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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-269: Differential Hazard Analysis For Conventional And New Linac Acceptance Testing Procedures

Purpose: New techniques and materials have recently been developed to expedite the conventional Linac Acceptance Testing Procedure (ATP). The new ATP method uses the Electronic Portal Imaging Device (EPID) for data collection and is presented separately. This new procedure is meant to be more efficient then conventional methods. While not clinically implemented yet, a prospective risk assessment is warranted for any new techniques. The purpose of this work is to investigate the risks and establish the pros and cons between the conventional approach and the new ATP method. Methods: ATP tests that were modified and performed with the EPID were analyzed. Five domain experts (Medical Physicists) comprised the core analysis team. Ranking scales were adopted from previous publications related to TG 100. The number of failure pathways for each ATP test procedure were compared as well as the number of risk priority numbers (RPN’s) greater than 100 were compared. Results: There were fewer failure pathways with the new ATP compared to the conventional, 262 and 556, respectively. There were fewer RPN’s > 100 in the new ATP compared to the conventional, 41 and 115. Failure pathways and RPN’s > 100 for individual ATP tests on average were 2 and 3.5 times higher in the conventional ATP compared to the new, respectively. The pixel sensitivity map of the EPID was identified as a key hazard to the new ATP procedure with an RPN of 288 for verifying beam parameters. Conclusion: The significant decrease in failure pathways and RPN’s >100 for the new ATP mitigates the possibilities of a catastrophic error occurring. The Pixel Sensitivity Map determining the response and inherent characteristics of the EPID is crucial as all data and hence results are dependent on that process. Grant from Varian Medical Systems Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Bryan Mound InSAR Analysis U.S. Strategic petroleum Reserve.

The U.S. Strategic Petroleum Reserve (SPR) is a stockpile of emergency crude oil to be tapped into if a disruption in the nation's oil supply occurs. The SPR is comprised of four salt dome sites. Subsidence surveys have been conducted either annually or biennially at all four sites over the life of the program. Monitoring of surface behavior is a first line defense to detecting possible subsurface cavern integrity issues. Over the life of the Bryan Mound site, subsidence rates over abandoned Cavern 3 have continuously been the highest at the site. In an effort to try and understand the subsurface dynamics, specifically over Bryan Mound Cavern 3, historic interferometric synthetic aperture radar (InSAR) data was acquired and processed by TRE Altamira. InSAR involves the processing of multiple satellite synthetic aperture radar scenes acquired across the same location of the Earth's surface at different times to map surface deformation. The analysis of the data has the ability to detect millimeters of motion spanning days, months, year and decades, across specific sites. The intent in regards to the Bryan Mound site was (1) to confirm the higher subsidence rates recorded over abandoned Cavern 3 indicated by land survey and (2) understand the regional surface behavior. This report describes the InSAR analysis results, how those results compare to the historical collection of land survey data, and what additional information the data has provided towards understanding the response recorded at the surface.

02 PETROLEUM↗
DOE OSTI2019

Big Hill 2018 InSAR Analysis U.S. Strategic Petroleum Reserve

The historical subsidence surveys shot over the U.S. Strategic Petroleum Reserve Big Hill site, located in southeastern Texas, have indicated surface uplift since 2002. In order to better understand and substantiate the surface behavior inferred from annual elevation measurements, InSAR (interferometric synthetic aperture radar) data was acquired. InSAR involves the processing of multiple satellite synthetic aperture radar scenes acquired across the same location of the Earth's surface at different times to map surface deformation. The analysis of the data can detect millimeters of motion spanning days, months, year and decades, across specific sites. The InSAR analysis indicates the fastest subsidence rates are over the north central region of the site, specifically centered over caverns 104 and 103. Subsidence rates decrease towards both the west and east, with the western side subsiding at greater rate than the eastern edge. There is some uplift noted, off the site and off the dome to the east. Overall, the subsidence pattern is in line with subsidence behavior expected over a cavern field. In investigating the validity of the uplift measured during the ground surveys it was discovered that reference location can impact results. An exercise was conducted that took the current InSAR data and presented two varying results dependent on the reference location, either on or off the dome. The conclusion was that if the reference is located on the dome, as it has been for years for the ground surveys, the reference location is moving too, giving the appearance of uplift.

