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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

BASTet (Berkeley Analysis and Storage Toolkit)

BASTet is an advanced software library written in Python. BASTet serves as the analysis and storage library for the OpenMSI project. BASTet is an integrate framework for: i) storage of spectral imaging data, ii) storage of derived analysis data, iii) provenance of analyses, iv) integration and execution of analyses via complex workflows. BASTet implements the API for the HDF5 storage format used by OpenMSI. Analyses that are developed using BASTet benefit from direct integration with storage format, automatic tracking of provenance, and direct integration with command-line and workflow execution tools. BASTet also defines interfaces to enable developers to directly integrate their analysis with OpenMSI's web-based viewing infrastruture without having to know OpenMSI. BASTet also provides numerous helper classes and tools to assist with the conversion of data files, ease parallel implementation of analysis algorithms, ease interaction with web-based functions, description methods for data reduction. BASTet also includes detailed developer documentation, user tutorials, iPython notebooks, and other supporting documents.

Bowen, Benjamin↗
DOE OSTI2015

Passive neutron coincidence counting with plastic scintillators for the characterization of radioactive waste drums

The quantification of radioactive material is essential in the fields of safeguards, criticality control of nuclear processes, dismantling of nuclear facilities and components, or radioactive waste characterization. The Nuclear Measurement Laboratory (LMN) of CEA is involved in the development of time-correlated neutron detection techniques using plastic scintillators. Usually, 3He proportional counters are used for passive neutron coincidence counting owing to their high thermal neutron capture efficiency and gamma insensitivity. However, the global {sup 3}He shortage in the past few years has made these detectors extremely expensive. In addition, contrary to {sup 3}He counters for which a few tens of microseconds are needed to thermalize fast neutrons, in view to maximize the {sup 3}He(n,p){sup 3}H capture cross section, plastic scintillators are based on elastic scattering and therefore the light signal is formed within a few nanoseconds, correlated pulses being detected within a few dozen- or hundred nanoseconds. This time span reflects fission particles time of flight, which allows reducing accordingly the duration of the coincidence gate and thus the rate of random coincidences, which may totally blind fission coincidences when using {sup 3}He counters in case of a high (α,n) reaction rate. However, plastic scintillators are very sensitive to gamma rays, requiring the use of a thick metallic shield to reduce the corresponding background. Cross talk between detectors is also a major issue, which consists on the detection of one particle by several detectors due to elastic or inelastic scattering, leading to true but undesired coincidences. Data analysis algorithms are tested to minimize cross-talk in simultaneously activated detectors. The distinction between useful fission coincidences and the correlated background due to cross-talk, (α,n) and induced (n,2n) or (n,n'γ) reactions, is achieved by measuring 3-fold coincidences. The performances of a passive neutron coincidence counting system for radioactive waste drums using plastic scintillators have been studied using the Monte Carlo radiation transport code MCNPX-PoliMi v2.0 coupled to data processing algorithms developed with ROOT data analysis software. In addition to the correlated background, accidental coincidences are taken into account in the simulation by randomly merging pulses from different calculations with fission and (α,n) sources. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Halo-independent analysis of direct dark matter detection through electron scattering

Sub-GeV mass dark matter particles whose collisions with nuclei would not deposit sufficient energy to be detected, could instead be revealed through their interaction with electrons. Analyses of data from direct detection experiments usually require assuming a local dark matter halo velocity distribution. In the halo-independent analysis method, properties of this distribution are instead inferred from direct dark matter detection data, which allows then to compare different data without making any assumption on the uncertain local dark halo characteristics. Furthermore, this method has so far been developed for and applied to dark matter scattering off nuclei. Here we demonstrate how this analysis can be applied to scattering off electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Plant metacaspase: A case study of microcrystal structure determination and analysis

Metacaspases are highly conserved in plants and play essential roles in mediating programmed cell death, biotic and abiotic stress responses, and damage-induced innate immunity. Ca 2+ signaling induced by plant damage leads to activation of metacaspase from Arabidopsis thaliana (AtMC4), which subsequently processes a plant elicitor peptide to trigger downstream immuno-response. To understand the structural basis of AtMC4 activation by Ca 2+ , we previously determined its crystal structure and performed in-crystal Ca 2+ treatment to probe activation-associated conformational changes. To enable structure determination and in-crystal Ca 2+ activation analysis, we used microcrystals and related methods which were essential for our successful approach. Here, in this paper, we describe in detail the methods that we used for determination of AtMC4 structure using single-wavelength isomorphous replacement with anomalous signals assembled from 22 microcrystals. We also describe the method for in-crystal Ca 2+ soaking, microcrystal data collection, data assembly and analysis to obtain the activated structure of AtMC4 from 91 micro-sized crystals. The described methods may be useful to study other plant metacaspases and more broadly other plant enzymes for their structure determination and in-crystal functional characterization.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Energy Finite Element Analysis for Computing the High Frequency Vibration of the Aluminum Testbed Cylinder and Correlating the Results to Test Data

