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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Cassini Spacecraft Strategy for Recovering from a Complete Loss-Of-Signal Condition

Unlike other faults where the space craft’s automated Fault Protection (FP) routines can be relied upon to provide a carrier signal and telemetered data to the ground-based Spacecraft Operations Flight Support (SOFS) team to facilitate vehicle acquisition, diagnosis, and fault recovery, a complete Loss-of-Spacecraft-Signal (LOS) condition will yield no information at all to help re-acquire the vehicle and resolve the fault. For any FP response activation, the spacecraft’s downlink signal must be acquired via the Deep Space Network (DSN) on the expected (post-fault) Radio Frequency Subsystem (RFS) device configuration commanded by the FP; data that will not be attainable in the case of a complete LOS condition since there is no signal available for the ground station to lock onto. In the case of the Cassini Mission-to-Saturn spacecraft, numerous fault possibilities exist that can cause a LOS condition. Activations of the onboard RFS-related FP can reconfigure the post-fault RFS states, impeding the ground team’s ability to acquire the spacecraft’s downlink signal

Morgan, Paula S.↗
DOE OSTI2018

GoldenEye: stream-based network packet inspection using GPUs

High-performance packet analysis systems have attracted great interest as tools to deal with security concerns in high-speed networks. Recently, researchers have utilized GPUs to improve packet processing performance. However, most existing work has been targeted at per-packet analysis level. Flow-centric operations have been challenging for GPUs because they require sequential operations and large buffers for flow reassembly. In this work, we present the GoldenEye GPU Packet Processing System (GoldenEye), a deep packet inspection (DPI) system that tracks out-of-order TCP packets and provides streambased signature matching. When a batch of packets arrives, GoldenEye sorts packets into flow-reassembled streams and normalizes retransmission through a GPU-implemented reordering module. For signatures that straddle batch boundaries, GoldenEye couples a small set of metadata with a functionallyequivalent minimal regular expression retrieval algorithm to connect the partial matches. Results show that GoldenEye can reassemble tens of millions of packets/sec and conduct stateful DPI operations on TCP streams at multi-ten Gbit/sec rates.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Advances in High-rate Delay Tolerant Networking On-board the International Space Station

The High-rate Delay Tolerant Networking (HDTN) project at the NASA John H. Glenn Research Center (GRC) is developing a performance optimized Delay Tolerant Networking (DTN) implementation which is able to provide reliable multigigabit per second automated network communications for near-Earth and deep space missions. To that end, this paper provides an overview of the testing and integration efforts culminating in a high-rate DTN demonstration onboard the International Space Station (ISS). Over several years, the HDTN team has performed a series of end-to-end tests between the Software Development and Integration Laboratory (SDIL) at the Lyndon B. Johnson Space Center (JSC) and Marshall Space Flight Center’s Huntsville Operations Support Center (HOSC). The testing has focused on a realistic emulation of the ISS Ku-band RF link, which operates at a maximum of 500 Mbps downlink with a 600 ms round-trip time. In this environment, the HDTN onboard gateway has been tested for interoperability with ISS payload nodes and the DTN ground gateway, store and forward capability, reliable transport using the Licklider Transmission Protocol (LTP), and successful recovery from unexpected loss of signal. In addition to integration testing, HDTN has developed a series of software engineering practices to ensure the stability and maturity of the implementation. As the result, HDTN has successfully demonstrated high-rate DTN services onboard the ISS. This paper concludes with a summary of preliminary flight testing results from the Integrated LCRD LEO User Modem and Amplifier Terminal networking experiments.

Delay tolerant networking↗
NASA NTRS2018

A Dichroic Plate with Rounded Corners for NASA’s 34m Beam Waveguide Antenna

A S/K-band frequency selective surface (FSS) or a dichroic plate is designed, manufactured and tested for use in NASA’s Deep Space Network (DSN) 34m beam waveguide (BWG) antennas. Due to its large size, the proposed dichroic incorporates a new design feature, waveguides with rounded corners to cut cost and allow ease of manufacturing the plate. The dichroic is designed using an analysis that combines the Finite Element Method (FEM) for arbitrarily shaped guides with the Method of Moments and Floquet mode theory for periodic structures. The software was verified by comparison with previously measured and computed dichroic plates. The large plate was manufactured with end-mill machining. Their RF performance was measured and is in excellent agreement with the analytical results. The dichroic has been successfully installed and in operational at three NASA Deep Space Stations.

Imbriale, William↗
DOE OSTI2022

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

In this work, time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗
NASA NTRS1980

Pioneer Venus Overview

A Pioneer Venus Overview is discussed. Pioneer Venus consists of two basic spacecraft: Orbiter and Multiprobe. The latter was separated into five separate vehicles near Venus. These were the probe transporter (called the Bus), a large atmospheric entry probe (dubbed Sounder) and three identical smaller probes (called North , Day, and Night in accordance with their entry locations). At Venus all six spacecraft communicated directly back to the Earth-based Deep Space Network (DSN) and, in the case of the Multiprobe mission, to two special receiving sites near Guam and Santiago (Chile). At the time this Special Issue was submitted for publication the nominal mission was complete and the Orbiter was continuing into an extended mission phase. It appears so that sufficient fuel remains to permit full operation through calendar year 1980, at least. The scientific payload, Principle Investigator, and his affiliation are listed for each Pioneer Venus spacecraft. This special issue is primarily devoted to short descriptions of the instruments listed with the exception of the Orbitor Cloud Photopolarimeter. Detailed instrument descriptions for this experiment have been published. Before proceeding with descriptions of the individual instruments, four special archival-type reports are included. The first deals with spacecraft design and operation. The Pioneer Venus spacecraft were unique and very special design features and operational modes needed to be incorporated. These are summarized therein. It should be noted that neither the scientific objectives nor the scientific results of the Pioneer Venus program are described or discussed in detail. The objectives have been published elsewhere.