02 PETROLEUM↗
DOE OSTI2024

Development of CMOS Sensors for HEP with a US-based foundry

We will present a program to establish the first development and manufacturing of HEP-specific sensors monolithically integrated into a standard CMOS process using a US-based foundry. In collaboration with several US universities the project aims to develop Monolithic Active Pixel Sensors (MAPS) designs implemented in the 90 nm technology node, including simple test structures and multi-pixel arrays, and monolithic CMOS sensors with readout integrated circuits, perform detailed characterization of the detector prototypes and quantify their performance for HEP applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analyses

This presentation demonstrates a spatial analysis workflow to assess data availability for the many components of geologic carbon storage technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. Presented at the FECM NETL Carbon Management Program Review Meeting 2024.

Creason, Christopher↗
DOE OSTI2024

Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analyses

This presentation demonstrates a spatial analysis workflow to assess data availability for the many components of geologic carbon storage technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. Presented at the Geological Society of America Connects 2024 Annual Meeting in Anaheim, California, 22-25 September 2024.

Creason, Christopher↗
DOE OSTI2025

Mapping and Synthesis of International Biomass Supply Assessments

This report, Mapping and Synthesis of International Biomass Supply Assessments (or Global Biomass Resource Assessment) is the first step in a long-term process to assemble data from around the globe into a virtual repository that can be updated and provide user-friendly access to the data. The Clean Energy Ministerial (CEM) Biofuture Platform Initiative recommended that research be completed to “address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies.” To act upon the CEM Biofuture recommendation, in 2024, the U.S. Department of Energy (DOE) commissioned Oak Ridge National Laboratory (ORNL) to prepare this report as the primary deliverable for a one-year assignment to assemble data into a citable form that could help resolve the persistent question presented related to bioenergy policy, “Is there enough sustainable biomass?” In response to that query, this report includes information received by August 2024 from national CEM representatives, collaborators, and public sources on current and future sustainable biomass supplies in 62 nations,and subsequently documents (a) the approach used by ORNL to analyze and categorize the information received in a manner that enables aggregation and comparability; and (b) recommendations for next steps and guidelines to help others update and harmonize future assessments of global sustainable biomass supplies.

09 BIOMASS FUELS↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Ameri, Kimia (ORCID:0000000328791871)↗
DOE OSTI2017

Towards Scalable Deep Learning via I/O Analysis and Optimization

Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Given the importance of deep learning systems, researchers have been investigating techniques to optimize such systems. An area of particular interest has been using large supercomputing systems to quickly generate effective deep learning networks: a phase often referred to as “training” of the deep learning neural network. As we scale existing deep learning frameworks—such as Caffe—on these large supercomputing systems, we notice that the parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall system scalability. In this paper, we first present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. Our analysis shows that the I/O subsystem of Caffe—LMDB—relies on memory-mapped I/O to access its database, which can be highly inefficient on large-scale systems because of its interaction with the process scheduling system and the network-based parallel filesystem. Based on this analysis, we then present LMDBIO, our optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Our experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.

Pumma, Sarunya↗
DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Axisymmetric Radiative Transfer Models of Kilonovae

The detailed observations of GW170817 proved for the first time directly that neutron star mergers are a major production site of heavy elements. The observations could be fit by a number of simulations that qualitatively agree, but can quantitatively differ (e.g., in total r-process mass) by an order of magnitude. We categorize kilonova ejecta into several typical morphologies motivated by numerical simulations, and apply a radiative transfer Monte Carlo code to study how the geometric distribution of the ejecta shapes the emitted radiation. We find major impacts on both spectra and light curves. The peak bolometric luminosity can vary by two orders of magnitude and the timing of its peak by a factor of five. These findings provide the crucial implication that the ejecta masses inferred from observations around the peak brightness are uncertain by at least an order of magnitude. Mixed two-component models with lanthanide-rich ejecta are particularly sensitive to geometric distribution. A subset of mixed models shows very strong viewing angle dependence due to lanthanide "curtaining," which persists even if the relative mass of lanthanide-rich component is small. The angular dependence is weak in the rest of our models, but different geometric combinations of the two components lead to a highly diverse set of light curves. We identify geometry-dependent P Cygni features in late spectra that directly map out strong lines in the simulated opacity of neodymium, which can help to constrain the ejecta geometry and to directly probe the r-process abundances.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

