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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Experimental investigation of shock-cell noise reduction for single-stream nozzles in simulated flight, comprehensive data report. Volume 3: Shadowgraph photos and facility description

A total of 142 shadowgraph photographs were taken on 43 different plumes that were distributed over the six nozzle configurations using the 9.5 inch diameter collimated light beam of the shadowgraph setup. Aerodynamic flow conditions of the shadowgraph test points, the location and identification of each of the photographs, and copies of the pictures are presented.

Yamamoto, K.↗
NASA NTRS1989

The crush of new earth science data knocking at our door

The reasons for collecting massive amounts of earth science data in the Earth Observing System (EOS) Project are discussed. A processing hierarchy for handling the data is described, and the prospects for adequate throughput and storage for operational data analysis in the EOS era are addressed. Needs for successful exploratory data analysis are examined, and the policy issues implicated by the large stream of EOS data are considered.

Kahn, Ralph↗
NASA NTRS2013

File Specification for GEOS-5 FP (Forward Processing)

The GEOS-5 FP Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The assimilation is performed at a horizontal resolution of 0.3125-degree longitude by 0.25- degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational FP, using the most recent version of the GEOS-5 assimilation system. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GMAO products page: http://gmao.gsfc.nasa.gov/products/.

GSI↗
NASA NTRS2013

File Specification for GEOS-5 FP-IT (Forward Processing for Instrument Teams)

The GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP-IT incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP-IT uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The analysis is performed at a horizontal resolution of 0.625-degree longitude by 0.5-degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational GEOS-5 FP-IT processing in support of the EOS instrument teams. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary.

3D Var↗
NASA NTRS1981

Seasat altimeter observations of dynamic topography in the Gulf Stream region

A straightforward approach to altimeter data analysis in the Gulf Stream system is presented, using a detailed geoid model to remove the gravitational component. The resulting sea surface height profiles compare remarkably well with independent oceanographic observations. Specific features such as cold rings, warm rings, and no anomaly regions are analyzed and it is shown that known positions of cyclonic and anticyclonic rings correspond with depressions and elevations, respectively, with amplitudes as large as 95 cm. The apparent fluctuation of the Gulf Stream is indicated by the results, as in the finding that on time scales of a few days, surface transport indicated by the sea surface height difference across the stream varied by nearly 30%

Cheney, R. E.↗
DOE OSTI2024

Semi–Analytical Modeling of Transient Stream Drawdown and Depletion in Response to Aquifer Pumping

Analytical and semi–analytical models for stream depletion with transient stream stage drawdown induced by groundwater pumping are developed to address a deficiency in existing models, namely, the use of a fixed stream stage condition at the stream–aquifer interface. Here field data are presented to demonstrate that stream stage drawdown does indeed occur in response to groundwater pumping near aquifer–connected streams. A model that predicts stream depletion with transient stream drawdown is developed based on stream channel mass conservation and finite stream channel storage. The resulting models are shown to reduce to existing fixed–stage models in the limit as stream channel storage becomes infinitely large, and to the confined aquifer flow with a no–flow boundary at the streambed in the limit as stream storage becomes vanishingly small. The model is applied to field measurements of aquifer and stream drawdown, giving estimates of aquifer hydraulic parameters, streambed conductance, and a measure of stream channel storage. The results of the modeling and data analysis presented herein have implications for sustainable groundwater management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Coolant tube curvature effects on film cooling as detected by infrared imagery

Reported herein are comparative thermal film cooling footprints observed by infrared imagery from straight, curved and looped coolant tube geometries. It was hypothesized that the difference in secondary flow and turbulence structure of flow through these three tubes should influence the mixing properties between the coolant and mainstream. The coolant was injected across an adiabatic plate through a hole angled at 30 deg to the surface in line with the free stream flow. The data cover a range of blowing rates from 0.37 to 1.25 (mass flow per unit area of coolant divided by free stream). Average temperature difference between coolant and tunnel air was 25 C. Data comparisons confirmed that coolant tube curvature significantly influences film cooling effectiveness.

Papell, S. S.↗
NASA NTRS2011

Noise Prediction Module for Offset Stream Nozzles

A Modern Design of Experiments (MDOE) analysis of data acquired for an offset stream technology was presented. The data acquisition and concept development were funded under a Supersonics NRA NNX07AC62A awarded to Dimitri Papamoschou at University of California, Irvine. The technology involved the introduction of airfoils in the fan stream of a bypass ratio (BPR) two nozzle system operated at transonic exhaust speeds. The vanes deflected the fan stream relative to the core stream and resulted in reduced sideline noise for polar angles in the peak jet noise direction. Noise prediction models were developed for a range of vane configurations. The models interface with an existing ANOPP module and can be used or future system level studies.

Henderson, Brenda S.↗
NASA NTRS1991

Fiber Optic Tactical Local Network (FOTLAN)

A 100 Mbit/s FDDI (fiber distributed data interface) network interface unit is described that supports real-time data, voice and video. Its high-speed interrupt-driven hardware architecture efficiently manages stream and packet data transfer to the FDDI network. Other enhancements include modular single-mode laser-diode fiber optic links to maximize node spacing, optic bypass switches for increased fault tolerance, and a hardware performance monitor to gather real-time network diagnostics.

