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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

The ATLAS integrated structural analysis and design software system

The ATLAS system provides an extensive set of integrated technical computer-program modules for the analysis and design of general structural configurations, as well as capabilities that are particularly suited for the aeroelastic design of flight vehicles. The system is based on the stiffness formulation of the finite element structural analysis method and can be executed in batch and interactive computing environments on CDC 6600/CYBER computers. Problem-definition input data are written in an engineering-oriented language using a free field format. Input-data default values, generation options, and data quality checks provided by the preprocessors minimize the amount of data and flowtime for problem definition/verfication. Postprocessors allow selected input and calculated data to be extracted, manipulated, and displayed via on-line and off-line prints or plots for monitoring and verifying problem solutions. The sequence and mode of execution of selected program modules are controlled by a common user-oriented language.

Dreisbach, R. L.↗
NASA NTRS1985

Verification of L-band SAR calibration

Absolute calibration of a digital L-band SAR system to an accuracy of better than 3 dB has been verified. This was accomplished with a calibration signal generator that produces the phase history of a point target. This signal relates calibration values to various SAR data sets. Values of radar cross-section (RCS) of reference reflectors were obtained using a derived calibration relationship for the L-band channel on the ERIM/CCRS X-C-L SAR system. Calibrated RCS values were compared to known RCS values of each reference reflector for verification and to obtain an error estimate. The calibration was based on the radar response to 21 calibrated reference reflectors.

Larson, R. W.↗
NASA NTRS1992

Cosmic gamma-ray bursts from BATSE - Another great debate

The BATSE detectors aboard Compton Observatory record about one cosmic gamma-ray burst (GRB) per day. Preliminary data analysis shows a highly isotropic sky map and a nonuniform brightness distribution. Anisotropies expected from a Galactic neutron star population, the most frequently considered source model, did not emerge from the data. Taken at face value, the data seem to suggest a heliocentric solution of the GRB puzzle. The observed isotropy can be achieved if sources are either very near or extragalactic. Pop I neutron stars in the disk do not simultaneously fit sky and brightness distributions. A possibility are sources in an extended Galactic halo with scale length large enough to avoid strong anisotropies due to the solar offset from the Galactic center. If GRBs are located in an extended halo we ask whether the neutron star paradigm can survive. We show that the recently discovered high velocity radio pulsars may provide a natural source population for GRBs. If these pulsars formed in the halo, as suggested by the radio data, the possibility arises that GRBs and high velocity pulsars are two related phenomena that provide observational evidence of the dark Galactic corona. We also discuss cosmological redshift constraints that follow from the observed brightness distribution.

Hartmann, Dieter H.↗
NASA NTRS2008

Specialized Color Function for Display of Signed Data

This Mathematica script defines a color function to be used with Mathematica's plotting modules for differentiating data attaining both positive and negative values. Positive values are shown as shades of blue, and negative values are shown in red. The intensity of the color reflects the absolute value of the data value.

Kalb, Virginia↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
NASA NTRS1991

Predicting cloud-to-ground lightning with neural networks

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consist of ground based field mill data, meteorological tower data, lightning location data, and radiosonde data. High values of the field mill data and rapid changes in the field mill data, offset in time, provide the forecasts or desired output values used to train the neural network through backpropagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed.

Barnes, Arnold A., Jr.↗
DOE OSTI2021

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, SeyedMohammad↗
DOE OSTI2023

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, Seyed Mohammad↗
DOE OSTI2016

Parallel sort with a ranged, partitioned key-value store in a high perfomance computing environment

Improved sorting techniques are provided that perform a parallel sort using a ranged, partitioned key-value store in a high performance computing (HPC) environment. A plurality of input data files comprising unsorted key-value data in a partitioned key-value store are sorted. The partitioned key-value store comprises a range server for each of a plurality of ranges. Each input data file has an associated reader thread. Each reader thread reads the unsorted key-value data in the corresponding input data file and performs a local sort of the unsorted key-value data to generate sorted key-value data. A plurality of sorted, ranged subsets of each of the sorted key-value data are generated based on the plurality of ranges. Each sorted, ranged subset corresponds to a given one of the ranges and is provided to one of the range servers corresponding to the range of the sorted, ranged subset. Each range server sorts the received sorted, ranged subsets and provides a sorted range. A plurality of the sorted ranges are concatenated to obtain a globally sorted result.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Pattern recognition algorithm using temporal data

The value of a previously classified image is discussed with the use of spectral and temporal information. A probability theory is presented of a signal X, belonging to class pi sub i.

Source record↗
NASA NTRS1981

Determination of surface stress by Seasat-SASS - A case study with JASIN data

The values of sea surface stress determined with the dissipation method and those determined with a surface-layer model from observations on F.S. Meteor during the Joint Air-Sea Interaction (JASIN) Experiment are compared with the backscatter coefficients measured by the scatterometer SASS on the satellite Seasat. This study demonstrates that SASS can be used to determine surface stress directly as well as wind speed. The quality of the surface observations used in the calibration of the retrieval algorithms, however, is important. This sample of measurements disagrees with the predictions by the existing wind retrieval algorithm under non-neutral conditions and the discrepancies depend on atmospheric stability.

Liu, W. T.↗
NASA NTRS1989

Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data

The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted.

