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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Networks consolidation program

The Networks Consolidation Program (NCP) goals were defined to include the planning, designing, and implementing of a single network of ground tracking stations. The proposed consolidated network is to make use of facilities that are now included in the Goddard Ground Spaceflight Tracking and Data Network as well as the existing JPL Deep Space Network. These facilities are to be combined and modified to provide a consolidated network capable of supporting the set of planetary and Highly Elliptical Earth Orbiter missions that are planned for the Tracking and Data Relay Satellite System era. The history of activities and events that led to the decision to consolidate the NASA ground tracking and data networks are traced. A summary of the management and planning activities that have taken place at the Jet Propulsion Laboratory, Goddard Space Flight Center, and at the NASA Headquarters Office of Space Tracking and Data Systems, with respect to the NCP, from the time of decision in October 1979 to the present, are discussed.

Yeater, M. L.↗
NASA NTRS2018

Automatic Speech Recognition for Launch Control Center Communication Using Recurrent Neural Networks with Data Augmentation and Custom Language Model

Transcribing voice communications in NASA’s launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

Chow, Edward↗
NASA NTRS1992

Memory Network For Distributed Data Processors

Universal Memory Network (UMN) is modular, digital data-communication system enabling computers with differing bus architectures to share 32-bit-wide data between locations up to 3 km apart with less than one millisecond of latency. Makes it possible to design sophisticated real-time and near-real-time data-processing systems without data-transfer "bottlenecks". This enterprise network permits transmission of volume of data equivalent to an encyclopedia each second. Facilities benefiting from Universal Memory Network include telemetry stations, simulation facilities, power-plants, and large laboratories or any facility sharing very large volumes of data. Main hub of UMN is reflection center including smaller hubs called Shared Memory Interfaces.

Bolen, David↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2020

Utah FORGE: Discrete Fracture Network (DFN) Data

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 16A(78)-32. These fracture sets are fully stochastic and do not contain the deterministic set that matches the pilot well 58-32 FMI data. Well 58-32 has been completed and 16A(78)-32 is to be drilled as part of Phase 3. The original .fab files are not included due to redundancy. The *.fabgz data for the 800m and 1200m depth areas are in the native FracMan format and have been compressed using Gzip. Filtered data for the 800m depth area includes .csv spreadsheets, native FracMan (.fab), and GOCAD (.ts) files that are in a compressed zip format. The file titled "SGW 2020 Finnila and Podgorney DFN fracture files on GDR.pdf" is a description of the data and should be reviewed prior to data use.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Adaptive continuity-preserving simplification of street networks

While street network data are nearly universally available, their representation is usually transportation-based. However, for many types of analyses, e.g., urban morphology or network science, unprocessed transportation-based street network data is unsuitable, making a cumbersome manual simplification process necessary. To address this challenge, in this paper we propose an algorithm for simplification of street networks, based on the detection of network portions that need to be simplified, and continuity-preserving heuristics that generate new geometries. The algorithm, released in the open-source Python package neatnet, facilitates the generation of morphological networks and generalises to various geographical contexts without a need to alter the parameters, while offering better performance than other available solutions.

Fleischmann, Martin [Charles University, Prague, C↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Study on Co-existing Heterogeneous Wireless Networks for Data Transmission within a Nuclear Facility

Deployment of wireless technologies is a salient need for modernization, automation and improved operation of nuclear power plants (NPPs). As a single technology cannot support the ever-changing needs, it is required to have a heterogeneous wireless network architecture to address the different technical and economic challenges. However, the coexistence of these multiband heterogeneous wireless networks brings numerous challenges due to the factors including dissimilarity in their channel access mechanism, distance between nodes, transmit power level and many more. This paper develops real-world experiments and simulations of wireless coexistence for Wi-Fi, Fifth generation cellular (5G) and Zigbee in the unlicensed band to understand the challenges and opportunities. The experiments were conducted over the Platform for Open Wireless Data-driven Experimental Research (POWDER) testbed at the university of Utah. In addition, this paper is the first to propose a novel packet rate control technique at the network layer to create temporary opportunities for 5G or Zigbee signal transmissions focusing its application in a nuclear facility while using the shared band. The performance of the proposed coexistence solution is validated with experimental results and simulation.

5G↗
DOE OSTI2025

High Energy Physics Network Requirements Review: Final Report, July 2024–December 2024

The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1982

Channel coding in the space station data system network

A detailed discussion of the use of channel coding for error correction, privacy/secrecy, channel separation, and synchronization is presented. Channel coding, in one form or another, is an established and common element in data systems. No analysis and design of a major new system would fail to consider ways in which channel coding could make the system more effective. The presence of channel coding on TDRS, Shuttle, the Advanced Communication Technology Satellite Program system, the JSC-proposed Space Operations Center, and the proposed 30/20 GHz Satellite Communication System strongly support the requirement for the utilization of coding for the communications channel. The designers of the space station data system have to consider the use of channel coding.