Bergman, L. A.↗
DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗
NASA NTRS2007

A New Source of Space Weather Data

The twin STEREO spacecraft were launched on October 26,2006 and, after spending several weeks in alignment orbits, became operational in heliocentric orbits on January 2 1,2007. The spacecraft are now in orbits similar to Earth's and are separating from each other at a rate of about 45 degrees per year as viewed from the sun. The STEREO spacecraft are each equipped with a complement of EUV, and white light imagers as well as particles and fields in situ instruments and a radio burst tracker. In addition to full resolution data downloaded once per day, each spacecraft broadcasts a real time telemetry 'beacon' stream containing compressed images and typically one minute averages of solar wind measurements and radio intensities. These real time data streams are received by a mix of NOAA and NASA-managed tracking stations and the data is transmitted by these stations to the STEREO Science Center at NASA Goddard Space Flight Center where they are processed into useable parameters with a latency of less than five minutes from receipt. As the two spacecraft recede from the sun-Earth line, triangulation of the imaging and radio measurements should allow better estimate of the speed and direction of coronal mass ejections, allowing a more precise estimate of their arrival at Earth. In situ particles and fields measurements, particularly for the spacecraft (Behind) trailing Earth in its orbit, will sample the solar wind that will eventually co-rotate around to the sun-Earth line. Late in the prime mission when the spacecraft are very far (>90 degrees) from the sun-Earth line, observations from the Behind spacecraft, will be particularly valuable for providing a preview of active regions well beyond the sun's East limb (as seen from Earth). Continually updated STEREO space weather data can be viewed and downloaded at http:/lstereo-ssc.nascom.nasa.govldata/beacon/.

Kaiser, Michael L.↗
NASA NTRS2014

Circuit for Communication over DC Power Line Using High Temperature Electronics

A high temperature communications circuit includes a power conductor for concurrently conducting electrical energy for powering circuit components and transmitting a modulated data signal, and a demodulator for demodulating the data signal and generating a serial bit stream based on the data signal. The demodulator includes an absolute value amplifier for conditionally inverting or conditionally passing a signal applied to the absolute value amplifier. The absolute value amplifier utilizes no diodes to control the conditional inversion or passing of the signal applied to the absolute value amplifier.

Krasowski, Michael J.↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Distributed Wind Monitoring Best Practices

Accessible performance and operational data have been identified as a key enabler for distributed wind energy industry advancement. While utility-scale wind turbines benefit from reliable and continuous supervisory control and data acquisition (SCADA)-based monitoring platforms, monitoring of the U.S. fleet of distributed wind (DW) turbines has been more inconsistent, unreliable, and sometime difficult to access. Without fleet monitoring data, the industry will never understand and thus work to improve turbine under-performance and reliability issues. For the DW industry to scale up, attract investors, and boost credibility, fleetwide monitoring must be robust and reliable, select data must be made accessible to stakeholders, and the data must be in a format useful to users. To help move the industry toward a more standardized, accessible stream of monitoring data, this distributed wind monitoring best practices report attempts to cover topics including key monitoring channels, hardware, communication strategies, and accessibility. Strategic engagement with DW original equipment manufacturers (OEMs), service providers, lab and university researchers, testing organization, certification bodies, end users and solar photovoltaic (PV) monitoring experts has enabled a better understanding of the current state-of-the-art of monitoring and aided in articulating this set of best practices that will guide OEMs toward harmonized monitoring strategies, aimed at a future goal of achieving accessible performance and operational data for the entire fleet of U.S. distributed wind turbines.

17 WIND ENERGY↗
NASA NTRS1990

Three-Dimensional Cavity Flow Fields at Subsonic and Transonic Speeds

An experimental investigation was conducted to expand the data base and knowledge of flow fields in cavities over the subsonic and transonic speed regimes. A rectangular, 3-D cavity was tested over a Mach number range from 0.30 to 0.95 and at Reynolds numbers per foot from 1 x 10 to the 6th power to 4.2 x 10 to the 6th power. Two sizes of cavities were tested with length-to-height ratios (l/h) of 4.4 and 11.7 and with rectangular and nonrectangular cross-sections. Extensive static pressure data on the model walls were obtained and a complete tabulation of the data are presented. The boundary layer approaching the cavity was turbulent and the thickness was measured with a total pressure rake. The static pressure measurements obtained with the deep cavity configuration (l/h = 4.4) at Reynolds numbers greater than 3.0 x 10 to the 6th power per foot showed large fluctuations during the data sampling time. For the deep cavity, at lower Reynolds numbers, and for all conditions tested with the shallow cavity, the data showed much less unsteadiness. Though mean static pressure distributions have been used in past cavity analysis at transonic free stream conditions, the data presented here indicates that it is necessary to consider the instantaneous pressure distributions. The data also indicated that the shallow cavity static pressure measurements were sensitive to the thickness of the boundary layer entering the cavity.

Plentovich, E. B.↗
NASA NTRS1994

VLSI-based Video Event Triggering for Image Data Compression

Long-duration, on-orbit microgravity experiments require a combination of high resolution and high frame rate video data acquisition. The digitized high-rate video stream presents a difficult data storage problem. Data produced at rates of several hundred million bytes per second may require a total mission video data storage requirement exceeding one terabyte. A NASA-designed, VLSI-based, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term (DC-like) or short term (AC-like) changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pre-trigger and post-trigger storage techniques are then adaptable to archiving only the significant video images.

Williams, Glenn L.↗