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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The high-temperature heat capacity of the (Th,U)O 2 and (U,Pu)O 2 solid solutions

The enthalpy increment data for the (Th,U)O 2 and (U,Pu)O 2 solid solutions are reviewed and complemented with new experimental data (400–1773 K) and many-body potential model simulations. The results of the review show that from room temperature up to about 2000 K the enthalpy data are in agreement with the additivity rule (Neumann-Kopp) in the whole composition range. Above 2000 K the effect of Oxygen Frenkel Pair (OFP) formation leads to an excess enthalpy (heat capacity) that is modeled using the enthalpy and entropy of OFP formation from the end-members. Here, a good agreement with existing experimental work is observed, and a reasonable agreement with the results of the many-body potential model, which indicate the presence of the diffuse Bredig (superionic) transition that is not found in the experimental enthalpy increment data.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multi-wavelength characterization of the blazar S5 0716+714 during an unprecedented outburst phase

Context. The BL Lac object S5 0716+714, a highly variable blazar, underwent an impressive outburst in January 2015 (Phase A), followed by minor activity in February (Phase B). The MAGIC observations were triggered by the optical flux observed in Phase A, corresponding to the brightest ever reported state of the source in the R-band. Aims.The comprehensive dataset collected is investigated in order to shed light on the mechanism of the broadband emission. Methods. Multi-wavelength light curves have been studied together with the broadband spectral energy distributions (SEDs). The sample includes data from Effelsberg, OVRO, Metsähovi, VLBI, CARMA, IRAM, SMA, Swift-UVOT, KVA, Tuorla, Steward, RINGO3, KANATA, AZT-8+ST7, Perkins, LX-200, Swift-XRT, NuSTAR, Fermi-LAT and MAGIC. Results. The flaring state of Phase A was detected in all the energy bands, providing for the first time a multi-wavelength sample of simultaneous data from the radio band to the very-high-energy (VHE, E > 100 GeV). In the constructed SED, the Swift-XRT+NuSTAR data constrain the transition between the synchrotron and inverse Compton components very accurately, while the second peak is constrained from 0.1 GeV to 600 GeV by Fermi+MAGIC data. The broadband SED cannot be described with a one-zone synchrotron self-Compton model as it severely underestimates the optical flux in order to reproduce the X-ray to γ-ray data. Instead we use a two-zone model. The electric vector position angle (EVPA) shows an unprecedented fast rotation. An estimation of the redshift of the source by combined high-energy (HE, 0.1 GeV < E < 100 GeV) and VHE data provides a value of z = 0.31 ± 0.02 stats ± 0.05 sys , confirming the literature value. Conclusions. The data show the VHE emission originating in the entrance and exit of a superluminal knot in and out of a recollimation shock in the inner jet. A shock–shock interaction in the jet seems responsible for the observed flares and EVPA swing. Finally, this scenario is also consistent with the SED modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-P-19: Fetal Dose Estimate for a High-Dose Fluoroscopy Guided Intervention Using Modern Data Tools

Purpose: An accurate dose estimate is necessary for effective patient management after a fetal exposure. In the case of a high-dose exposure, it is critical to use all resources available in order to make the most accurate assessment of the fetal dose. This work will demonstrate a methodology for accurate fetal dose estimation using tools that have recently become available in many clinics, and show examples of best practices for collecting data and performing the fetal dose calculation. Methods: A fetal dose estimate calculation was performed using modern data collection tools to determine parameters for the calculation. The reference point air kerma as displayed by the fluoroscopic system was checked for accuracy. A cumulative dose incidence map and DICOM header mining were used to determine the displayed reference point air kerma. Corrections for attenuation caused by the patient table and pad were measured and applied in order to determine the peak skin dose. The position and depth of the fetus was determined by ultrasound imaging and consultation with a radiologist. The data collected was used to determine a normalized uterus dose from Monte Carlo simulation data. Fetal dose values from this process were compared to other accepted calculation methods. Results: An accurate high-dose fetal dose estimate was made. Comparison to accepted legacy methods were were within 35% of estimated values. Conclusion: Modern data collection and reporting methods ease the process for estimation of fetal dose from interventional fluoroscopy exposures. Many aspects of the calculation can now be quantified rather than estimated, which should allow for a more accurate estimation of fetal dose.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1976

Survey of users of earth resources remote sensing data

A user survey was conducted to determine current earth resources survey (ERS) data use/user status and recommendations for strengthening use. Only high-altitude aircraft and satellite (primarily LANDSAT) data were included. Emphasis was placed on the private sector/industrial user. Objectives of the survey included: who is using ERS data, how they are using the data, the relative value of current data use as well as obtaining user views as to possible ways of strengthening future ERS data use. The survey results are documented and should provide relevant decision making information for developing future programs of maximum benefit to all end users of satellite ERS data.

