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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

An exploration of heterogeneity in supernova type Ia samples

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that the Panstarrs dataset is the most stable of the three to changes in the data, although it does not, at the moment, go to high enough redshifts to tightly constrain the equation of state of dark energy, w . The Union2.1, drawn from several different sources, appears to be somewhat susceptible to changes within the dataset. The JLA reconstructs well for a smaller number of cosmological parameters. At higher degrees of freedom, the dependence of its errors on redshift can lead to varying results between subsets. Panstarrs is inconsistent with the other two datasets at about 2σ confidence level, and JLA and Union2.1 are about 1σ away from each other. For the Ω{sub 0} {sub m} − w cosmological reconstruction, with no additional data, the 1σ range of values in w for selected subsets of each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs. The range in Ω{sub 0} {sub m} for the same subsets remains approximately similar for all three datasets. We find that although there are differences in the fitting and correction techniques used in the different samples, the most important criterion is the selection of the SNe, a slightly different SNe selection can lead to noticeably different results both in the purely statistical analysis and in cosmological reconstruction. We note that a single, high quality low redshift sample could help decrease the uncertainties in the result. We also note that lack of homogeneity in the magnitude errors may bias the results and should either be modeled, or its effect neutralized by using other, complementary datasets. A supernova sample with high quality data at both high and low redshifts, constructed from a few surveys to avoid heterogeneity in the sample, and with homogeneous errors, would result in a more robust cosmological reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

Trajectory selection for the Mariner Jupiter/Saturn 1977 Project

This paper describes the use of decision analysis to facilitate a group decision-making problem in the selection of trajectories for the two spacecraft of the Mariner Jupiter/Saturn 1977 Project. This NASA project includes the participation of some eighty scientists divided by specialization among eleven science teams. A set of thirty-two candidate trajectory pairs was developed by the Project in collaboration with the science teams. Each science team then ordinally ranked and assigned cardinal utility function values to the trajectory pairs. These data and statistics derived from collective choice rules were used by the scientists in selecting the preferred trajectory pair.

Dyer, J. S.↗
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
DOE OSTI2017

An assessment of total depletion uncertainties for a GBC-32 dry storage cask with PLUS7 fuels using Monte Carlo sampling and two-way ANOVA F-test

It is important to accurately evaluate depletion uncertainties that exist due to the limitations of a computer code on accuracy in a nuclear depletion calculation for criticality safety analysis. In this work, the bias and bias uncertainty in the effective neutron multiplication factor (k{sub eff}) for the generic 32 PWR-assembly burnup credit (GBC-32) cask system were assessed using the Monte Carlo uncertainty sampling method for three different burnups and the isotopic biases and uncertainties obtained with the calculated and measured isotopic concentrations. The PLUS7 used nuclear fuel assemblies discharged from the Hanbit Nuclear Power Plant Unit 3 were assumed to be loaded into the GBC-32 cask. In addition, the statistical F-test in the two-way analysis of variance (ANOVA) was suggested to efficiently estimate the statistically converged total depletion uncertainty in k{sub eff}, with a significant reduction of computing efforts in the Monte Carlo criticality calculations, and it was applied to the GBC-32 cask system after a sensitivity analysis was performed to select the important nuclides having significant contributions to the total depletion uncertainty. With the statistical F-tests in the two-way ANOVA, 100 criticality calculations were needed to get a statistically converged total depletion uncertainty for 10 and 30 GWD/MTU, while 120 criticality calculations were needed for 50 GWD/MTU. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1982

Multispectral determination of soil moisture-2

Soil moisture data obtained using scatterometers, modular multispectral scanners and passive microwave radiometers were revised and grouped into four field cover types for statistical anaysis. Guymon data are grouped as alfalfa, bare, milo with rows perpendicular to the field view, and milo viewed parallel to the field of view. Dalhart data are grouped as bare combo, stubble, disked stubble, and corn field. Summary graphs combine selected analyses to compare the effects of field cover. The analysis for each of the cover types is presented in tables and graphs. Other tables show elementary statistics, correlation matrices, and single variable regressions. Selected eigenvectors and factor analyses are included and the highest correlating sensor typs for each location are summarized.

