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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Using integrated data analysis to extend measurement capability (invited)

The analysis approach called integrated data analysis (IDA) provides a means to exploit all information present in multiple streams of raw data to produce the best inference of a plasma parameter. This contrasts with the typical approach in which information (data) from a single diagnostic is used to measure a given parameter, e.g., visible bremsstrahlung → Z eff . Data from a given diagnostic usually contain information on many parameters. For example, a Thomson scattering diagnostic is sensitive to bremsstrahlung and line emission in addition to electron temperature. This background light is typically subtracted off and discarded but could be used to improve knowledge of Z eff . IDA encourages explicit awareness of such information and provides the quantitative framework to exploit it. This gives IDA the ability to increase spatial and temporal resolution, increase precision and accuracy of inferences, and measure plasma parameters that are difficult or impossible to measure using single diagnostic techniques. One example is the measurement of Z eff on Madison symmetric torus using IDA since no single diagnostic can provide a robust measurement. Thus, as we enter the burning plasma era, application of IDA will be critical to the measurement of certain parameters, as diagnostic access in the harsh fusion environment will be extremely limited.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Design and implementation of a data analysis pipeline for paper spray ionization mass spectrometry of UO 2 Cl 2

Ambient mass spectrometry is an emerging characterization method for nuclear materials analysis. SRNL has recently acquired a new JEOL AccuTOF™-DART® LP 4G with a Filter Spray™ front end sample injection system, to develop new methods for ambient mass spectrometry of uranium analytes. Preliminary measurements of uranium-containing samples have demonstrated a need to rapidly evaluate large datasets generated with tailored data analysis scripts. To address this gap a data analysis pipeline written in R within the R Studio environment has been developed. The pipeline comprises a series of processing steps where the data are ingested, analyte peaks of interest are extracted, subsequent ratio and normalization calculations are performed, and results are presented in a series of graphical plots. In detail, the data files containing up to several hundred chronological spectra of a single sample in netCDF format were first extracted into RStudio. Then, initial 3D plots of the data were created in respect to the time, intensity, and mass to charge domains. These graphics serve as an initial easily visualized survey of instrument data. Next, a .csv format file containing the names, masses, and abundances of isotopes of interest were ingested into RStudio and were used to automatically collect intensity values at the mass value. Uranium isotope ratios were plotted against gadolinium isotope ratios (gadolinium was used as an internal standard) and assessed for ion beam stability and intensity. The spectra were filtered by applying a threshold intensity for the 158GdO+ mass channel. A Monte Carlo experiment was performed by creating a theoretical distribution of the data in order to determine the ratio of the uranium isotopes. The results show that the developed data analysis pipeline successfully filters high quality data from overall poor quality runs. Our results also support the idea that paper spray ionization mass spectrometry can be used for both detection and quantification of trace amounts of uranium compounds.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Cloud Based Analytical Framework for Synchrophasor Data Analysis

The paper presents initial results from the development of a cloud-based, Big Data analysis platform for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Actual examples of power system events detection using synchrophasor data are presented. It has been shown that applications of the cloud based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

PMU, Machine Learning, Big Data, SPARK, Event Dete↗
DOE OSTI2015

Simplified Processing Method for Meter Data Analysis

Simple/Quick metered data processing method that can be used for Army Metered Data Management System (MDMS) and Logistics Innovation Agency data, but may also be useful for other large data sets. Intended for large data sets when analyst has little information about the buildings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Astrophysical data analysis with information field theory

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principal components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