Wukelic, G. E.↗
NASA NTRS2003

Validating the Autonomous Science Agent

This paper describes the validation process for the Autonomous Science Agent, a software agent that will fly onboard the EO-1 spacecraft from 2003-2004. This agent will recognize science events, retarget the spacecraft to respond to the science events, and reduce data downlink to only the highest value science data. The autonomous science agent has been designed using a layered architectural approach with specific redundant safeguards to reduce the risk of an agent malfunction to the EO-1 spacecraft. This 'safe' design is also in the process of being thoroughly validated by informal validation methods and extensive testing. This paper describes the analysis used to define agent safety, elements of the design that increase the safety of the agent, and the process being used to validate agent safety prior to the agent software controlling the spacecraft.

agent safety↗
NASA NTRS2005

Safe Agents in Space: Preventing and Responding to Anomalies in the Autonomous Sciencecraft Experiment

This paper describes the design of the Autonomous Sciencecraft Experiment, a software agent that has been running on-board the EO-I spacecraft since 2003. The agent recognizes science events, retargets the spacecraft to respond to the science events, and reduces data downlink to only the highest value science data. The autonomous science agent was designed using a layered architectural approach with specific redundant safeguards to reduce the risk of an agent malfUnction to the EO-I spacecraft. The agent was designed to be "safe" by first preventing anomalies, then by automatically detecting and responding to them when possible. This paper describes elements of the design that increase the safety of the agent, the anomalies that occurred during the experiment, and how the agent responded to these anomalies.

autonomous science agent↗
NASA NTRS2014

Weather Forecasting Systems and Methods

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

Mecikalski, John↗
DOE OSTI2020

HarDWR - Cumulative Water Rights Curves

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on Harmonized Water Rights Records v2.0 sourced from WestDAAT - Added "Unspecified" was a water source category, and files associated with this category v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description This product an updated version of the database used as input to the WBM model (Grogan et al. in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (2024). This database contains 2,667 individual .csv files, three for each Water Management Area (WMA) in the 11-state region. File Naming WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either "S" for surface water rights, "G" for groundwater rights, or "U" for unspecified. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the [###] unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al. in review) For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in the study region. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the second index in this object, the data for California will also be in the second index for each accompanying list. wmasRightsPersGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for groundwater. wmasRightsPersSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for surface water. wmasRightsPersUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for water from an unspecified source. wmasRightsTotsGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for groundwater. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for surface water. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for water from an unspecified source. This object is provided for convenience to check out original total values, if desired. wmaIDByState: A list of data frames which contain the by state allocation by WMA matrix. There is a matrix for each state in a different data frame within the object.

Economics↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Nuclear Data Sheets for A = 247

Available information pertaining to the nuclear structure of all nuclei with mass numbers A=247 is presented. Various decay and reaction data are evaluated and compared. Adopted data, levels, spin, parity and configuration assignments are given. When there are insufficient data, expected values from systematics of nuclear properties or/and theoretical calculations are quoted. Unexpected or discrepant experimental results are also noted. A summary and compilation of the discovery of various isotopes in this mass region is given in 2013Fr02 ({sup 247}Pu, {sup 247}Am, {sup 247}Cm, {sup 243}Bk, {sup 247}Cf), 2011Me01 ({sup 247}Es), and 2013Th02 ({sup 247}Fm, {sup 247}Md)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1998

Evaluating Extinction Values Using Wire Impactor Data

The purpose of the study was to compare the extinctions calculated from data obtained with the Ames Wire Impactor to extinctions measured with the SAGE 11 satellite system. The comparison was intended to serve as a validation of the extinctions obtained using the wire impactor data. It was felt that if the extinctions obtained by the two diverse methods agreed well, it would be an indication that the number densities measured on the wires were correct. Tables and charts are presented to show the extinction values from the two different methods.

Source record↗
NASA NTRS1973

Further examination of pilot instrument scanning data and development of a new link value estimator

Pilot instrument scanning data collected during simulated transport instrument landing approaches is examined. Deterministic features of random pilot instrument scanning behavior are confirmed to be: transitions in point of eye fixation which originate and terminate on the same instrument are rare; and transitions in point of eye fixation which originate on one secondary instrument and terminate on another secondary instrument are rare. Link value estimators are developed using statistics and these experimental facts. The result has a special significance when there is but a single primary instrument, a flight director. The result is used to simplify the iterative computational procedure of STI's display theory to a non-iterative procedure for the flight director case.