Estes, J. E.↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
NASA NTRS1972

Further developments in cloud statistics for computer simulations

This study is a part of NASA's continued program to provide global statistics of cloud parameters for computer simulation. The primary emphasis was on the development of the data bank of the global statistical distributions of cloud types and cloud layers and their applications in the simulation of the vertical distributions of in-cloud parameters such as liquid water content. These statistics were compiled from actual surface observations as recorded in Standard WBAN forms. Data for a total of 19 stations were obtained and reduced. These stations were selected to be representative of the 19 primary cloud climatological regions defined in previous studies of cloud statistics. Using the data compiled in this study, a limited study was conducted of the hemogeneity of cloud regions, the latitudinal dependence of cloud-type distributions, the dependence of these statistics on sample size, and other factors in the statistics which are of significance to the problem of simulation. The application of the statistics in cloud simulation was investigated. In particular, the inclusion of the new statistics in an expanded multi-step Monte Carlo simulation scheme is suggested and briefly outlined.

Chang, D. T.↗
DOE OSTI2023

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

On the use of Graphs for Test Sequence Selection

This report demonstrates that applying graph theory techniques provides a way to obtain sufficient statistics in finding errors when testing complex state machines. It discusses how to define the tests, then demonstrates how to automatically generate test suites that diversify test cases, subject to constraints. If included within a continuous integration approach, these constructs provide an unbiased means to systematically check for errors within the latest controller software release.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

The effects of Cr, Co, Al, Mo and Ta on the cyclic oxidation behavior of a prototype cast Ni-base superalloy based on a 2(5) composite statistically designed experiment

A series of cast Ni-base superalloys were systematically varied at selected levels of Co, Cr, Mo, Ta, and Al. The elemental levels varied were Mo, 0 to 4 percent; Cr, 6 to 18 percent; Co, 0 to 20 percent, Ta, 0 to 8 percent; and Al, 3.25 to 6.25 percent. The cyclic oxidation resistance was determined from specific weight change data as a function of time for 1 hr cycles in static air at 1100 C. The significant terms in decreasing order of their importance were Al, Ta, Cr2, Al-Cr, Cr-Co, Co2, Al-Mo, Cr-Mo, Al-Al, and Mo-Ta. The Al term alone accounted for close to 82 percent of the explained variability. The estimating equation showed that the Al level was the most important and should be at its 6.25 wt % maximum value. The Mo and Ta levels should also be at their maximum 4 and 8 wt % respectively. The cobalt composition should be as low as possible, i.e., 0 wt%. The Cr level optimum varies depending on the other 4 levels. The X-ray diffaction results indicate the most protective scales are alumina/aluminate spinel stabilizized with a tri-rutile oxide high in Ta and Mo.

Barrett, C. A.↗
NASA NTRS1983

Gravitational lens effects of a cosmological density of compact objects

Amplification of quasar light by a cosmological density of compact objects causes significant effects on many quasars in magnitude-limited samples. For lens masses solar mass less than 100,000 solar mass the continuum would be amplified by a magnitude or more but the line emission would not. Examination of the UV selected sample of Marshall et al. (1983) gives limits to more than 90 percent statistical confidence of Omega(c) less than 0.1 for a mass between 200 and 100,000 solar mass, where Omega(c) is the mean density of objects of mass M relative to the closure density. Preliminary results from an X-ray selected sample may probe to more than 0.1 solar mass and give a value for Omega(c) of less than one. These limits indicate that the remnants of an early population of massive stars cannot make a cosmologically significant contribution to the mass density of the universe. On a separate topic, recent work on the enhanced surface density of quasars near galaxies due to lensing by stars in the galaxy halos is reviewed.

Canizares, C. R.↗
NASA NTRS1993

Pairing statistics for infrared-luminous galaxies

A knowledge of the pairing fraction of IR-selected galaxies is important in understanding the role of interactions in star formation and nuclear activity. It is clear that interactions are extremely common among the most IR-luminous systems, but results have been inconsistent for less luminous systems; this may result from using peculiar morphology as an indicator of an interaction. Since features associated with milder interactions (e.g., tidal tails) tend to be faint, their detection necessarily depends on the depth of the survey used to identify interacting pairs. We present a sample of IR- and optically-selected galaxies for which we have computed pairing fractions using strictly geometrical, quantitative criteria. For the analysis we used a likelihood ratio technique developed by Keel and van Soest (1992). We find that the pairing fraction for IR-selected galaxies is roughly 30 percent, comparable to that found by Keel and van Soest for optically-selected starbursts. We also detect a strong correlation (r above 0.99) between the 60 micron luminosity of a galaxy sample and its pairing fraction. We conclude that although tidal interactions do not guarantee enhanced activity (AGN or starburst), they can amplify internal properties that would feed such activity.