THREE-DIMENSIONAL RADIO AND X-RAY MODELING AND DATA ANALYSIS SOFTWARE: REVEALING FLARE COMPLEXITY

Many problems in solar physics require analysis of imaging data obtained in multiple wavelength domains with differing spatial resolution in a framework supplied by advanced three-dimensional (3D) physical models. To facilitate this goal, we have undertaken a major enhancement of our IDL-based simulation tools developed earlier for modeling microwave and X-ray emission. The enhanced software architecture allows the user to (1) import photospheric magnetic field maps and perform magnetic field extrapolations to generate 3D magnetic field models; (2) investigate the magnetic topology by interactively creating field lines and associated flux tubes; (3) populate the flux tubes with user-defined nonuniform thermal plasma and anisotropic, nonuniform, nonthermal electron distributions; (4) investigate the spatial and spectral properties of radio and X-ray emission calculated from the model; and (5) compare the model-derived images and spectra with observational data. The package integrates shared-object libraries containing fast gyrosynchrotron emission codes, IDL-based soft and hard X-ray codes, and potential and linear force-free field extrapolation routines. The package accepts user-defined radiation and magnetic field extrapolation plug-ins. We use this tool to analyze a relatively simple single-loop flare and use the model to constrain the magnetic 3D structure and spatial distribution of the fast electrons inside this loop. We iteratively compute multi-frequency microwave and multi-energy X-ray images from realistic magnetic flux tubes obtained from pre-flare extrapolations, and compare them with imaging data obtained by SDO, NoRH, and RHESSI. We use this event to illustrate the tool's use for the general interpretation of solar flares to address disparate problems in solar physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

3rd Annual Earth System Grid Federation and 3rd Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Face-to-Face Meeting Report December 2013

The climate and weather data science community gathered December 3–5, 2013, at Lawrence Livermore National Laboratory, in Livermore, California, for the third annual Earth System Grid Federation (ESGF) and Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Meeting, which was hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UV-CDAT are global collaborations designed to develop a new generation of open-source software infrastructure that provides distributed access and analysis to observed and simulated data from the climate and weather communities. The tools and infrastructure developed under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change, while the F2F meetings help to build a stronger climate and weather data science community and stronger federated software infrastructure. The 2013 F2F meeting determined requirements for existing and impending national and international community projects; enhancements needed for data distribution, analysis, and visualization infrastructure; and standards and resources needed for better collaborations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Open Reproducible Electron Microscopy Data Analysis

Electron microscopy (EM) is a cornerstone technique in the materials and biological sciences capable of imaging structures at nano- to atomic-scale resolution. Advances in technologies mean that one acquires datasets at increasing data rates and sizes. These advancements present enormous opportunities for researchers to understand complex systems. However, processing the resulting large-scale, complex data in a reproducible and shareable way is a real challenge for researchers. The building, managing, and maintaining complex workflows in a reproducible manner requires extensive knowledge in several areas outside the researchers’ core skill sets, such as software engineering, data science, and high-performance computing (HPC). Our work demonstrates an innovative approach to solving these problems, enabling reproducible EM data analysis through container encapsulated pipelines. Using modern container technologies, we encapsulate processing elements and connect them using shared memory. We expose user-friendly, advanced algorithms and tools to allow end users to utilize without expert programming skills. The platform enables reproducible, scalable, shareable pipelines for the analysis and visualization of EM data. Focusing on interoperability, we leverage the DOE and other agencies’ existing investments to provide a powerful software platform for EM data analysis.

Harris, Christopher↗
DOE OSTI2014

Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Final Report

A partnership across government, academic, and private sectors has created a novel system that enables climate researchers to solve current and emerging data analysis and visualization challenges. The Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) software project utilizes the Python application programming interface (API) combined with C/C++/Fortran implementations for performance-critical software that offers the best compromise between "scalability" and “ease-of-use.” The UV-CDAT system is highly extensible and customizable for high-performance interactive and batch visualization and analysis for climate science and other disciplines of geosciences. For complex, climate data-intensive computing, UV-CDAT’s inclusive framework supports Message Passing Interface (MPI) parallelism as well as taskfarming and other forms of parallelism. More specifically, the UV-CDAT framework supports the execution of Python scripts running in parallel using the MPI executable commands and leverages Department of Energy (DOE)-funded general-purpose, scalable parallel visualization tools such as ParaView and VisIt. This is the first system to be successfully designed in this way and with these features. The climate community leverages these tools and others, in support of a parallel client-server paradigm, allowing extreme-scale, server-side computing for maximum possible speed-up.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Visualizing nD Point Clouds as Topological Landscape Profiles to Guide Local Data Analysis

