST exposures. Source lists from Data Release 8 of the HLA are matched using an algorithm developed by Budavári and Lubow. The mean photometric accuracy for the catalog as a whole is better than 0.10 mag, with relative accuracy as good as 0.02 mag in certain circumstances (e.g., bright isolated stars). The relative astrometric residuals are typically within 10 mas, with a value for the mode (i.e., most common value) of 2.3 mas. The absolute astrometric accuracy is better than 0{sup ″} .1 for most sources, but can be much larger for a fraction of fields that could not be matched to the PanSTARRS, SDSS, or 2MASS reference systems. In this paper we describe the database design with emphasis on those aspects that enable the users to fully exploit the catalog while avoiding common misunderstandings and potential pitfalls. We provide usage examples to illustrate some of the science capabilities and data quality characteristics, and briefly discuss plans for future improvements to the Hubble Source Catalog.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1996

Experimental and Analytical Study of Two-Phase Flow in Microgravity

Design of the two-phase flow systems which are anticipated to be utilized in future spacecraft thermal management systems requires a knowledge of two-phase flow and heat transfer parameters in reduced gravities. A program has been initiated by NASA to design a two-phase test loop and to perform a series of experiments to study the effect of gravity on the Critical Heat Flux (CHF) and onset of instability. The test loop is also instrumented to generate data for two-phase pressure drop. In addition to low gravity airplane trajectory testing, the experimental program consisted of a set of laboratory tests which were intended to generate data under the bounding conditions (+1 g and -1 g) in order to plan the test matrix. One set of airplane trajectory tests has been performed and several modifications to the test set-up have been identified. Preliminary test results have been used to demonstrate the applicability of the earth gravity models for prediction of the two-phase friction pressure drop.

McQuillen, John B.↗

DOE OSTI2023

Empowering Scientific Discovery Through Computing at the Advanced Photon Source

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS Data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership Computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication.

Parraga, Hannah↗

DOE OSTI2019

Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology

A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate-hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM-generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data-driven approaches for effective thermal transport calculation and the promise of the FEM-generated data analysis for more comprehensive evaluation of metallic alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Agricultural Baseline (BL0) scenario of the 2016 Billion-Ton Report

Scientific reason for data generation: to serve as the reference case for the BT16 volume 1 agricultural scenarios. The agricultural baseline runs from 2015 through 2040; a starting year of 2014 is used. Date the data set was last modified: 02/12/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: simulation was developed without offering a farmgate price to energy crops or residues (i.e., building on both the USDA 2015 baseline and the agricultural census data (USDA NASS 2014). Data generated are .txt output files by year, simulation identifier, county code (1-3109). Instruments used: POLYSYS (version POLYS2015_V10_alt_JAN22B) supplied by the University of Tennessee APAC The quality assurance and quality control that have been applied: • Check for negative planted area, harvested area, production, yield and cost values. • Check if harvested area exceeds planted area for annuals. • Check FIPS codes.

09 BIOMASS FUELS↗

DOE OSTI2016

Agricultural Baseline (BL0) scenario

09 BIOMASS FUELS↗

DOE OSTI2018

SciPhon : a data analysis software for nuclear resonant inelastic X-ray scattering with applications to Fe, Kr, Sn, Eu and Dy

We present the synchrotron radiation technique of nuclear resonant inelastic X-ray scattering (NRIXS), also known as nuclear resonance vibrational spectroscopy or nuclear inelastic scattering, provides a wealth of information on the vibrational properties of solids. It has found applications in studies of lattice dynamics and elasticity, superconductivity, heme biochemistry, seismology, isotope geochemistry and many other fields. It involves probing the vibrational modes of solids by using the nuclear resonance of Mössbauer isotopes such as 57 Fe, 83 Kr, 119 Sn, 151 Eu and 161 Dy. After data reduction, it provides the partial phonon density of states of the Mössbauer isotope that is investigated, as well as many other derived quantities such as the mean force constant of the chemical bonds and the Debye velocity. The data reduction is, however, not straightforward and involves removal of the elastic peak, normalization and Fourier–Log transformation. Furthermore, some of the quantities derived are highly sensitive to details in the baseline correction. A software package and several novel procedures to streamline and hopefully improve the reduction of the NRIXS data generated at sector 3ID of the Advanced Photon Source have been developed. The graphical user interface software is named SciPhon and runs as a Mathematica package. It is easily portable to other platforms and can be easily adapted for reducing data generated at other beamlines. Several tests and comparisons are presented that demonstrate the usefulness of this software, whose results have already been used in several publications. Here, the SciPhon software is used to reduce Kr, Sn, Eu and Dy NRIXS data, and potential implications for interpreting natural isotopic variations in those systems are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2019