Gallimore, J. F.↗
NASA NTRS1997

Sulfur in Cometary Dust

The computer-intensive project consisted of the analysis and synthesis of existing data on composition of comet Halley dust particles. The main objective was to obtain a complete inventory of sulfur containing compounds in the comet Halley dust by building upon the existing classification of organic and inorganic compounds and applying a variety of statistical techniques for cluster and cross-correlational analyses. A student hired for this project wrote and tested the software to perform cluster analysis. The following tasks were carried out: (1) selecting the data from existing database for the proposed project; (2) finding access to a standard library of statistical routines for cluster analysis; (3) reformatting the data as necessary for input into the library routines; (4) performing cluster analysis and constructing hierarchical cluster trees using three methods to define the proximity of clusters; (5) presenting the output results in different formats to facilitate the interpretation of the obtained cluster trees; (6) selecting groups of data points common for all three trees as stable clusters. We have also considered the chemistry of sulfur in inorganic compounds.

Fomenkova, M. N.↗
NASA NTRS1992

Remote Sensing Data from CLARET: A Prototype Cart Data Set

A data set containing radiation, meteorological, and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of this experiment, ARM scientists requested archival of subsets or the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. The cases range from 2 to 9.5 h in length. A pyranometer, pyrgeometer, pyrheliometer, and an infrared radiometer constituted the ensemble of instruments that provided surface radiation data. A lidar, radar, and ceilometer observed the cloud geometrical structure, and visual reports and all-sky camera observations were assimilated to provide cloud cover data. Radiosondes, wind profiler, RASS (profiling virtual temperature), microwave radiometers (observing column integrated liquid water and water vapor), and standard surface measurements provided meteorological data. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory. In addition to documenting the data set, this report describes CLARET and gives a bibliography of publications associated with the project. Some preliminary results of CLARET' research are also summarized. Simultaneous CO 2 lidar and radar backscatter measurements were shown to provide estimates of the effective radius of ice particles. Simultaneous radar and infrared radiometer data appear useful for estimating column-integrated numbers and average sizes of ice cloud particles. Ice water content obtained with this method compared favorably with values from another empirical technique using radar data alone. Depolarization of the CO 2 lidar signal from ice clouds was surprisingly small, suggesting that calculation of backscatter from nonspherical particles for this lidar is a tractable problem. Examples are also cited of CO 2 lidar measurements of the effective radius of water cloud drop size distributions and of inference of the size of pristine ice crystals that assume a particular orientation in the air. These parameters are all important to radiative transfer through clouds.

Clouds (Meteorology)↗
DOE OSTI2018

Gaussian kernel density functions for compositional quantification in atom probe tomography

Highlights: • A methodology for compositional quantification from atom probe tomography is proposed. • A probability density function p(x) given discrete data points is constructed. • Each atom approximated by a Gaussian function with a particular smoothing parameter. • Density Kernel functions are used to find the optimum smoothing parameter. • The method is validated by using simulated data of semiconductor materials. - Abstract: Atom probe tomography (APT) has the ability to identify the nature and position of single atoms in a material with an almost 3D atomic resolution. However, the quantification of the material composition requires an appropriate treatment of the discrete APT data. When the amount of atoms selected to quantify the composition is relatively small, the spatial resolution is enhanced but the statistical error worsens. Conversely, the increase in the number of atoms considered reduces the spatial resolution, but improving the statistics. We have developed a methodology to reach an optimum equilibrium between the positional and the statistical error in an unsupervised form using Gaussian Kernel density functions. The validity of the method has been tested using APT simulated data of semiconductor materials. It has been proved that the chemical quantification in these materials requires the appropriate selection of the smoothing parameter, obtained without user intervention. In addition, the results have been compared to the usual techniques for composition measurement from APT data (voxelization and proximity histograms), showing better precision for high spatial resolution. This work supplies a data driven methodology based in Gaussian Kernel density functions for the accurate quantification of the composition from APT data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Pandora-based muon neutrino disappearance search in the SBN program with 1muonNp quasi-elastic like channel

