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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Binary Black Hole Mergers in the First Advanced LIGO Observing Run

The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper we present full results from a search for binary black hole merger signals with total masses up to 100M solar mass and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than 5 alpha over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance, which has a 87 probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations we infer stellar-mass binary black hole merger rates lying in the range 9-240 Gpc-3 yr-1. These observations are beginning to inform astrophysical predictions of binary black hole formation rates, and indicate that future observing runs of the Advanced detector network will yield many more gravitational wave detections.

Abbott, B. P.↗
NASA NTRS2017

Millimeter Wave Systems for Airports and Short-Range Aviation Communications: A Survey of the Current Channel Models at mmWave Frequencies

Millimeter-wave (mmWave) communications will play a key role in enhancing the throughput, reliability, and security of next generation wireless networks. These advancements are achieved through the large bandwidth available in this band and through the use of highly directional links that will be used to overcome the large pathloss at these frequencies. Although the terrestrial application of mmWave systems is advancing at a rapid pace, the use of mmWave communication systems in aviation systems or airports is still in its infancy. This can be attributed to the challenges related to radio technology and lack of development, and characterization of mmWave wireless channels for the aviation field and the airport environment. Consequently, one of our goals is to develop methodologies that support mmWave air to ground links, and various links at airports, by applying new localization schemes that allow for application of highly directional links that can be deployed over longer distances despite the high path loss at mmWave frequencies. However, a very thorough understanding of the mmWave channel models are needed to enable such new applications. To this end, in this paper, we present a survey of the current channel models in the mmWave band. The 3-dimensional statistical channel model is also reviewed and its parameters and typical characteristics for this model are identified and computed through simulation for the Boise metropolitan area.

air traffic management↗
DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗
DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, A. Jacob [Fermilab; Unlisted]↗
NASA NTRS1993

Cryogenic, low-noise high electron mobility transistor amplifiers for the Deep Space Network

The rapid advances recently achieved by cryogenically cooled high electron mobility transistor (HEMT) low-noise amplifiers (LNA's) in the 1- to 10-GHz range are making them extremely competitive with maser amplifiers. In order to address future spacecraft navigation, telemetry, radar, and radio science needs, the Deep Space Network is investing both maser and HEMT amplifiers for its Ka-band (32-GHz) downlink capability. This article describes the current state cryogenic HEMT LNA development at Ka-band for the DSN. Noise performance results at S-band (2.3 GHz) and X-band (8.5 GHz) for HEMT's and masers are included for completeness.

Bautista, J. J.↗
DOE OSTI2024

Cookie-Jar: An Adaptive Re-configurable Framework for Wireless Network Infrastructures

5G advancements like Massive Multiple Input Multiple Output (MIMO) bring high capacity and low latency, but also intensify interference challenges. Static and dynamic coordination techniques address this, often at the cost of increased power draw. We introduce Cookie-Jar (CJ), an interference coordination (IC) framework using reinforcement learning for multi-goal optimization. By dynamically adjusting network, power, and topology parameters based on real-time conditions, CJ improves Signal to Noise and Interference Ratio (SINR) while minimizing power consumption. Simulated 5G experiments showcase CJ's potential, achieving a 15% SINR improvement with near-identical power draw compared to existing methods.

Network↗
DOE OSTI2023

Classifying metal‐binding sites with neural networks

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Systems and methods for revocation management in an AMI network

Systems and methods for certificate revocation management for advanced metering infrastructure networks are provided. An accumulation manager can be used to accumulate certificate revocation list (CRL) information and determine an accumulator value and a proof value based on the accumulated CRL information using a plurality of functions. These values can be sent on to smart meters, which can then use them for authentication purposes.

Cebe, Mumin↗
NASA NTRS2008

Distributed Observer Network

NASA s advanced visual simulations are essential for analyses associated with life cycle planning, design, training, testing, operations, and evaluation. Kennedy Space Center, in particular, uses simulations for ground services and space exploration planning in an effort to reduce risk and costs while improving safety and performance. However, it has been difficult to circulate and share the results of simulation tools among the field centers, and distance and travel expenses have made timely collaboration even harder. In response, NASA joined with Valador Inc. to develop the Distributed Observer Network (DON), a collaborative environment that leverages game technology to bring 3-D simulations to conventional desktop and laptop computers. DON enables teams of engineers working on design and operations to view and collaborate on 3-D representations of data generated by authoritative tools. DON takes models and telemetry from these sources and, using commercial game engine technology, displays the simulation results in a 3-D visual environment. Multiple widely dispersed users, working individually or in groups, can view and analyze simulation results on desktop and laptop computers in real time.