Analyzing high-dimensional point clouds is a classical challenge in visual analytics. Traditional techniques, such as projections or axis-based techniques, suffer from projection artifacts, occlusion, and visual complexity.We propose to split data analysis into two parts to address these shortcomings. First, a structural overview phase abstracts data by its density distribution. This phase performs topological analysis to support accurate and non-overlapping presentation of the high-dimensional cluster structure as a topological landscape profile. Utilizing a landscape metaphor, it presents clusters and their nesting as hills whose height, width, and shape reflect cluster coherence, size, and stability, respectively. A second local analysis phase utilizes this global structural knowledge to select individual clusters or point sets for further, localized data analysis. Focusing on structural entities significantly reduces visual clutter in established geometric visualizations and permits a clearer, more thorough data analysis. In conclusion, this analysis complements the global topological perspective and enables the user to study subspaces or geometric properties, such as shape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

An integrated data analysis tool for improving measurements on the MST RFP

Many plasma diagnostics contain complementary information. For example, the double-foil soft x-ray system (SXR) and the Thomson Scattering diagnostic (TS) on the Madison Symmetric Torus both measure electron temperature. The complementary information from these diagnostics can be combined using a systematic method based on integrated data analysis techniques, leading to more accurate and sensitive results. An integrated data analysis tool based on Bayesian probability theory was able to estimate electron temperatures that are consistent with both the SXR and TS diagnostics and more precise than either. A Markov Chain Monte Carlo analysis to increase the flexibility of the tool was implemented and benchmarked against a grid search method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Interlaboratory Reproducibility of Contour Method Data Analysis and Residual Stress Calculation

While the contour method for residual stress assessment has developed rapidly, no published study documents its interlaboratory reproducibility. Objective: Here we report an initial reproducibility experiment focused on contour method data analysis and residual stress calculation. The experiment uses surface topography data from a physical process simulation of elastic-plastic beam bending. The simulation provides surface topography, for input to the contour method data analysis, as well as a known residual stress field with 130 MPa peak magnitude. Additionally, to increase realism, noise and specific artifacts are added to the topography data. A group of participants received the topography data (without the known residual stress), independently analyzed the data, and submitted results as a two-dimensional residual stress field. Analysis of submissions provides a group average residual stress field and the spatial distribution of reproducibility standard deviation. The group average residual stress agrees with the known stress in magnitude and spatial trend. The reproducibility standard deviation ranges from 2 to 54 MPa over the measurement plane, with an average of 5.4 MPa. Reproducibility standard deviation is smaller in the cross-section interior (≤ 5 MPa), modest near local extrema in the stress field (5 to 10 MPa), and larger near the cross-section boundaries (10 to 30 MPa). Overall, the largest values of reproducibility standard deviation (up to 54 MPa) occur in limited areas where artifacts had been added to the topography data; while some participants identified and removed these artifacts, some did not, leading to systematic differences that elevated the standard deviation.

42 ENGINEERING↗
DOE OSTI2018

Aggregation and data analysis of corrosion studies in molten chloride and fluoride salts

Corrosion studies in molten fluoride and chloride salts were surveyed, and key data were aggregated into a single dataset. Studies were graphed by salt purity, temperature, sample material, container material, and experimental method (loop, capsule, crucible). To elucidate and quantify the factors that affect corrosion in molten salts, a data analysis using a newly-defined corrosion resistance score and a correlation analysis using techniques borrowed from machine learning were performed. It is shown that salt purity had the strongest correlation with corrosion rates in molten chlorides and fluorides. As a result, data analysis across varied works was inconsistent due to the lack of standardization among molten salt corrosion studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