The Short-Baseline Neutrino (SBN) program at Fermilab aims to perform a definitive search for light sterile neutrinos using multiple liquid argon time projection chamber detectors. We present the status of an analysis of muon neutrino charged-current interactions in two SBN detectors (SBND and ICARUS), selecting fully contained events with one muon and at least one proton in the final state, inspired by the recent ICARUS standalone results. This topology-driven selection reduces dependence on neutrino interaction cross-section modeling while preserving high statistics and good neutrino energy resolution, enabled by robust reconstruction and particle identification. Fully contained events enable precise kinematic reconstruction and support relative measurements between detectors, directly addressing the core goals of the SBN program. A detailed evaluation of systematic uncertainties is currently underway, with the objective of reducing the dominant systematics to the percent level. The current status of this analysis, with a particular focus on the event selection performance, will be presented in this poster.

Artero Pons, Maria [Padua U.; INFN, Padua]↗
DOE OSTI2019

Genetic and antigenic variation of foot-and-mouth disease virus during persistent infection in naturally infected cattle and Asian buffalo in India

The role of foot-and-mouth disease virus (FMDV) persistently infected ruminants in initiating new outbreaks remains controversial, and the perceived threat posed by such animals hinders international trade in FMD-endemic countries. In this study we report longitudinal analyses of genetic and antigenic variations of FMDV serotype O/ME-SA/Ind2001d sublineage during naturally occurring, persistent infection in cattle and buffalo at an organised dairy farm in India. The proportion of animals from which FMDV RNA was recovered was not significantly different between convalescent (post-clinical) and sub-clinically infected animals or between cattle and buffalo across the sampling period. However, infectious virus was isolated from a higher proportion of buffalo samples and for a longer duration compared to cattle. Analysis of the P1 sequences from recovered viruses indicated fixation of mutations at the rate of 1.816 x 10 -2 substitution/site/year (s/s/y) (95% CI 1.362–2.31 x 10 -2 s/s/y). However, the majority of point mutations were transitional substitutions. Within individual animals, the mean dN/dS (ω) value for the P1 region varied from 0.076 to 0.357, suggesting the selection pressure acting on viral genomes differed substantially across individual animals. Statistical parsimony analysis indicated that all of the virus isolates from carrier animals originated from the outbreak virus. The antigenic relationship value as determined by 2D-VNT assay revealed fluctuation of antigenic variants within and between carrier animals during the carrier state which suggested that some carrier viruses had diverged substantially from the protection provided by the vaccine strain. This study contributes to understanding the extent of within-host and within-herd evolution that occurs during the carrier state of FMDV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Reconciling Basin-Scale Top-Down and Bottom-Up Methane Emission Measurements for Onshore Oil and Gas Development: Cooperative Research and Development Final Report, CRADA Number CRD-14-572

The overall objective of the Research Partnership to Secure Energy for America (RPSEA)-funded research project is to develop independent estimates of methane emissions using top-down and bottom-up measurement approaches and then to compare the estimates, including consideration of uncertainty. Such approaches will be applied at two scales: basin and facility. At facility scale, multiple methods will be used to measure methane emissions of the whole facility (controlled dual tracer and single tracer releases, aircraft-based mass balance and Gaussian back-trajectory), which are considered top-down approaches. The bottom-up approach will sum emissions from identified point sources measured using appropriate source-level measurement techniques (e.g., high-flow meters). At basin scale, the top-down estimate will come from boundary layer airborne measurements upwind and downwind of the basin, using a regional mass balance model plus approaches to separate atmospheric methane emissions attributed to the oil and gas sector. The bottom-up estimate will result from statistical modeling (also known as scaling up) of measurements made at selected facilities, with gaps filled through measurements and other estimates based on other studies. The relative comparison of the bottom-up and top-down estimates made at both scales will help improve understanding of the accuracy of the tested measurement and modeling approaches. The subject of this CRADA is NREL's contribution to the overall project. This project resulted from winning a competitive solicitation no. RPSEA RFP2012UN001, proposal no. 12122-95, which is the basis for the overall project. This Joint Work Statement (JWS) details the contributions of NREL and Colorado School of Mines (CSM) in performance of the CRADA effort.

54 ENVIRONMENTAL SCIENCES↗