U.S. Wind Energy Manufacturing and Supply Chain: A Competitiveness Analysis

The goal of the project was to develop a greater understanding of the key factors determining wind energy component manufacturing costs and pricing on a global basis in order to enhance the competitiveness of U.S. manufacturers, and to reduce installed systems cost. Multiple stakeholders including DOE, turbine OEMs, and large component manufactures will all benefit by better understanding the factors determining domestic competitiveness in the emerging offshore and next generation land-based wind industries. Major objectives of this project were to: 1. Carry out global cost and process comparisons for 5MW jacket foundations, blades, towers, and permanent magnet generators; 2. Assess U.S. manufacturers’ competitiveness and potential for cost reduction; 3. Facilitate informed decision-making on investments in U.S. manufacturing; 4. Develop an industry scorecard representing the readiness of the U.S. manufacturers’ to produce components for the next generations of wind turbines, nominally 3MW land-based and 5MW offshore; 5. Disseminate results through the GLWN Wind Supply Chain GIS Map, a free website that is the most comprehensive public database of U.S. wind energy suppliers; 6. Identify areas and develop recommendations to DOE on potential R&D areas to target for increasing domestic manufacturing competitiveness, per DOE’s Clean Energy Manufacturing Initiative (CEMI). Lists of Deliverables 1. Cost Breakdown Competitive Analyses of four product categories: tower, jacket foundation, blade, and permanent magnet (PM) generator. The cost breakdown for each component includes a complete Bill of Materials with net weights; general process steps for labor; and burden adjusted by each manufacturer for their process categories of SGA (sales general and administrative), engineering, logistics cost to a common U.S. port, and profit. 2. Value Stream Map Competitiveness Analysis: A tool that illustrates both information and material flow from the point of getting a customer order at the manufacturing plant; to the orders being forwarded by the manufacturing plant to the material suppliers; to the material being received at the manufacturing plant and processed through the system; to the final product being shipped to the Customer. 3. Competitiveness Scorecard: GLWN developed a Wind Industry Supply Chain Scorecard that reflects U.S. component manufacturers’ readiness to supply the next generation wind turbines, 3MW and 5MW, for land-based and offshore applications. 4. Wind Supply Chain Database & Map: Expand the current GLWN GIS Wind Supply Chain Map to include offshore elements. This is an on-line, free access, wind supply chain map that provides a platform for identifying active and emerging suppliers for the land-based and offshore wind industry, including turbine component manufacturers and wind farm construction service suppliers.

17 WIND ENERGY↗
DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗
DOE OSTI2013

THE VIRUS-P EXPLORATION OF NEARBY GALAXIES (VENGA): SURVEY DESIGN, DATA PROCESSING, AND SPECTRAL ANALYSIS METHODS

We present the survey design, data reduction, and spectral fitting pipeline for the VIRUS-P Exploration of Nearby Galaxies (VENGA). VENGA is an integral field spectroscopic survey, which maps the disks of 30 nearby spiral galaxies. Targets span a wide range in Hubble type, star formation activity, morphology, and inclination. The VENGA data cubes have 5.''6 FWHM spatial resolution, {approx}5 A FWHM spectral resolution, sample the 3600 A-6800 A range, and cover large areas typically sampling galaxies out to {approx}0.7R{sub 25}. These data cubes can be used to produce two-dimensional maps of the star formation rate, dust extinction, electron density, stellar population parameters, the kinematics and chemical abundances of both stars and ionized gas, and other physical quantities derived from the fitting of the stellar spectrum and the measurement of nebular emission lines. To exemplify our methods and the quality of the data, we present the VENGA data cube on the face-on Sc galaxy NGC 628 (a.k.a. M 74). The VENGA observations of NGC 628 are described, as well as the construction of the data cube, our spectral fitting method, and the fitting of the stellar and ionized gas velocity fields. We also propose a new method to measure the inclination of nearly face-on systems based on the matching of the stellar and gas rotation curves using asymmetric drift corrections. VENGA will measure relevant physical parameters across different environments within these galaxies, allowing a series of studies on star formation, structure assembly, stellar populations, chemical evolution, galactic feedback, nuclear activity, and the properties of the interstellar medium in massive disk galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Cell-Type Specific DNA Methylation Patterns Define Human Breast Cellular Identity

