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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Smisc - A collection of miscellaneous functions

A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAs d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).

Landon Sego, PNNL↗
DOE OSTI2015

Smisc - A collection of miscellaneous functions

A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAs d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).

Landon Sego, PNNL↗
NASA NTRS2021

VAPRE: A tool to support the design optimization of planetary entry probe missions

The importance of the atmospheric data collected by planetary entry probes has been emphasized since the results of Galileo in 1995 and are reflected in the interest . The results add to our fundamental understanding about the formation and the evolution of our solar system. Of special interest are the Outer Planets Saturn, Uranus and Neptune, serving as missing puzzle pieces to the existing data sets. In order to maximize the data and science return from those missions, the tool VAPRE is developed to facilitate the design optimization of planetary entry probe missions by increasing the trade-space. Using a data set of interplanetary trajectories, it assesses the change in availability and accessibility of entry sites on the planets with respect to their latitudes depending on the hyperbolic arrival velocity. In this paper, we introduce the motivation behind the implementation of VAPRE, the science and engineering input gathered to bridge the knowledge between both fields, the design structure of the implementation as well as the significance and future development and potential of the tool VAPRE for future mission concept developments for planetary probe and lander missions.

Simon, Amy↗
NASA NTRS2004

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which cutails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1'(approximately 10 km) and coarser resolution equal-angle grids, and are computed for the first seven MODIS wavelengths, ranging from 0.47 through 2.1 microns, and for three broadband wavelengths, 0.3-0.7,0.3-5.0 and 0.7-5.0 microns.

King, Michael D.↗
DOE OSTI2023

Determination of the titanium spectral function from ($e,e'p$) data

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has measured the (e, e'p) cross section in parallel kinematics using a natural titanium target. In this paper, we report the analysis of the dataset obtained in different kinematics for our solid natural titanium target. Data were obtained in a range of missing momentum and missing energy between 15 ≲ p m ≲ 250 MeV / c and 12 ≲ E m ≲ 80 MeV, respectively, and using an electron beam energy of 2.2 GeV. We measured the reduced cross section with ~7% accuracy as a function of both missing momentum and missing energy. Furthermore, our Monte Carlo simulation, including both a model spectral function and the effects of final-state interactions, satisfactorily reproduces the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

A lightweight I/O scheme to facilitate spatial and temporal queries of scientific data analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data postprocessing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Tian, Yuan↗
NASA NTRS1999

A Co-Investigator Project for the Cornell University Cleft Accelerated Plasma Experimental Rocket-CAPER

The CAPER rocket campaign was to follow the SCIFER experiment as a detailed study of the ion acceleration processes in the Cleft Ion Fountain (CIF) above 1000 km altitude. The SCIFER rocket demonstrated that the experiment was feasible and that the CIF acceleration processes on the dayside are different from those observed in the discrete aurora on the nightside. The responsibility of the GI/UAF co-investigator project was to provide the real-time acquisition and display of large-and small-scale ground observations, and satellite solar wind data at the launch control center at Longyearbyen, Svalbard for the determination of the launch conditions and the later interpretation of the rocket observations. The rocket campaign was proposed for January of 1998, but was slipped to January of 1999. The rocket was launched on January 21, 1999 at 06 h 13 m 30 s UT. All of the GI/UAF co-investigator systems functioned well, except the narrow-field TV camera which was to follow the 140 km conjugate of the payload on command from GPS tracking data sent from Andoya. The data were not available during the flight, and the camera tracked the nominal conjugate. Unfortunately, the trajectory was well west of nominal, so no useful narrow-field conjugate data were acquired . In addition, the payload missed the region of more intense precipitation, brighter aurora, stronger currents, and likely large fluxes of transverse ion acceleration. On the other hand, good data were acquired across a region of the ionosphere that appears to have had a double convection boundary because of the IMF switching its z component shortly before launch. These data are important for understanding the reaction of the magnetosphere and ionosphere to changes in the IMF.

Deehr, Charles S.↗
NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗
NASA NTRSDate not supplied

Spacecraft Material Characterization Using Reflectance Spectra Extracted from RGB/IR Color Images

The space environment is detrimental to the exterior of the spacecraft and imposes stringent requirements on spacecraft materials. Space materials are often expected to perform for 15-20 years in the harsh space environment without significant degradation of their optical, electrical, mechanical and thermal properties. Further, as commercial activities increase in the space domain, development of predictive capability for material evolution is critical for agile adaption of novel materials. Therefore, it is very important to understand the effects of the space environments on the spacecraft materials. The Materials on the International Space Station Experiment Flight Facility (MISSE-FF) has flown numerous material samples to investigate the effect of LEO space weather exposure on the performance and durability of materials and devices. We are manifest on the MISSE-16 mission, scheduled to launch in June 2022, to fly fifteen novel and well-characterized materials in the LEO environment for a duration of six months. Changes in spectral reflectivity will be measured in real time throughout the mission with an RGB/IR camera. These time-resolved data will function as a "space-truth" reference for our team's ongoing laboratory-based space weather-material interaction experiments. Correlation of the MISSE-16 data with extensive ground testing of duplicate samples under simulated space weather conditions will enable development of fundamental chemical models for material degradation. This paper discusses preliminary results from the ground test campaign to collect the RGB/IR images for pristine and damaged materials and the development of machine learning algorithms to extract reflectance spectra from the color images.