Pierson, Willard J., Jr.↗
NASA NTRS2004

Using Block-local Atomicity to Detect Stale-value Concurrency Errors

Data races do not cover all kinds of concurrency errors. This paper presents a data-flow-based technique to find stale-value errors, which are not found by low-level and high-level data race algorithms. Stale values denote copies of shared data where the copy is no longer synchronized. The algorithm to detect such values works as a consistency check that does not require any assumptions or annotations of the program. It has been implemented as a static analysis in JNuke. The analysis is sound and requires only a single execution trace if implemented as a run-time checking algorithm. Being based on an analysis of Java bytecode, it encompasses the full program semantics, including arbitrarily complex expressions. Related techniques are more complex and more prone to over-reporting.

Artho, Cyrille↗
NASA NTRS1996

Multiwavelength Comparison of Modeled and Measured Remote Tropospheric Aerosol Backscatter Over Pacific Ocean

Aerosol concentrations and size distributions in the middle and upper troposphere over the remote Pacific Ocean were measured with a forward scattering spectrometer probe (FSSP) on the NASA DC-8 aircraft during NASA's Global Backscatter Experiment (GLOBE) in May-June 1990. The FSSP size channels were recalibrated based on refractive index estimates from flight-level aerosol volatility measurements with a collocated laser optical particle counter (LOPC). The recalibrated FSSP size distributions were averaged over 100-s intervals, fitted with lo-normal distributions and used to calculate aerosol backscatter coefficients at selected wavelengths. The FSSP-derived backscatter estimates were averaged over 300-s intervals to reduce large random fluctuations. The smoothed FSSP aerosol backscatter coefficients were then compared with LOPC-derived backscatter values and with backscatter measured at or near flight level from four lidar systems operating at 0.53, 1.06, 9.11, 9.25, and 10.59 micrometers. Agreement between FSSP-derived and lidar-measured backscatter was generally best at flight level in homogeneous aerosol fields and at high backscatter values. FSSP data often underestimated low backscatter values especially at the longer wavelengths due to poor counting statistics for larger particles (greater than 0.8 micrometers diameter) that usually dominate aerosol backscatter at these wavelengths. FSSP data also underestimated backscatter at shorter wavelengths when particles smaller than the FSSP lower cutoff diameter (0.35 micrometers) made significant contributions to the total backscatter.

Cutten, D. R.↗
NASA NTRS2018

Lessons and Recommendations for Board-Level Testing with Protons

Protons with sufficiently high energy, provided in a broad field covering on the order of 0.1m2 can be used to perform board-level testing for single event effects (SEE). NASA has used this approach for board-level testing over the last 20 years. Although many difficulties inherent in SEE testing are simplified when using a board-level test, including reduced cost, the method is inherently risky because of the limited value of the collected data and the potential to make critical mistakes when performing SEE testing this way, leading to data of less value. Historically, NASA’s approach to proton board-level testing has been limited to lower criticality applications. However, with users both inside and outside NASA using this method for higher levels of mission assurance, we have put together a set of lessons and recommendations to improve the value of data collected using this method. Focus areas covered include test preparation, test execution, and interpretation of results.

Guertin, Steven M.↗
NASA NTRS2003

Active State Model for Autonomous Systems

The concept of the active state model (ASM) is an architecture for the development of advanced integrated fault-detection-and-isolation (FDI) systems for robotic land vehicles, pilotless aircraft, exploratory spacecraft, or other complex engineering systems that will be capable of autonomous operation. An FDI system based on the ASM concept would not only provide traditional diagnostic capabilities, but also integrate the FDI system under a unified framework and provide mechanism for sharing of information between FDI subsystems to fully assess the overall health of the system. The ASM concept begins with definitions borrowed from psychology, wherein a system is regarded as active when it possesses self-image, self-awareness, and an ability to make decisions itself, such that it is able to perform purposeful motions and other transitions with some degree of autonomy from the environment. For an engineering system, self-image would manifest itself as the ability to determine nominal values of sensor data by use of a mathematical model of itself, and selfawareness would manifest itself as the ability to relate sensor data to their nominal values. The ASM for such a system may start with the closed-loop control dynamics that describe the evolution of state variables. As soon as this model was supplemented with nominal values of sensor data, it would possess self-image. The ability to process the current sensor data and compare them with the nominal values would represent self-awareness. On the basis of self-image and self-awareness, the ASM provides the capability for self-identification, detection of abnormalities, and self-diagnosis.

Park, Han↗
DOE OSTI2026

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare, 2025.

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare.” 2025. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 3 (2025, N = 8,296). This dataset represents the third and final phase of a national survey aimed at understanding user acceptance and preferences related to pooled rideshare (PR) services in the United States. Building on insights from earlier phases, this phase expands both the sample size and the depth of analysis to support policymaking, transportation planning, and service design for sustainable mobility systems. The Phase 3 survey was administered online to a nationally representative sample of 8,296 U.S. adults. The sample includes a wide range of demographics. The survey retained core questions from previous phases while introducing 77 detailed service features (actionable items) to evaluate potential improvements to PR offerings. Each feature was designed to assess whether a specific improvement, such as enhanced safety measures, real-time ride tracking, or user training would increase participants’ willingness to adopt PR services. In addition, behavioral predictors, current rideshare habits, environmental attitudes, and perceived barriers (e.g., safety, privacy, and comfort) were captured. - Phase_3_Final - The dataset contains rows corresponding to individual respondents and columns representing survey items, demographic characteristics, and response values. The data is available in both .CSV and .SAV formats. - Phase_3_Final_MapFile - Accompanying this dataset is a data dictionary explaining each variable, value range, and coding schema. An .XLSX format of the full survey instrument is included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