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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

THE GREEN BANK TELESCOPE 350 MHz DRIFT-SCAN SURVEY II: DATA ANALYSIS AND THE TIMING OF 10 NEW PULSARS, INCLUDING A RELATIVISTIC BINARY

We have completed a 350 MHz Drift-scan Survey using the Robert C. Byrd Green Bank Telescope with the goal of finding new radio pulsars, especially millisecond pulsars that can be timed to high precision. This survey covered {approx}10,300 deg{sup 2} and all of the data have now been fully processed. We have discovered a total of 31 new pulsars, 7 of which are recycled pulsars. A companion paper by Boyles et al. describes the survey strategy, sky coverage, and instrumental setup, and presents timing solutions for the first 13 pulsars. Here we describe the data analysis pipeline, survey sensitivity, and follow-up observations of new pulsars, and present timing solutions for 10 other pulsars. We highlight several sources-two interesting nulling pulsars, an isolated millisecond pulsar with a measurement of proper motion, and a partially recycled pulsar, PSR J0348+0432, which has a white dwarf companion in a relativistic orbit. PSR J0348+0432 will enable unprecedented tests of theories of gravity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining

Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. K-means and Bayesian demixing analysis suggests the characteristic response be separated into four components, with hysteretic type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.

big data↗
DOE OSTI2015

Training package 1 for slitting data analysis

This document and accompanying files are intended as a first training package on how to analyze slitting data. The end goal is to have Idaho National Laboratory (INL) personnel trained to analyze future slitting data taken in the INL Hot Cell on clad, Low-Enriched Uranium (LEU) fuel plates. This first data package will cover data analysis for a monolithic material (as compared to a layered material like the clad fuel plates). The additional issues for layered specimens will be covered in a future training package.

42 ENGINEERING↗
DOE OSTI2017

Nuclear Smuggling Detection and Deterrence FY 2016 Data Analysis Annual Report

The National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence (NSDD) has facilitated the installation of more than 3,500 radiation portal monitors (RPMs) at 606 sites in 56 countries worldwide. This collection of RPMs represents the world’s largest network of radiation detectors and provides one element in the defense-in-depth approach that supports the Global Nuclear Detection Architecture. These systems support NSDD’s mission to build partner country capability to deter, detect, and interdict the illicit transport of radiological and fissile material through strategic points of entry and exit at seaports, airports, and border crossings. NSDD works collaboratively with partner countries and international organizations to optimize the operation of these RPMs. The large amount of data provided by NSDD partner countries highlights the close cooperation and partnerships NSDD has built with 56 countries around the world. Thirty-seven of these countries shared at least some RPM-related data with NSDD in fiscal year 2016. This significant level of data sharing is a key element that distinguishes the NSDD office as unique among nuclear nonproliferation programs and initiatives: NSDD can provide specific, objective, data-driven decisions and support for sustaining the radiation detection systems it helped deploy. This data analysis report summarizes and aggregates the RPM data provided to the NSDD office for analysis and review in fiscal year 2016. The data can be used to describe RPM performance and characterize the wide diversity of NSDD deployment sites. For example, NSDD deploys detector systems across sites with natural background radiation levels that can vary by a factor of approximately six from site to site. Some lanes have few occupancies, whereas others have approximately 8,000 occupancies per day and the different types of cargo that travel through a site can result in site-wide alarm rates that range from near 0% at some sites to above 3% at others. Based on the data received, the global average uptime for NSDD RPMs was above 96% for fiscal year 2016. NSDD takes all of these factors into account in making recommendations to partner countries on how to most effectively manage and maintain site operations. NSDD utilizes reports and other information products created by data analysts to efficiently allocate the resources needed to detect and ultimately interdict illicit nuclear and radiological material.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2013

Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)

For the past three years, a large analysis and visualization effort—funded by the Department of Energy’s Office of Biological and Environmental Research (BER), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA)—has brought together a wide variety of industry-standard scientific computing libraries and applications to create Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) to serve the global climate simulation and observational research communities. To support interactive analysis and visualization, all components connect through a provenance application–programming interface to capture meaningful history and workflow. Components can be loosely coupled into the framework for fast integration or tightly coupled for greater system functionality and communication with other components. The overarching goal of UV-CDAT is to provide a new paradigm for access to and analysis of massive, distributed scientific data collections by leveraging distributed data architectures located throughout the world. The UV-CDAT framework addresses challenges in analysis and visualization and incorporates new opportunities, including parallelism for better efficiency, higher speed, and more accurate scientific inferences. Today, it provides more than 600 users access to more analysis and visualization products than any other single source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Self-Organizing Maps and Their Applications to Data Analysis