Source record↗
DOE OSTI2013

System and method for implementing periodic early discard in on-chip buffer memories of network elements

An advance is made over the prior art in accordance with the principles of the present invention that is directed to a new approach for a system and method for a buffer management scheme called Periodic Early Discard (PED). The invention builds on the observation that, in presence of TCP traffic, the length of a queue can be stabilized by selection of an appropriate frequency for packet dropping. For any combination of number of TCP connections and distribution of the respective RTT values, there exists an ideal packet drop frequency that prevents the queue from over-flowing or under-flowing. While the value of the ideal packet drop frequency may quickly change over time and is sensitive to the series of TCP connections affected by past packet losses, and most of all is impossible to compute inline, it is possible to approximate it with a margin of error that allows keeping the queue occupancy within a pre-defined range for extended periods of time. The PED scheme aims at tracking the (unknown) ideal packet drop frequency, adjusting the approximated value based on the evolution of the queue occupancy, with corrections of the approximated packet drop frequency that occur at a timescale that is comparable to the aggregate time constant of the set of TCP connections that traverse the queue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Center for Computing Research Highlights

Sandia has a legacy of leadership in the advancement of high performance computing (HPC) at extreme scales. First-of-a-kind scalable distributed-memory parallel platforms such as the Intel Paragon, ASCI Red (the world’s first teraflops computer), and Red Storm (co-developed with Cray) helped form the basis for one of the most successful supercomputer product lines ever: the Cray XT series. Sandia also has pioneered system software elements—including lightweight operating systems, the Portals network programming interface, advanced interconnection network designs, and scalable I/O— that are critical to achieving scalability on large computing systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Advanced Communication Technology Satellite (ACTS) Very Small Aperture Terminal (VSAT) Network Control Performance

This paper discusses the performance of the network control function for the Advanced Communications Technology Satellite (ACTS) very small aperture terminal (VSAT) full mesh network. This includes control of all operational activities such as acquisition, synchronization, timing and rain fade compensation as well as control of all communications activities such as on-demand integrated services (voice, video, and date) connects and disconnects Operations control is provided by an in-band orderwire carried in the baseboard processor (BBP) control burst, the orderwire burst, the reference burst, and the uplink traffic burst. Communication services are provided by demand assigned multiple access (DAMA) protocols. The ACTS implementation of DAMA protocols ensures both on-demand and integrated voice, video and data services. Communications services control is also provided by the in-band orderwire but uses only the reference burst and the uplink traffic burst. The performance of the ACTS network control functions have been successfully tested during on-orbit checkout and in various VSAT networks in day to day operations. This paper discusses the network operations and services control performance.

Coney, T. A.↗
NASA NTRS2000

Bridging the Gap from Networking Technologies to Applications: Workshop Report

The objective of the Next Generation Internet (NGI) Federal program is threefold, encompassing development of networking technologies, high-performance network testbeds, and revolutionary applications. There have been notable advances in emerging network technologies and several nationwide testbeds have been established, but the integration of emerging technologies into applications is lagging. To help bridge this gap between developers of NGI networking technologies and developers of NGI applications, the NASA Research and Education Network (NREN) project hosted a two-day workshop at NASA Ames Research Center in August 1999. This paper presents a summary of the results of this workshop and also describes some of the challenges NREN is facing while incorporating new technologies into HPCC and other NASA applications. The workshop focused on three technologies - Quality of Service (QoS), advanced multicast, and security-and five major NGI application areas - telemedicine, digital earth, digital video, distributed data-intensive applications, and computational infrastructure applications. Network technology experts, application developers, and NGI testbed representatives came together at the workshop to promote cross-fertilization between the groups. Presentations on the first day, including an overview of the three technologies, application case studies and testbed status reports, laid the foundation for discussions on the second day. The objective of these latter discussions, held within smaller breakout groups, was to establish a coherent picture of the current status of the various pieces of each of the three technologies, to create a roadmap outlining future technology development, and to offer technological guidance to application developers. In this paper we first present a brief overview of the NGI applications that were represented at the workshop, focusing on the identification of technological advances that have successfully been incorporated in each application and technological challenges that remain. Next we present the technology roadmaps that were created at the workshop, summarizing the status of various mechanisms that are currently under development and forecasting when various advances are likely to occur within the next one-to-three-year time span. Then we identify issues that were raised at the workshop that might hinder technology development or that might impede integration into NGI applications. We also report some specific guidelines that were offered at the workshop to enable application developers to integrate and effectively use emerging NGI technology building blocks. Finally, we describe NREN activities to incorporate emerging technologies into NASA applications. These activities include support for other NASA High-Performance Computing and Communications Program areas such as IPG (Information Power Grid), support for NASA science enterprises such as Earth science and Mars program prototyping activities, support for satellite/terrestrial networking applications such as the TransAtlantic and TransPacific demonstrations and the Interplanetary Internet, support for NASA telemedicine applications such as the Virtual Collaborative Clinic, and participation in NGI advanced technology testbed initiatives such as the QBone and the NTON/Supernet. For each activity we highlight the primary technological challenge that is associated with it.

Johnson, Marjory J.↗
DOE OSTI2022

Results From Invoking Artificial Neural Networks to Measure Insider Threat Detection & Mitigation

Advances on differentiating between malicious intent and natural “organizational evolution” to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. Overall, this research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors—suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1991

Advanced Satellite Research Project: SCAR Research Database. Bibliographic analysis

The literature search was provided to locate and analyze the most recent literature that was relevant to the research. This was done by cross-relating books, articles, monographs, and journals that relate to the following topics: (1) Experimental Systems - Advanced Communications Technology Satellite (ACTS), and (2) Integrated System Digital Network (ISDN) and Advance Communication Techniques (ISDN and satellites, ISDN standards, broadband ISDN, flame relay and switching, computer networks and satellites, satellite orbits and technology, satellite transmission quality, and network configuration). Bibliographic essay on literature citations and articles reviewed during the literature search task is provided.

Pelton, Joseph N.↗
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