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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
DOE OSTI2013

GRB 090227B: THE MISSING LINK BETWEEN THE GENUINE SHORT AND LONG GAMMA-RAY BURSTS

The time-resolved spectral analysis of GRB 090227B, made possible by the Fermi-GBM data, allows us to identify in this source the missing link between the genuine short and long gamma-ray bursts (GRBs). Within the Fireshell model of the GRBs we predict genuine short GRBs: bursts with the same inner engine of the long bursts but endowed with a severely low value of the baryon load, B {approx}< 5 Multiplication-Sign 10{sup -5}. A first energetically predominant emission occurs at the transparency of the e {sup +} e {sup -} plasma, the Proper-GRB (P-GRB), followed by a softer emission, the extended afterglow. The typical separation between the two emissions is expected to be of the order of 10{sup -3}-10{sup -2} s. We identify the P-GRB of GRB 090227B in the first 96 ms of emission, where a thermal component with the temperature kT = (517 {+-} 28) keV and a flux comparable with the non-thermal part of the spectrum is observed. This non-thermal component as well as the subsequent emission, where there is no evidence for a thermal spectrum, is identified with the extended afterglow. We deduce a theoretical cosmological redshift z = 1.61 {+-} 0.14. We then derive the total energy E{sup tot}{sub e{sup +}e{sup -}}= (2.83{+-}0.15) Multiplication-Sign 10{sup 53} erg, the baryon load B = (4.13 {+-} 0.05) Multiplication-Sign 10{sup -5}, the Lorentz {Gamma} factor at transparency {Gamma}{sub tr} = (1.44 {+-} 0.01) Multiplication-Sign 10{sup 4}, and the intrinsic duration {Delta}t' {approx} 0.35 s. We also determine the average density of the circumburst medium (CBM), (n {sub CBM}) = (1.90 {+-} 0.20) Multiplication-Sign 10{sup -5} particles cm{sup -3}. There is no evidence of beaming in the system. In view of the energetics and of the baryon load of the source, as well as of the low interstellar medium and of the intrinsic timescale of the signal, we identify the GRB progenitor as a binary neutron star. From the recent progress in the theory of neutron stars, we obtain masses of the stars m {sub 1} = m {sub 2} = 1.34 M {sub Sun} and their corresponding radii R {sub 1} = R {sub 2} = 12.24 km and thickness of their crusts {approx}0.47 km, consistent with the above values of the baryon load, of the energetics and of the time duration of the event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
DOE OSTI2021

HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory

merging High-Performance Computing (HPC) workloads, such as graph analytics, machine learning, and big data science, are data-intensive. Data-intensive workloads usually present fine-grained memory accesses with limited or no data locality, and thus incur frequent cache misses and low utilization of memory bandwidth. 3D-stacked memory devices such as Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM) can provide significantly higher bandwidth than conventional memory modules. However, the traditional interfaces and optimization methods for JEDEC DDR devices do not allow to fully exploit the potential performance of 3D-stacked memory with the massive amount of irregular memory accesses of data-intensive applications. In this paper, we propose a novel Hotspot-Aware Manager (HAM) infrastructure for 3D-stacked memory devices capable of optimizing memory access streams via request aggregation, hotspot detection, and in-memory prefetching. %and an associated hotspot-aware page policy. We present the HAM design and implementation, and simulate it on a system using RISC-V embedded cores with attached HMC devices. We extensively evaluate HAM with over 12 benchmarks and applications representing diverse irregular memory access patterns. The results show that, on average, HAM reduces redundant requests by 37.51\% and increases the prefetch buffer hit rate by 4.2 times, compared to a baseline streaming prefetcher. On the selected benchmark set, HAM provides performance gains of 21.81\% in average (up to 34.28\%) and power savings of 35.07\% over a standard 3D-stacked memory.

Wang, Xi↗
NASA NTRS1988

A simulator investigation of the use of digital data link for pilot/ATC communications in a single pilot operation

Studies have shown that radio communications between pilots and air traffic control contribute to high pilot workload and are subject to various errors. These errors result from congestion on the voice radio channel, and missed and misunderstood messages. The use of digital data link has been proposed as a means of reducing this workload and error rate. A critical factor, however, in determining the potential benefit of data link will be the interface between future data link systems and the operator of those systems, both in the air and on the ground. The purpose of this effort was to evaluate the pilot interface with various levels of data link capability, in simulated general aviation, single-pilot instrument flight rule operations. Results show that the data link reduced demands on pilots' short-term memory, reduced the number of communication transmissions, and permitted the pilots to more easily allocate time to critical cockpit tasks while receiving air traffic control messages. The pilots who participated unanimously indicated a preference for data link communications over voice-only communications. There were, however, situations in which the pilot preferred the use of voice communications, and the ability for pilots to delay processing the data link messages, during high workload events, caused delays in the acknowledgement of messages to air traffic control.

Hinton, David A.↗
DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗
DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data: Preprint

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data input to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1992

High time resolution studies of binary x ray pulsars

This report covers the period September 1, 1992 through August 1, 1993 for NASA grant NAG 5-1260 through the Astrophysics Data Program (ADP), for the project 'High Time Resolution Studies of Binary X-ray Pulsars'. During the past 11 months, we have concentrated on scientific analysis of the data, and have had to reanalyze several data sets, due to a newly discovered error in the unpacking of the count rate data. This error has now been corrected, but may have caused us to miss some scientific results, so we have gone back and reanalyzed the affected data sets, as well as continuing our analysis of other objects.