Self-Organizing Maps(SOMs) are a form of unsupervised neural network that are used for visualization and exploratory data analysis of high dimensional datasets. Our goal was to understand how we can use a SOM to gain insights about datasets. We do this by first understanding the initialization, training, error metrics, and convergence properties of the SOM. Next we discuss the ways to interpret and visualize a Self-Organizing Map. Finally we used real datasets to understand what the Self-Organizing Map can tell us about labeled and unlabeled data. Based on experiments with our datasets we found that the Self-Organizing Map can tell us about the spacing and position of high dimensional clusters, help us find non-linear patterns, and give us insight into the shape of our data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The green bank northern celestial cap pulsar survey. I. Survey description, data analysis, and initial results

We describe an ongoing search for pulsars and dispersed pulses of radio emission, such as those from rotating radio transients (RRATs) and fast radio bursts, at 350 MHz using the Green Bank Telescope. With the Green Bank Ultimate Pulsar Processing Instrument, we record 100 MHz of bandwidth divided into 4096 channels every 81.92 μs. This survey will cover the entire sky visible to the Green Bank Telescope (δ > –40°, or 82% of the sky) and outside of the Galactic Plane will be sensitive enough to detect slow pulsars and low dispersion measure (<30 pc cm{sup –3}) millisecond pulsars (MSPs) with a 0.08 duty cycle down to 1.1 mJy. For pulsars with a spectral index of –1.6, we will be 2.5 times more sensitive than previous and ongoing surveys over much of our survey region. Here we describe the survey, the data analysis pipeline, initial discovery parameters for 62 pulsars, and timing solutions for 5 new pulsars. PSR J0214+5222 is an MSP in a long-period (512 days) orbit and has an optical counterpart identified in archival data. PSR J0636+5129 is an MSP in a very short-period (96 minutes) orbit with a very low mass companion (8 M{sub J}). PSR J0645+5158 is an isolated MSP with a timing residual RMS of 500 ns and has been added to pulsar timing array experiments. PSR J1434+7257 is an isolated, intermediate-period pulsar that has been partially recycled. PSR J1816+4510 is an eclipsing MSP in a short-period orbit (8.7 hr) and may have recently completed its spin-up phase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Gamification and Visualization of Sensor Data Analysis in Research Buildings

The use of video game elements in non-gaming systems, or gamification , has potential value in transforming data analysis. Our study focused on creating a web-based videogame that models two physical test buildings, each of which contains hundreds of sensors. After the application renders the models, the player can walk through the environments and interact with the virtual representations of the sensors inside. Rather than trudging through a database with textual commands and screens full of data, the user can (virtually) walk up to a sensor and view its data graphically. But these features only scratch the surface of what is possible using our new gamification approach. We anticipate being able to show that recent progress in game design techniques and capacities can contribute to the field of analysis through gamification. The net result could be more stimulating, intuitive, user-friendly interfaces, as well as potentially more informative and insightful applications.

Stone, Jackson A↗
DOE OSTI2011

Parallel Index and Query for Large Scale Data Analysis

Modern scientific datasets present numerous data management and analysis challenges. State-of-the-art index and query technologies are critical for facilitating interactive exploration of large datasets, but numerous challenges remain in terms of designing a system for process- ing general scientific datasets. The system needs to be able to run on distributed multi-core platforms, efficiently utilize underlying I/O infrastructure, and scale to massive datasets. We present FastQuery, a novel software framework that address these challenges. FastQuery utilizes a state-of-the-art index and query technology (FastBit) and is designed to process mas- sive datasets on modern supercomputing platforms. We apply FastQuery to processing of a massive 50TB dataset generated by a large scale accelerator modeling code. We demonstrate the scalability of the tool to 11,520 cores. Motivated by the scientific need to search for inter- esting particles in this dataset, we use our framework to reduce search time from hours to tens of seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Low-Dose (10-Gy) Total Skin Electron Beam Therapy for Cutaneous T-Cell Lymphoma: An Open Clinical Study and Pooled Data Analysis