Jainisha R. Shah↗
NASA NTRSDate not supplied

Spacecraft Material Characterization Using Reflectance Spectra Extracted from RGB/IR Color Images

The space environment is detrimental to the exterior of the spacecraft and imposes stringent requirements on spacecraft materials. Space materials are often expected to perform for 15-20 years in the harsh space environment without significant degradation of their optical, electrical, mechanical and thermal properties. Further, as commercial activities increase in the space domain, development of predictive capability for material evolution is critical for agile adaption of novel materials. Therefore, it is very important to understand the effects of the space environments on the spacecraft materials. The Materials on the International Space Station Experiment Flight Facility (MISSE-FF) has flown numerous material samples to investigate the effect of LEO space weather exposure on the performance and durability of materials and devices. We are manifest on the MISSE-16 mission, scheduled to launch in June 2022, to fly fifteen novel and well-characterized materials in the LEO environment for a duration of six months. Changes in spectral reflectivity will be measured in real time throughout the mission with an RGB/IR camera. These time-resolved data will function as a "space-truth" reference for our team's ongoing laboratory-based space weather-material interaction experiments. Correlation of the MISSE-16 data with extensive ground testing of duplicate samples under simulated space weather conditions will enable development of fundamental chemical models for material degradation. This paper discusses preliminary results from the ground test campaign to collect the RGB/IR images for pristine and damaged materials and the development of machine learning algorithms to extract reflectance spectra from the color images.

Jainisha R. Shah↗
DOE OSTI2018

24.1.3.1.1 – Frisch Grid CZT Spectrometer

During FY18, BNL continued helping FLIR to integrate the instrument prototype, improving the readout software and testing electronics. BNL and LLNL carried out more measurements with the 4x4 arrays in laboratory conditions using several Cs sources and reanalyzed the raw data from the field measurements. LLNL processed the old recalibrated data and new data collected in the lab. These efforts allowed us to understand the cause of the discrepancies observed during the measurements at the Westinghouse Fuel Fabrication Facility (WFFF). The problem was traced back to the original ASIC and DAQ firmware provided by the ASIC developers. A program error and some other deficiencies were identified in the original firmware. These problems, which caused missing some detected events from the data stream, never showed up in the lab environment. So, they were not spotted before the field test. The ASIC designers are currently working to address these problems and will provide the updated firmware for the next field measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2018

24.1.3.1.1 – Frisch Grid CZT Spectrometer

During FY18, BNL continued helping FLIR to integrate the instrument prototype, improving the readout software and testing electronics. BNL and LLNL carried out more measurements with the 4x4 arrays in laboratory conditions using several Cs sources and reanalyzed the raw data from the field measurements. LLNL processed the old recalibrated data and new data collected in the lab. These efforts allowed us to understand the cause of the discrepancies observed during the measurements at the Westinghouse Fuel Fabrication Facility (WFFF). The problem was traced back to the original ASIC and DAQ firmware provided by the ASIC developers. A program error and some other deficiencies were identified in the original firmware. These problems, which caused missing some detected events from the data stream, never showed up in the lab environment. So, they were not spotted before the field test. The ASIC designers are currently working to address these problems and will provide the updated firmware for the next field measurements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2008

Effect of LEO Exposure on Aromatic Polymers Containing Phenylphosphine Oxide Groups

As part of the Materials on The International Space Station Experiment (MISSE), aromatic polymers containing phenylphosphine oxide groups were exposed to low Earth orbit for approx.4 years. All of the aromatic polymers containing phenylphosphine oxide groups survived the exposure despite the high fluence of atomic oxygen that completely eroded other polymer films such as Kapton(TradeMark) and Mylar(Trademark) of comparable or greater thickness. The samples were characterized for changes in physical properties, thermal/optical properties surface chemistry, and surface topography. The data from the polymer samples on MISSE were compared to samples from the same batch of material stored under ambient conditions on Earth. In addition, comparisons were made between the MISSE samples and those subjected to shorter term space flight exposures. The results of these analyses will be presented.

Lillehei, P. T.↗
NASA NTRS2013

Mobile Thread Task Manager

The Mobile Thread Task Manager (MTTM) is being applied to parallelizing existing flight software to understand the benefits and to develop new techniques and architectural concepts for adapting software to multicore architectures. It allocates and load-balances tasks for a group of threads that migrate across processors to improve cache performance. In order to balance-load across threads, the MTTM augments a basic map-reduce strategy to draw jobs from a global queue. In a multicore processor, memory may be "homed" to the cache of a specific processor and must be accessed from that processor. The MTTB architecture wraps access to data with thread management to move threads to the home processor for that data so that the computation follows the data in an attempt to avoid L2 cache misses. Cache homing is also handled by a memory manager that translates identifiers to processor IDs where the data will be homed (according to rules defined by the user). The user can also specify the number of threads and processors separately, which is important for tuning performance for different patterns of computation and memory access. MTTM efficiently processes tasks in parallel on a multiprocessor computer. It also provides an interface to make it easier to adapt existing software to a multiprocessor environment.

Clement, Bradley J.↗
DOE OSTI2014

Sparse inpainting and isotropy

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2018

Flood Analysis of the Thomas Flood in California Using NASA Data in a GIS

In December 2017, the Thomas Fire burned areas in Ventura and Santa Barbara Counties in California, consuming 281,893 acres (440 square miles), and becoming the largest wildfire in California history. On January 8, 2018, a winter storm made landfall in Santa Barbara County, releasing more than 5 inches of rain onto the same scarred area, causing San Ysidro and Montecito Creeks to overflow and leading to devastating flash flooding and debris flows by January 9, 2018. Several areas that were not typically prone to flooding were at risk due to the higher than usual flows in the nearby streams and creeks. The unprecedented flooding killed at least 20 people and left several others missing. NASA's Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. This poster presents the use of satellite data from NASA's inventory that have the potential for use in identification and analysis of forest fire risk and subsequent after-effects within a geographic information system (GIS).

science↗