The Energy Finite Element Analysis (EFEA) is a finite element based computational method for high frequency vibration and acoustic analysis. The EFEA solves with finite elements governing differential equations for energy variables. These equations are developed from wave equations. Recently, an EFEA method for computing high frequency vibration of structures either in vacuum or in contact with a dense fluid has been presented. The presence of fluid loading has been considered through added mass and radiation damping. The EFEA developments were validated by comparing EFEA results to solutions obtained by very dense conventional finite element models and solutions from classical techniques such as statistical energy analysis (SEA) and the modal decomposition method for bodies of revolution. EFEA results have also been compared favorably with test data for the vibration and the radiated noise generated by a large scale submersible vehicle. The primary variable in EFEA is defined as the time averaged over a period and space averaged over a wavelength energy density. A joint matrix computed from the power transmission coefficients is utilized for coupling the energy density variables across any discontinuities, such as change of plate thickness, plate/stiffener junctions etc. When considering the high frequency vibration of a periodically stiffened plate or cylinder, the flexural wavelength is smaller than the interval length between two periodic stiffeners, therefore the stiffener stiffness can not be smeared by computing an equivalent rigidity for the plate or cylinder. The periodic stiffeners must be regarded as coupling components between periodic units. In this paper, Periodic Structure (PS) theory is utilized for computing the coupling joint matrix and for accounting for the periodicity characteristics.

Vlahopoulos, Nickolas↗
NASA NTRS1976

Application of the vortex-lattice technique to the analysis of thin wings with vortex separation and thick multi-element wings

Two techniques for extending the range of applicability of the basic vortex-lattice method are discussed. The first improves the computation of aerodynamic forces on thin, low-aspect-ratio wings of arbitrary planforms at subsonic Mach numbers by including the effects of leading-edge and tip vortex separation, characteristic of this type wing, through use of the well-known suction-analogy method of E. C. Polhamus. Comparisons with experimental data for a variety of planforms are presented. The second consists of the use of the vortex-lattice method to predict pressure distributions over thick multi-element wings (wings with leading- and trailing-edge devices). A method of laying out the lattice is described which gives accurate pressures on the top and part of the bottom surface of the wing. Limited comparisons between the result predicted by this method, the conventional lattice arrangement method, experimental data, and 2-D potential flow analysis techniques are presented.

Smith, C. W.↗
NASA NTRS1981

Comments on settling chamber design for quiet, blowdown wind tunnels

Transfer of an existing continous circuit supersonic wind tunnel to Langley and its operation there as a blowdown tunnel is planned. Flow disturbance requirements in the supply section and methods for reducing the high level broad band acoustic disturbances present in typical blowdown tunnels are reviewed. Based on recent data and the analysis of two blowdown facilities at Langley, methods for reducing the total turbulence levels in the settling chamber, including both acoustic and vorticity modes, to less than one percent are recommended. The pertinent design details of the damping screens and honeycomb and the recommended minimum pressure drop across the porous components providing the required two orders of magnitude attenuation of acoustic noise levels are given. A suggestion for the support structure of these high pressure drop porous components is offered.

Beckwith, I. E.↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Characterization of ceramic powders by an X-ray measuring method

X-ray line broadening analysis gives quantitative data on structural changes of ceramic powders after different processing steps. Various Al2O3 powders were investigated and the following points are discussed on the basis of these results: X-ray line broadening analysis, structural changes during grinding, structural changes during annealing, influence of structural properties on sintering behavior and application of line broadening analysis to quality control of powders.

Ziegler, B.↗
NASA NTRS1991

Doppler indices of gas phase formation in hypobaric environments: Time-intensity analysis

A semi-quantitative method to analyze decompression data is described. It possesses the advantage that it allows a graded response to decompression rather than the dichotomous response generally employed. A generalized critical volume (C-V), or stoichiometric time-dependent equilibrium model is examined that relates the constant of the equation P sub i equals m P sub f plus b to variable tissue supersaturation and gas washout terms. The effects of the tissue ratio on gas phase formation indicate that a decreased ratio yields fewer individuals with Doppler detectable gas bubbles, but those individuals still present with Spencer Grade 3 or 4. This might indicate a local collapse of tissue saturation. The individuals with Grade 3 or 4 could be at risk for type 2 decompression sickness by transpulmonic arterialization. The primary regulator of the problems of decompression sickness is the reduction of local supersaturation, presumably governed by the presence and number of gas micronuclei. It is postulated that a reduction in these nuclei will favor a low incidence of decompression sickness in microgravity secondary to hypokinesia and adynamia.

Powell, Michael R.↗
DOE OSTI2016

New ORNL Method Could Unleash Solar Power Potential

Measurement and data analysis techniques developed at the Department of Energy’s Oak Ridge National Laboratory could provide new insight into performance-robbing flaws in crystalline structures, ultimately improving the performance of solar cells.