Cominsky, Lynn R.↗
NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗
DOE OSTI2017

The NUBASE2016 evaluation of nuclear properties

This paper presents the NUBASE2016 evaluation that contains the recommended values for nuclear and decay properties of 3437 nuclides in their ground and excited isomeric (T1/2≥100 ns) states. All nuclides for which any experimental information is known were considered. NUBASE2016 covers all data published by October 2016 in primary (journal articles) and secondary (mainly laboratory reports and conference proceedings) references, together with the corresponding bibliographical information. During the development of NUBASE2016, the data available in the “Evaluated Nuclear Structure Data File” (ENSDF) database were consulted and critically assessed for their validity and completeness. Furthermore, a large amount of new data and some older experimental results that were missing from ENSDF were compiled, evaluated and included in NUBASE2016. The atomic mass values were taken from the “Atomic Mass Evaluation” (AME2016, second and third parts of the present issue). In cases where no experimental data were available for a particular nuclide, trends in the behavior of specific properties in neighboring nuclides (TNN) were examined. This approach allowed to estimate values for a range of properties that are labeled in NUBASE2016 as “non-experimental” (flagged “#”). Evaluation procedures and policies used during the development of this database are presented, together with a detailed table of recommended values and their uncertainties. AMDC: http://amdc.impcas.ac.cn/

Atomic Mass Evaluation (AME)↗
DOE OSTI2022

Narrow-Band Least-Squares Infrasound Array Processing

Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.

58 GEOSCIENCES↗
DOE OSTI2018

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Search for flavour-changing neutral current top-quark decays t → qZ in proton-proton collisions at $\sqrt{s}=13 $ TeV with the ATLAS detector

A search for flavour-changing neutral-current processes in top-quark decays is presented. Data collected with the ATLAS detector from proton-proton collisions at the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13 $ TeV, corresponding to an integrated luminosity of 36.1 fb-1, are analysed. The search is performed using top-quark pair events, with one top quark decaying through the t → qZ (q = u, c) flavour-changing neutral-current channel, and the other through the dominant Standard Model mode t → bW. Only Z boson decays into charged leptons and leptonic W boson decays are considered as signal. Consequently, the final-state topology is characterized by the presence of three isolated charged leptons (electrons or muons), at least two jets, one of the jets originating from a b-quark, and missing transverse momentum from the undetected neutrino. Here, the data are consistent with Standard Model background contributions, and at 95% confidence level the search sets observed (expected) upper limits of 1.7 × 10 -4 (2.4 × 10 -4 ) on the t → uZ branching ratio and 2.4 × 10 -4 (3.2 × 10 -4 ) on the t → cZ branching ratio, constituting the most stringent limits to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Probing the Repulsive Core of the Nucleon-Nucleon Interaction via the 4 He(e,e`pN) Triple-Coincidence Reaction

We studied simultaneously the 4 He(e,e'p), 4 He(e,e'pp), and 4 He(e,e'pn) reactions at Q 2 =2 [GeV/c] 2 and x B >1, for a (e,e'p) missing-momentum range of 400 to 830 MeV/c. The knocked-out proton was detected in coincidence with a proton or neutron recoiling almost back to back to the missing momentum, leaving the residual A=2 system at low excitation energy. These data were used to identify two-nucleon short-range correlated pairs and to deduce their isospin structure as a function of missing momentum in a region where the nucleon-nucleon force is expected to change from predominantly tensor to repulsive. Neutron-proton pairs dominate the high-momentum tail of the nucleon momentum distributions, but their abundance is reduced as the nucleon momentum increases beyond ~500 MeV/c. The extracted fraction of proton-proton pairs is small and almost independent of the missing momentum in the range we studied. Our data are compared with ab-initio calculations of two-nucleon momentum distributions in 4 He.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion

We couple airborne, ground-based, and satellite observations; conduct regional simulations; and develop and apply an inversion technique to constrain hourly smoke emissions from the Rim Fire, the third largest observed in California, USA. Emissions constrained with multiplatform data show notable nocturnal enhancements (sometimes over a factor of 20), correlate better with daily burned area data, and are a factor of 2–4 higher than a priori estimates, highlighting the need for improved characterization of diurnal profiles and day-to-day variability when modeling extreme fires. Constraining only with satellite data results in smaller enhancements mainly due to missing retrievals near the emissions source, suggesting that top-down emission estimates for these events could be underestimated and a multiplatform approach is required to resolve them. As a result, predictions driven by emissions constrained with multiplatform data present significant variations in downwind air quality and in aerosol feedback on meteorology, emphasizing the need for improved emissions estimates during exceptional events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗
NASA NTRS2006

THE MATERIALS ON INTERNATIONAL SPACE STATION EXPERIMENT (MISSE): FIRST RESULTS FROM MSFC INVESTIGATIONS

Marshall Space Flight Center worked with the Air Force Research Laboratory, Naval Research Laboratory, Langley Research Center, Glenn Research Center, the Jet Propulsion Laboratory, Johnson Space Center, Boeing, Lockheed Martin, TRW, the Aerospace Corporation, Triton Systems, AZ Technology, Alion (formerly IITRI), ENTECH, and L'Garde to bring together the first external materials exposure experiment on International Space Station (ISS). MISSE re-uses hardware from the MEEP flown on the Russian space station Mir. MISSE has returned a treasure trove of materials data that will be useful not only for ISS but also for programs as diverse as the new Crew Exploration Vehicle, the James Webb Telescope, the Lunar Surface Access Module, the Robotic Lunar Exploration Program, High Altitude Airships, and solar sails. MISSE-1 and -2 (Figure 1) were attached to the Quest airlock on ISS for 4 years and were retrieved during STS-114. MISSE-3 and -4 were bumped fr-om STS-114 and are currently slated for deployment during STS-121. MISSE-5 (Figure 2) was deployed during STS-114.

Finckenor, Miria↗