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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Application of a PC based, real-time, data-aquisition system in rotorcraft wind-tunnel testing

Data has been acquired for a rotocraft test in the NASA Langley Transonic Dynamics Tunnel using a desktop data acquisition system. The system, which consists of an IBM Personal Computer AT (PC-AT) and an Omega Engineering OM-900 Stand-Alone Interface System, is well suited for acquiring high speed data on a limited number of channels. The data acquisition system and the interrupt driven software which provides the capability for near real-time cyclic data acquisition as well as data storage and display are described.

Wilbur, Matthew L.↗
NASA NTRS1993

A nonhydrostatic version of the Penn State-NCAR Mesoscale Model - Validation tests and simulation of an Atlantic cyclone and cold front

A nonhydrostatic extension to the Pennsylvania State University-NCAR Mesoscale Model is presented, which employs reference pressure as the basis for a terrain-following vertical coordinate, and the fully compressible system of equations. It is shown that this model, combined with the existing initialization techniques and the physics of the current hydrostatic model, is capable of real-data simulations on any scale, limited only by data quality and resolution and by computer resources. An example of an explosive cyclone simulation is presented, demonstrating the capability of the nonhydrostatic model to reproduce the hydrostatic model results at large scales.

Dudhia, Jimy↗
NASA NTRS1997

Results of the REFLEX (Return Flux Experiment) Flight Mission

The numerous problems occurring in this first flight of the REFLEX experiment, both in the spacecraft and with the instrument package, seriously constrained the acquisition and analysis of data and severely limited the interpretation of the data that were obtained. Of these, the ambient helium measurements appear to be the most promising. They are summarized and discussed in Appendix A. Further analyses could be attempted to establish the correct values for the energy centers as they varied during the mission. In addition, an extensive laboratory recalibration on a high-speed beam system could in principle provide corrections to be used in analyzing and interpreting the returned data set. The unknown malfunction which generated the energy drift needs to be understood and corrected before the REFLEX experiment is reflown; some hardware modification, or at least retuning, is likely to be required.

Pepin, R. O.↗
NASA NTRS2004

Southern Alaska Glaciers: Spatial and Temporal Variations in Ice Volume

Although temperate mountain glaciers comprise less than 1% of the glacier-covered area on Earth, they are important because they appear to be melting rapidly under present climatic conditions and, therefore, make significant contributions to rising sea level. In this study, we use ICESat observations made in the last 1.5 years of southern Alaska glaciers to estimate ice elevation profiles, ice surface slopes and roughness, and bi-annual and/or annual ice elevation changes. We report initial results from the near coastal region between Yakutat Bay and Cape Suckling that includes the Malaspina and Bering Glaciers. We show and interpret ice elevations changes across the lower reaches of the Bagley Ice Valley for the period between October 2003 and May 2004. In addition, we use off-nadir pointing observations to reference tracks over the Bering and Malaspina Glaciers in order to estimate annual ice elevation change. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Shuttle Radar Topography Mission (SRTM) derived DEMs are used to estimate across track regional slopes between ICESat data acquisitions. Although the distribution and quantity of ICESat elevation profiles with multiple, exact repeat data is currently limited in Alaska, individual ICESat data tracks, provide an accurate reference surface for comparison to other elevation data (e.g. ASTER and SRTM X- and C-band derived DEMs). Specifically we report the elevation change over the Malaspina Glacier's piedmont lobe between a DEM derived from SRTM C-band data acquired in Feb. 2000 and ICESat Laser #2b data from Feb.-March 2004. We also report use of ICESat elevation data to enhance ASTER derived absolute DEMs. Mountain glaciers generally have rougher surfaces and steeper regional slopes than the ice sheets for which the ICESat design was optimized. Therefore, rather than averaging ICESat observations over large regions or relying on crossovers, we are working with well-located ICESat footprint returns to estimate glacier ice elevations and surface characteristics. Additional information is included in the original extended abstract.