Purpose: Cutaneous T-cell lymphomas (CTCLs) are dominated by mycosis fungoides (MF) and Sézary syndrome (SS), and durable disease control is a therapeutic challenge. Standard total skin electron beam therapy (TSEBT) is an effective skin-directed therapy, but the possibility of retreatments is limited to 2 to 3 courses in a lifetime due to skin toxicity. This study aimed to determine the clinical effect of low-dose TSEBT in patients with MF and SS. Methods and Materials: In an open clinical study, 21 patients with MF/SS stages IB to IV were treated with low-dose TSEBT over <2.5 weeks, receiving a total dose of 10 Gy in 10 fractions. Data from 10 of these patients were published previously but were included in the current pooled data analysis. Outcome measures were response rate, duration of response, and toxicity. Results: The overall response rate was 95% with a complete cutaneous response or a very good partial response rate (<1% skin involvement with patches or plaques) documented in 57% of the patients. Median duration of overall cutaneous response was 174 days (5.8 months; range: 60-675 days). TSEBT-related acute adverse events (grade 1 or 2) were observed in 60% of patients. Conclusions: Low-dose (10-Gy) TSEBT offers a high overall response rate and is relatively safe. With this approach, reirradiation at times of relapse or progression is likely to be less toxic than standard dose TSEBT. It remains to be established whether adjuvant and combination treatments can prolong the beneficial effects of low-dose TSEBT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

The parallelism motifs of genomic data analysis

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or ‘motifs’ that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Toward Real Time Data Analysis for Smart Grids

This paper describes the architecture and design of a novel system for supporting large-scale real-time data analysis for future power grid systems. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components of the grid. As a result, the whole system becomes highly dynamic and requires constant adjusting based on real time data. Even though millions of sensors such as phase measurement units (PMU) and smart meters are being widely deployed, a data layer that can analyze this amount of data in real time is needed. Unlike the data fabric in other cloud services, the data layer for smart grids has some unique design requirements. First, this layer must provide real time guarantees. Second, this layer must be scalable to allow a large number of applications to access the data from millions of sensors in real time. Third, reliability is critical and this layer must be able to continue to provide service in face of failures. Fourth, this layer must be secure. We address these challenges though a scalable system architecture that integrates the I/O and data processing capability in a devise set of devices. Data process operations can be placed anywhere from sensors, data storage devices, to control centers. We further employ compression to improve performance. We design a lightweight compression customized for power grid data. Our system can reduce end-to-end response time by reduce I/O overhead through compression and overlap compression operations with I/O. The initial prototype of our system was demonstrated with several use cases from PNNL’s FPGI and show that our system can provide real time guarantees to a diverse set of applications.

Data Analytics, Power Grid, Big Data↗
DOE OSTI2024

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS 2 ) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS 1 -based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS 2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Extreme-Scale Distribution-Based Data Analysis

Scientists overview and identify regions of interest by transforming data into compact information descriptors that characterize simulation results and allow detailed analysis on demand. Among many existing feature descriptors, statistical information derived from data samples is a promising approach to taming the big data avalanche because data distributionscomputed from a population can compactly describe the presence and characteristics of salient data features with minimal data movement. The ability to computationally summarize and process data using distributions also provides an efficient and representative capture of information that can adjust to size and resource constraints, with the added benefit that uncertainty associated with results can be quantified and communicated. In this project, we have developed a distribution-based data analysis and visualization framework for optimizing in situ processing and analysis of extreme-scale scientific data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Development of a Monte Carlo code for the data analysis of the {sup 18}F(p,α){sup 15}O reaction at astrophysical energies

Novae are astrophysical events (violent explosion) occurring in close binary systems consisting of a white dwarf and a main-sequence star or a star in a more advanced stage of evolution. They are called 'narrow systems' because the two components interact with each other: there is a process of mass exchange with resulting in the transfer of matter from the companion star to the white dwarf, leading to the formation of this last of the so-called accretion disk, rich mainly of hydrogen. Over time, more and more material accumulates until the pressure and the temperature reached are sufficient to trigger nuclear fusion reactions, rapidly converting a large part of the hydrogen into heavier elements. The products of 'hot hydrogen burning' are then placed in the interstellar medium as a result of violent explosions. Studies on the element abundances observed in these events can provide important information about the stages of evolution stellar. During the outbursts of novae some radioactive isotopes are synthesized: in particular, the decay of short-lived nuclei such as {sup 13}N and {sup 18}F with subsequent emission of gamma radiation energy below 511 keV. The gamma rays from products electron-positron annihilation of positrons emitted in the decay of {sup 18}F are the most abundant and the first observable as soon as the atmosphere of the nova starts to become transparent to gamma radiation. Hence the importance of the study of nuclear reactions that lead both to the formation and to the destruction of {sup 18}F. Among these, the {sup 18}F(p,α){sup 15}O reaction is one of the main channels of destruction. This reaction was then studied at energies of astrophysical interest. The experiment done at Riken, Japan, has as its objective the study of the {sup 18}F(p,α){sup 15}O reaction, using a beam of {sup 18}F produced at CRIB, to derive important information about the phenomenon of novae. In this paper we present the experimental technique and the Monte Carlo code developed to be used in the data analysis process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages

This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