DNA methylation plays a role in a variety of biological processes including embryonic development, imprinting, Xchromosome inactivation, and stem cell differentiation. Tissue specific differential methylation has also been well characterized. We sought to extend these studies to create a map of differential DNA methylation between different cell types derived from a single tissue. Using three pairs of isogenic human mammary epithelial and fibroblast cells, promoter region DNA methylation was characterized using MeDIP coupled to microarray analysis. Comparison of DNA methylation between these cell types revealed nearly three thousand cell-type specific differentially methylated regions (ctDMRs). MassARRAY was performed upon 87 ctDMRs to confirm and quantify differential DNA methylation. Each of the examined regions exhibited statistically significant differences ranging from 10–70%. Gene ontology analysis revealed the overrepresentation of many transcription factors involved in developmental processes. Additionally, we have shown that ctDMRs are associated with histone related epigenetic marks and are often aberrantly methylated in breast cancer. Overall, our data suggest that there are thousands of ctDMRs which consistently exhibit differential DNA methylation and may underlie cell type specificity in human breast tissue. In addition, we describe the pathways affected by these differences and provide insight into the molecular mechanisms and physiological overlap between normal cellular differentiation and breast carcinogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Complete information acquisition in scanning probe microscopy

In the last three decades, scanning probe microscopy (SPM) has emerged as a primary tool for exploring and controlling the nanoworld. A critical part of the SPM measurements is the information transfer from the tip-surface junction to a macroscopic measurement system. This process reduces the many degrees of freedom of a vibrating cantilever to relatively few parameters recorded as images. Similarly, the details of dynamic cantilever response at sub-microsecond time scales of transients, higher-order eigenmodes and harmonics are averaged out by transitioning to millisecond time scale of pixel acquisition. Hence, the amount of information available to the external observer is severely limited, and its selection is biased by the chosen data processing method. Here, we report a fundamentally new approach for SPM imaging based on information theory-type analysis of the data stream from the detector. This approach allows full exploration of complex tip-surface interactions, spatial mapping of multidimensional variability of material s properties and their mutual interactions, and SPM imaging at the information channel capacity limit.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2011

WHAT DRIVES THE EXPANSION OF GIANT H II REGIONS?: A STUDY OF STELLAR FEEDBACK IN 30 DORADUS

Observations show that star formation is an inefficient and slow process. This result can be attributed to the injection of energy and momentum by stars that prevents free-fall collapse of molecular clouds. The mechanism of this stellar feedback is debated theoretically; possible sources of pressure include the classical warm H II gas, the hot gas generated by shock heating from stellar winds and supernovae, direct radiation of stars, and the dust-processed radiation field trapped inside the H II shell. In this paper, we measure observationally the pressures associated with each component listed above across the giant H II region 30 Doradus in the Large Magellanic Cloud. We exploit high-resolution, multi-wavelength images (radio, infrared, optical, ultraviolet, and X-ray) to map these pressures as a function of position. We find that radiation pressure dominates within 75 pc of the central star cluster, R136, while the H II gas pressure dominates at larger radii. By contrast, the dust-processed radiation pressure and hot gas pressure are generally weak and not dynamically important, although the hot gas pressure may have played a more significant role at early times. Based on the low X-ray gas pressures, we demonstrate that the hot gas is only partially confined and must be leaking out the H II shell. Additionally, we consider the implications of a dominant radiation pressure on the early dynamics of 30 Doradus.

79 ASTRONOMY AND ASTROPHYSICS↗