Sauber, J.↗
NASA NTRSDate not supplied

Parallelized Convolutional Interleaver Implementation for Efficient DDR Memory Access

Convolutional interleavers are used in many different communications systems to correct for burst errors due to atmospheric fades and scintillation. The interleaver size is related to the channel coherence time and the data rate. Small convolutional interleavers can be implemented in a field programmable gate array (FPGA) block random access memory (BRAM). However, large interleavers exceeding the size of the BRAM on the FPGA are necessary for channels with longer fades and higher data rates. Therefore, an implementation utilizing double data rate (DDR) memory external to the FPGA is necessary. Wide DDR memory data buses can make the use of DDR memory for convolutional interleavers inefficient when individual symbols are written to and read from the memory. DDR memory operational speeds can also limit the data rate of the interleaver. The Consultative Committee for Space Data Systems (CCSDS) Optical Communications High Photon Efficiency (HPE) standard utilizes a convolutional channel symbol interleaver. A previous implementation of the HPE standard utilized BRAM for the convolutional interleaver, but mission requirements for the upcoming Optical Artemis-2 Orion (O2O) communications demonstration dictate the use of an interleaver exceeding the size of the BRAM. An algorithm and method for implementing the convolutional interleaver in the FPGA with DDR memory is described in this paper.

optical communications↗
NASA NTRSDate not supplied

Parallelized convolutional interleaver implementation for efficient DDR memory access

Convolutional interleavers are used in many different communications systems to correct for burst errors due to atmospheric fades and scintillation. The interleaver size is related to the channel coherence time and the data rate. Small convolutional interleavers can be implemented in a field programmable gate array (FPGA) block random access memory (BRAM). However, large interleavers exceeding the size of the BRAM on the FPGA are necessary for channels with longer fades and higher data rates. Therefore, an implementation utilizing double data rate (DDR) memory external to the FPGA is necessary. Wide DDR memory data buses can make the use of DDR memory for convolutional interleavers inefficient when individual symbols are written to and read from the memory. DDR memory operational speeds can also limit the data rate of the interleaver. The Consultative Committee for Space Data Systems (CCSDS) Optical Communications High Photon Efficiency (HPE) standard utilizes a convolutional channel symbol interleaver. A previous implementation of the HPE standard utilized BRAM for the convolutional interleaver, but mission requirements for the upcoming Optical Artemis-2 Orion (O2O) communications demonstration dictate the use of an interleaver exceeding the size of the BRAM. An algorithm and method for implementing the convolutional interleaver in the FPGA with DDR memory is described in this paper.

Optical communications↗
NASA NTRSDate not supplied

Long-Term Health Risk Quantification

Astronauts face hazards during spaceflight, including space radiation exposure, isolation and confinement, traveling far distances from Earth, reduced gravity levels, and closed and hostile environments. These hazards drive the definition of human health and performance risks associated with spaceflight. NASA’s Human System Risk Board maintains the human spaceflight risk posture for in-mission risks, as well as post-flight, Long-Term Health (LTH)risks potentially occurring later in the astronaut’s life. LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. Possible LTH risk outcomes include the time and interventions needed for the astronaut to return to preflight physiological states after experiencing spaceflight hazards and recovery from any in-mission medical events that persist into the post-flight timeframe. It includes chronic complications that may arise due to experiencing in-flight medical conditions or injuries and medical conditions that occur later in life with a higher probability of occurrence or with more severity because of their spaceflight exposure. Finally, LTH risk outcomes can also include a reduction in life expectancy due to spaceflight exposures. There have been 144 medical conditions identified by NASA’s Lifetime Surveillance of Astronaut Health team to be associated with LTH risk. Epidemiological studies have been performed for some of these conditions to determine if astronauts suffer from an increased prevalence or severity of the condition due to their spaceflight experience compared to a comparable cohort .Differences in astronaut mortality or morbidity due to spaceflight experience were not detected in several of these studies. There were two cases where a modest increase in the incidence rate of astronaut LTH outcomes was detected. The first suggested an increase in the incidence of melanoma cases in astronauts, where the number of cases in astronauts were similar to the elevated number of cases observed in airplane pilots. The second provided some evidence of elevated numbers of cardiovascular disease events in astronauts compared to an appropriate healthy comparator cohort, which may warrant additional investigation. The lack of detection of LTH risk outcomes should not ease concerns about astronaut LTH. The studies highlighted here constitute only a small portion of the potential LTH conditions that could occur. Once epidemiological studies are performed on all conditions, significant findings may be detected. The analysis of astronaut LTH also suffers from limited numbers of data points because of the limited numbers of astronauts overall and the even fewer who have reached an age where LTH outcomes may begin to manifest. As shuttle and ISS astronauts begin to age and increase the feasibility of analysis, LTH outcomes may be detected. An application of risk quantification is the use of risk metrics within trade studies for resource prioritization and decision making. Trade studies regarding countermeasures to LTH risk outcomes would benefit from a quantification of LTH risk. NASA has ground-based processes in place such as astronaut screening and access to continuous medical monitoring and care during and after their astronaut career which are the main methods for mitigating LTH risk. In-mission countermeasures, such as acceptable levels of medical care and available countermeasures to counter spaceflight related physiological decrements, can mitigate a poor health and performance status immediately post-flight. Identifying appropriate risk metrics, obtaining valid quantities for them, and tying them to LTH countermeasures are necessary steps for realizing their use in trade studies. This presentation will highlight the challenges associated with the identification, quantification, and utilization of LTH risk metrics