Nasa GeneLab Computomics Reveal Horizontal Gene Transfer on International Space Station Environmental Metagenomes

Prokaryotic lifeforms can be observed to demonstrate many keen adaptive advantages, perhaps facilitated by a nature simplistic relative to divergent domains of life. In particular, decompartmentalized gene expression facilitates adaptation by allowing free exchange of genetic material, albeit at the cost of increased susceptibility to genetic damage. Thus, these lifeforms must compensate by embracing diverse investment strategies in an attempt to “brute force” the evolvability equation through precipitous genesis, lean metabolic efficiency, and sheer population. This prokaryotic archetype also enables symbiotic relationships with secondary mobile genetic elements known as plasmids, which have been shown to drive evolution on rapid temporal scales through processes such as conjugation and transformation. This study attempts to decipher whether these mechanisms of horizontal gene transfer (HGT) are major factors in determining prokaryote fitness within a unique isolated environment, the International Space Station (ISS). The ISS Microbial Tracking (MT) project has generated a wealth of data concerning the successive reigns of microbial genera that appear to thrive amidst harsh conditions for life. Despite relatively higher doses of ionizing radiation as compared to Earth, complications associated with microgravity, and the anti-microbial mélange deployed, microbial life still persists in this environment. The NASA GeneLab serves as a data repository and analysis platform to enable researchers to access space flight factor related data. With the use of GeneLab’s modern computational suites (computomics), phylogenetic and functional genomic investigations of HGT events were conducted on the data generated from the MT-1 project. The putative data concerning the plasmid population (plasmidome) of the ISS was algorithmically derived and compared to those of habitats with similar environmental dynamics- such as living quarters and hospitals- to investigate whether these HGT elements may play crucial role(s) in shaping the microbiome of this closed habitat that serves as the only inhabited structure in space.

Bense, Nicholas↗

DOE OSTI2016

Quantifying element incorporation in multispecies biofilms using nanoscale secondary ion mass spectrometry image analysis

Elucidating nutrient exchange in microbial communities is an important step in understanding the relationships between microbial systems and global biogeochemical cycles, but these communities are complex and the interspecies interactions that occur within them are not well understood. Phototrophic consortia are useful and relevant experimental systems to investigate such interactions as they are not only prevalent in the environment, but some are cultivable in vitro and amenable to controlled scientific experimentation. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful, high spatial resolution tool capable of visualizing the metabolic activities of single cells within a biofilm, but quantitative analysis of the resulting data has typically been a manual process, resulting in a task that is both laborious and susceptible to human error. In this work, the authors describe the creation and application of a semiautomated image-processing pipeline that can analyze NanoSIMS-generated data, applied to phototrophic biofilms as an example. The tool employs an image analysis process, which includes both elemental and morphological segmentation, producing a final segmented image that allows for discrimination between autotrophic and heterotrophic biomass, the detection of individual cyanobacterial filaments and heterotrophic cells, the quantification of isotopic incorporation of individual heterotrophic cells, and calculation of relevant population statistics. The authors demonstrate the functionality of the tool by using it to analyze the uptake of 15 N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. The authors found that the degree of 15N incorporation by individual cells was highly variable when labeled with 15 NH 4 + , but much more even when biofilms were labeled with 15 NO 3 – . In the 15 NH 4 + -amended biofilms, the heterotrophic distribution of 15 N incorporation was highly skewed, with a large population showing moderate 15 N incorporation and a small number of organisms displaying very high 15 N uptake. The results showed that analysis of NanoSIMS data can be performed in a way that allows for quantitation of the elemental uptake of individual cells, a technique necessary for advancing research into the metabolic networks that exist within biofilms with statistical analyses that are supported by automated, user-friendly processes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. Here we describe a “follow the tissue” pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Adaptive powertrain control for plugin hybrid electric vehicles

A powertrain control system for a plugin hybrid electric vehicle. The system comprises an adaptive charge sustaining controller; at least one internal data source connected to the adaptive charge sustaining controller; and a memory connected to the adaptive charge sustaining controller for storing data generated by the at least one internal data source. The adaptive charge sustaining controller is operable to select an operating mode of the vehicle's powertrain along a given route based on programming generated from data stored in the memory associated with that route. Further described is a method of adaptively controlling operation of a plugin hybrid electric vehicle powertrain comprising identifying a route being traveled, activating stored adaptive charge sustaining mode programming for the identified route and controlling operation of the powertrain along the identified route by selecting from a plurality of operational modes based on the stored adaptive charge sustaining mode programming.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Accelerating materials science with high-throughput computations and machine learning

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

36 MATERIALS SCIENCE↗