14 SOLAR ENERGY↗
DOE OSTI2016

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Inventory Uncertainty Quantification using TENDL Covariance Data in Fispact-II

The new inventory code Fispact-II provides predictions of inventory, radiological quantities and their uncertainties using nuclear data covariance information. Central to the method is a novel fast pathways search algorithm using directed graphs. The pathways output provides (1) an aid to identifying important reactions, (2) fast estimates of uncertainties, (3) reduced models that retain important nuclides and reactions for use in the code's Monte Carlo sensitivity analysis module. Described are the methods that are being implemented for improving uncertainty predictions, quantification and propagation using the covariance data that the recent nuclear data libraries contain. In the TENDL library, above the upper energy of the resolved resonance range, a Monte Carlo method in which the covariance data come from uncertainties of the nuclear model calculations is used. The nuclear data files are read directly by FISPACT-II without any further intermediate processing. Variance and covariance data are processed and used by FISPACT-II to compute uncertainties in collapsed cross sections, and these are in turn used to predict uncertainties in inventories and all derived radiological data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data

In this report, we present preliminary research into nonparametric clustering methods for multi-source imagery data and quantifying the performance of these models. In many domain areas, data sets do not necessarily follow well-defined and well-known probability distributions, such as the normal, gamma, and exponential. This is especially true when combining data from multiple sources describing a common set of objects (which we call multimodal analysis), where the data in each source can follow different distributions and need to be analyzed in conjunction with one another. This necessitates nonparametric density estimation methods, which allow the data to better dictate the distribution of the data. One prominent example of multimodal analysis is multimodal image analysis, when we analyze multiple images taken using different radar systems of the same scene of interest. We develop uncertainty analysis methods, which are inherent in the use of probabilistic models but often not taken advance of, to assess the performance of probabilistic clustering methods used for analyzing multimodal images. This added information helps assess model performance and how much trust decision-makers should have in the obtained analysis results. The developed methods illustrate some ways in which uncertainty can inform decisions that arise when designing and using machine learning models.

42 ENGINEERING↗
NASA NTRS2002

Development of an Efficient Binaural Simulation for the Analysis of Structural Acoustic Data

Binaural or "virtual acoustic" representation has been proposed as a method of analyzing acoustic and vibroacoustic data. Unfortunately, this binaural representation can require extensive computer power to apply the Head Related Transfer Functions (HRTFs) to a large number of sources, as with a vibrating structure. This work focuses on reducing the number of real-time computations required in this binaural analysis through the use of Singular Value Decomposition (SVD) and Equivalent Source Reduction (ESR). The SVD method reduces the complexity of the HRTF computations by breaking the HRTFs into dominant singular values (and vectors). The ESR method reduces the number of sources to be analyzed in real-time computation by replacing sources on the scale of a structural wavelength with sources on the scale of an acoustic wavelength. It is shown that the effectiveness of the SVD and ESR methods improves as the complexity of the source increases. In addition, preliminary auralization tests have shown that the results from both the SVD and ESR methods are indistinguishable from the results found with the exhaustive method.

Lalime, Aimee L.↗
DOE OSTI2015

Federal policy documentation and geothermal water consumption: Policy gaps and needs

With U.S. geothermal power production expected to more than triple by 2040, and the majority of this growth expected to occur in arid and water-constrained areas, it is imperative that decision-makers understand the potential long-term limitations to and tradeoffs of geothermal development due to water availability. To this end, water consumption data, including documentation triggered by the National Environmental Policy Act (NEPA) of 1969, production and injection data, and water permit data, were collected from state and federal environmental policy sources in an effort to determine water consumption across the lifecycle of geothermal power plants. Values extracted from these sources were analyzed to estimate water usage during well drilling; to identify sourcing of water for well drilling, well stimulation, and plant operations; and to estimate operational water usage at the plant level. Nevada data were also compared on a facility-by-facility basis with other publicly available water consumption data, to create a complete picture of water usage and consumption at these facilities. This analysis represents a unique method of capturing project-level water data for geothermal projects; however, a lack of statutory and legal requirements for such data and data quality result in significant data gaps, which are also explored

Schroeder, J. N.↗
DOE OSTI2016

The Regional Test Center Data Transfer System

The Regional Test Centers are a group of several sites around the US for testing photovoltaic systems and components related to photovoltaic systems. The RTCs are managed by Sandia National Laboratories. The data collected by the RTCs must be transmitted to Sandia for storage, analysis, and reporting. This document describes the methods that transfer the data between remote sites and Sandia as well as data movement within Sandia’s network. The methods described are in force as of September, 2016.

14 SOLAR ENERGY↗
DOE OSTI2023

Dimensionality reduction using elastic measures

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. Here, in this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

97 MATHEMATICS AND COMPUTING↗