Beth Lewandowski↗
NASA NTRS2003

Quantifying the Effect of Pressure Sensitive Paint On Aerodynamic Data

A thin pressure sensitive paint (PSP) coating can slightly modify the overall shape of a wind-tunnel model and produce surface roughness or smoothness that does not exist on the unpainted model. These undesirable changes in model geometry may alter flow over the model, and affect the pressure distribution and aerodynamic forces and moments on the model. This study quantifies the effects of PSP on three models in low-speed, transonic and supersonic flow regimes. At a 95% confidence level, the PSP effects on the integrated forces are insignificant for a slender arrow-wing-fuselage model and delta wing model with two different paints at Mach 0.2, 1.8, and 2.16 relative to the total balance accuracy limit. The data displayed a repeatability of 2.5 drag counts, while the balance accuracy limit was about 5.5 drag counts. At transonic speeds, the paint has a localized effect at high angles of attack and has a resolvable effect on the normal force, which is significant relative to the balance accuracy limit. For low speeds, the PSP coating has a localized effect on the pressure tap measurements, which leads to an appreciable decrease in the pressure tap reading. Moreover, the force and moment measurements had a poor precision, which precluded the ability to measure the PSP effect for this particular test.

Amer, T. R.↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Wang, Xiao↗
NASA NTRS1989

Collisional excitation of interstellar molecules - Deuterated water, HDO

Using an accurate theoretical interacting potential and coupled-channel molecular scattering calculations, excitation rates for deuterated water (HDO) in collisions with He atoms were computed. Data are presented for rates among the 34 rotational levels with energy below 400/cm for temperatures between 50 and 500 K. Excitation rate constants were determined using data from fixed energy cross sections averaged over thermal (Boltzmann) distributions of collision energies. Based on the agreement (for H2O) of rates from two different theoretical potentials and on agreement with the limited experimental data, the larger rates for HDO were estimated to be generally accurate to better than 50 percent.

Green, Sheldon↗
DOE OSTI2022

Snowmass Topical Group Summary Report: IF04 -- Trigger and Data Acquisition Systems

A trend for future high energy physics experiments is an increase in the data bandwidth produced from the detectors. Datasets of the Petabyte scale have already become the norm, and the requirements of future experiments -- greater in size, exposure, and complexity -- will further push the limits of data acquisition technologies to data rates of exabytes per seconds. The challenge for these future data-intensive physics facilities lies in the reduction of the flow of data through a combination of sophisticated event selection in the form of high-performance triggers and improved data representation through compression and calculation of high-level quantities. These tasks must be performed with low-latency (i.e. in real-time) and often in extreme environments including high radiation, high magnetic fields, and cryogenic temperatures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Snowmass Topical Group Summary Report: IF04 -- Trigger and Data Acquisition Systems

A trend for future high energy physics experiments is an increase in the data bandwidth produced from the detectors. Datasets of the Petabyte scale have already become the norm, and the requirements of future experiments -- greater in size, exposure, and complexity -- will further push the limits of data acquisition technologies to data rates of exabytes per seconds. The challenge for these future data-intensive physics facilities lies in the reduction of the flow of data through a combination of sophisticated event selection in the form of high-performance triggers and improved data representation through compression and calculation of high-level quantities. These tasks must be performed with low-latency (i.e. in real-time) and often in extreme environments including high radiation, high magnetic fields, and cryogenic temperatures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