Hofmann, L. G.↗
DOE OSTI2022

Adjoint sensitivity analysis and data assimilation for verification of dry storage cask contents

Dry cask storage is a method for interim storage of spent fuel assemblies which contain fissile isotopes of uranium and plutonium. These can present a proliferation concern and consequently there is a need for non-destructive testing methods to verify a dry cask's contents for proliferation protection. We present an application of adjoint sensitivity analysis and data assimilation to a multigroup diffusion model of dry cask storage. Adjoint sensitivity analysis allows the efficient calculation of sensitivities for use in data assimilation to calibrate imprecisely known parameter values and data consistency tests to detect diversion scenarios. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Systems and methods for screening particle source manufacturing and development test data

A computing system obtains test data for a particle source. The test data was generated by the particle source when the particle source was caused to emit particles. The test data comprises a first set of measurements of a first type and a second set of measurements of a second type. The computing system applies a data agnostic predictive model to the test data. The data agnostic predictive model is generated without a parametric analysis of variables of the first type and variables of the second type. The data agnostic predictive model outputs, based upon the test data, a value that is indicative of whether or not the test data is abnormal. Based upon the value, the computing system outputs an indication that the particle source was operating sub-optimally when emitting the particles.

Multari, Rosalie A.↗
NASA NTRS1984

Shuttle 'Challenger' aerodynamic performance from flight data - Comparisons with predicted values and 'Columbia' experience

Longitudinal aerodynamic performance comparisons between flight extracted and predicted values are presented for the first eight NASA Space Shuttle entry missions. Challenger results are correlated with the ensemble five flight Columbia experience and indicate effects due to differing angle-of-attack and body flap deflection profiles. An Appendix is attached showing the results of each flight using both the LaRC LAIRS and NOAA atmospheres. Discussions are presented which review apparent density anomalies observed in the flight data, with particular emphasis on the suggested shears and turbulence encountered during STS-2 and STS-4. Atmospheres derived from Shuttle data are presented which show structure different than that remotely sensed and imply regions of unstable air masses as a plausible explanation. Though additional aerodynamic investigations are warranted, an added benefit of Shuttle flight data for atmospheric research is discussed, in particular, as applicable to future NASA space vehicles such as AOTVs and tethered satellites.

Findlay, J. T.↗
DOE OSTI2014

Nuclear Data Sheets for A=243

We present available information pertaining to the nuclear structure of all nuclei with mass numbers A=243. Various decay and reaction data are evaluated and compared. Adopted data, levels, spin, parity and configuration assignments are given. When there are insufficient data, expected values from systematics of nuclear properties or/and theoretical calculations are quoted. Unexpected or discrepant experimental results are also noted. A summary and compilation of the discovery of various isotopes in this mass region is given in 2013Fr02 ( 243 Np, 243 Pu, 243 Am, 243 Cm, 243 Bk, and 243 Cf), 2011Me01 ( 243 Es), and 2013Th02 ( 243 Fm).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Raw tensiometer soil matric potential data, from Oct 2017-Jun 2020 in Manaus, Brazil

This data package contains raw output from two Campbell Scientific CR1000 data loggers each connected to three UMS T4e tensiometer sensors in Manaus, Brazil. Tensiometers at each data logger location were installed to depths of 10 cm, 20 cm and 50 cm below the ground surface for the purpose of measuring soil matric potential at each depth. “Station 3” tensiometers were installed at approximately 2°36'31.68"S, 60°12'34.70"W. “Station 4” tensiometers were installed at approximately 2°36'32.46"S, 60°12'34.92"W. The files “tensio3.edit.202109.csv” and “tensio4.edit.202109.csv” contain raw data (with invalid data removed). A text editor will be required to view the raw files in .csv (comma-separated values) format. Data processing was limited to: 1) Editing column headers for clarity; 2) Applying a correction that converts voltage read by the data loggers to pressure units (kPa); 3) removing all data greater than 50 kPa and less than -115 kPa; 4) Adding columns entitled “Caution.10cm”, “Caution.20cm” and “Caution.50cm” that contains values of either “NA” or “Caution”, the latter of which is applied when data exceeds the manufacturer reported data validity range of less than -85 kPa to suggest use of these data with caution; 5) Adding columns entitled “Invalid.10cm”, “Invalid.20cm”, and “Invalid.50cm” that contains values of either “NA” or “Invalid”, the latter of which is applied when data accuracy is deemed compromised due to the introduction of air bubbles into the instrument. Data marked “Invalid” may still indicate general trends of wet/high matric potential and dry/low matric potential, but values are inaccurate and dry/low matric potential values are likely not low enough. Wet/high matric potential values are likely to be more accurate than dry/low matric potential values; 6) Data format changed from .dat to .csv. This research was performed as a part of the NGEE Tropics project, which aims to advance model predictions of tropical forest carbon cycle responses to a changing climate over the 21st Century.

54 ENVIRONMENTAL SCIENCES↗