Healy, T.↗
DOE OSTI2022

Detecting Quantum Critical Points of Correlated Systems by Quantum Convolutional Neural Network Using Data from Variational Quantum Eigensolver

Machine learning has been applied to a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems, for classifying phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions corresponding to the paramagnetic and ferromagnetic phases of the TFIM with reasonable accuracy. The QCNN can be trained to predict the corresponding ‘phase’ of wavefunctions around the putative quantum critical point even though it is trained by wavefunctions far away. The paper provides a basis for exploiting the QCNN to identify the quantum critical point.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1981

Comparisons of global ozone trends inferred from the BUV experiment on Nimbus 4 and the ground-based network

Preliminary comparisons between global ozone burdens derived from the backscattered ultraviolet (BUV) experiment on Nimbus 4 and those inferred from an analysis of ground-based network data seem to indicate significant differences in the inter-annual variability of ozone. Some of the observed differences may be due to improper weighting of the ground-based network data, slowly changing planetary wave structure over the fixed station, of small inter-annual changes in meridional transport parameters. There is also some evidence which indicates that the polar stratosphere at high latitudes may represent an important ozone storage resevoir which tends to compensate for large scale changes observed in the regions outside of the polar stratosphere. Possible consequences of this are that the global trends derived from ground based ozone measurements may not be valid and furthermore that the current satellite techniques by themselves may be sufficient. An ozone monitoring system which includes observations from satellites, ground-based stations, balloons and rockets may be necessary.

Heath, D. F.↗
NASA NTRS1989

Neural networks for data compression and invariant image recognition

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Gardner, Sheldon↗
NASA NTRS1973

NASA directory of observation station locations, volume 1

Geodetic information for NASA tracking stations and for observation stations cooperating in NASA geodetic satellite programs is presented. A Geodetic Data Sheet is provided for each station, giving the position of the station and describing briefly how it was established. Geodetic positions and geocentric coordinates of these stations are tabulated on local or major geodetic datums and on selected world geodetic systems. The principal tracking facilities used by NASA, including the Spaceflight Tracking and Data Network, the Deep Space Network, and several large radio telescopes are discussed. Positions of these facilities are tabulated on their local or national datums, the Mercury Spheroid 1960, the Modified Mercury Datum 1968, and the Spaceflight Tracking and Data Network System. Observation stations in the NASA Geodetic Satellites Program are included along with stations participating in the National Geodetic Satellite Program. Positions of these facilities are given on local or preferred major datums, and on the Modified Mercury Datum 1968.

Source record↗
NASA NTRS1992

A simple, effective media access protocol system for integrated, high data rate networks

The operation and performance of a dual media access protocol for integrated, gigabit networks are described. Unlike other dual protocols, each protocol supports a different class of traffic. The Carrier Sensed Multiple Access-Ring Network (CSMA/RN) protocol and the Circulating Reservation Packet (CRP) protocol support asynchronous and synchronous traffic, respectively. The two protocols operate with minimal impact upon each other. Performance information demonstrates that they support a complete range of integrated traffic loads, do not require call setup/termination or a special node for synchronous traffic control, and provide effective pre-use and recovery. The CRP also provides guaranteed access and fairness control for the asynchronous system. The paper demonstrates that the CSMA-CRP system fulfills many of the requirements for gigabit LAN-MAN networks most effectively and simply. To accomplish this, CSMA-CRP features are compared against similar ring and bus systems, such as Cambridge Fast Ring, Metaring, Cyclic Reservation Multiple Access, and Distributed Dual Queue Data Bus (DQDB).

Foudriat, E. C.↗
NASA NTRS2015

TTEthernet for Integrated Spacecraft Networks

Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network determinism required by real-time spacecraft applications. Even with modern advancements, the uncoordinated (i.e. event-driven) nature of Ethernet communication unavoidably leads to message contention within network switches. The arbitration process used to resolve such conflicts introduces variation in the time it takes for messages to be forwarded. TTEthernet1 introduces decentralized clock synchronization to switched Ethernet, enabling message transmission according to a time-triggered (TT) paradigm. A network planning tool is used to allocate each device a finite amount of time in which it may transmit a frame. Each time slot is repeated sequentially to form a periodic communication schedule that is then loaded onto each TTEthernet device (e.g. switches and end systems). Each network participant references the synchronized time in order to dispatch messages at predetermined instances. This schedule guarantees that no contention exists between time-triggered Ethernet frames in the network switches, therefore eliminating the need for arbitration (and the timing variation it causes). Besides time-triggered messaging, TTEthernet networks may provide two additional traffic classes to support communication of different criticality levels. In the rate-constrained (RC) traffic class, the frame payload size and rate of transmission along each communication channel are limited to predetermined maximums. The network switches can therefore be configured to accommodate the known worst-case traffic pattern, and buffer overflows can be eliminated. The best-effort (BE) traffic class behaves akin to classical Ethernet. No guarantees are provided regarding transmission latency or successful message delivery. TTEthernet coordinates transmission of all three traffic classes over the same physical connections, therefore accommodating the full spectrum of traffic criticality levels required in IMA architectures. Common computing platforms (e.g. LRUs) can share networking resources in such a way that failures in non-critical systems (using BE or RC communication modes) cannot impact flight-critical functions (using TT communication). Furthermore, TTEthernet hardware (e.g. switches, cabling) can be shared by both TTEthernet and classical Ethernet traffic.

Loveless, Andrew↗