Colin, Lawrence↗
NASA NTRS2001

Advanced Communication and Networking Technologies for Mars Exploration

Next-generation Mars communications networks will provide communications and navigation services to a wide variety of Mars science vehicles including: spacecraft that are arriving at Mars, spacecraft that are entering and descending in the Mars atmosphere, scientific orbiter spacecraft, spacecraft that return Mars samples to Earth, landers, rovers, aerobots, airplanes, and sensing pods. In the current architecture plans, the communication services will be provided using capabilities deployed on the science vehicles as well as dedicated communication satellites that will together make up the Mars network. This network will evolve as additional vehicles arrive, depart or end their useful missions. Cost savings and increased reliability will result from the ability to share communication services between missions. This paper discusses the basic architecture that is needed to support the Mars Communications Network part of NASA's Space Science Enterprise (SSE) communications architecture. The network may use various networking technologies such as those employed in the terrestrial Internet, as well as special purpose deep-space protocols to move data and commands autonomously between vehicles, at disparate Mars vicinity sites (on the surface or in near-Mars space) and between Mars vehicles and earthbound users. The architecture of the spacecraft on-board local communications is being reconsidered in light of these new networking requirements. The trend towards increasingly autonomous operation of the spacecraft is aimed at reducing the dependence on resource scheduling provided by Earth-based operators and increasing system fault tolerance. However, these benefits will result in increased communication and software development requirements. As a result, the envisioned Mars communications infrastructure requires both hardware and protocol technology advancements. This paper will describe a number of the critical technology needs and some of the ongoing research activities.

Bhasin, Kul↗
NASA NTRS2010

Service Quality Assessment for NASA's Deep Space Network: No Longer a Luxury

When NASA's Deep Space Network (DSN) was established almost a half century ago, the concept of computer-based service delivery was impractical or infeasible due to the state of information technology As a result, the interface the DSN exposes to its customers tends to be equipment-centric, lacking a clear demarcation between the DSN and the mission operation systems (MOS) of its customers. As the number of customers has continued to increase, the need to improve efficiency and minimize costs has grown. This growth has been the impetus for a DSN transformation from an equipment-forrent provider to a provider of standard services. Service orientation naturally leads to requirements for service management, including proactive measurement of service quality and service levels as well as the efficiency of internal processes and the performance of service provisioning systems. DSN System Engineering has surveyed industry offerings to determine if commercial successes in decision support and Business Intelligence (BI) solutions can be applied to the DSN. A pilot project was initiated, and subsequently executed to determine the feasibility of repurposing a commercial Business Intelligence platform for engineering analysis in conjunction with the platform's intended business reporting and analysis functions.

Operations Efficiency↗
NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗
NASA NTRSDate not supplied

Move to Talk, Talk to Move: Tightly Integrated Communication and Control for Coordinated Swarms of Small Spacecraft

The Move to Talk, Talk to Move: Tightly Integrated Communication and Control for Coordinated Swarms of Small Spacecraft project will build on existing research on collaborative autonomy of multi-agent systems and design techniques that will enable coordinated communication and control of spacecraft. The success of many space exploration and science missions hinges on real-time monitoring of time-varying and/or geographically distributed phenomena. This monitoring can be achieved using a swarm of small spacecraft, which collect data about the environment and share information within the swarm of spacecraft. Current space exploration missions typically issue commands to control each spacecraft individually from Earth, and the data gathered by each spacecraft is also transmitted to Earth separately via X-band communication over the Deep Space Network (DSN). This approach is expensive, slow, and unreliable. Many coordinated tasks amongst a swarm of autonomous agents (or, specifically, small spacecraft) rely on communication. Existing control, estimation, and decision algorithms often assume that mostly reliable communications are available; however, this is often not the case in actual environments and thus is a barrier to operating swarms of small spacecraft.

Qi Han↗
NASA NTRS2020

Communications with Curiosity during solar conjunction

This study examines the methodology for operating and communicating with NASA's Curiosity Rover (MSL) during the 2019 solar conjunction. For MSL, solar conjunction occurs when the viewing angle between the Sun and Mars from Earth's perspective falls below 3 degrees, which occurs roughly every two Earth years and lasts for about two weeks. This presents a challenge for engineers operating a vehicle on Mars because the degraded signal to noise ratio disrupts data flow between Earth and the spacecraft. As a result, operators designate a command moratorium in which no commands are sent to the rover and instead design long-term plans that are uplinked weeks in advance (rather than the nominal case of daily uplinks). Coordinating communications with the rover leading up to and following conjunction requires negotiations with several orbiters, another lander, and the Deep Space Network (DSN) each with their own set of constraints. It is the Strategic Comm Planning T coordination, which acts as a baseline for the conjunction Comm team faced additional complications such as the arrival of two new spacecraft at Mars - Roscosmos ExoMars Trace Gas Orbiter (TGO).

Laubach, Sharon↗
DOE OSTI2019

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical frameworks that describe known, relevant phenomena that govern the microstructural evolution processes during neutron irradiation (such as recrystallization, and pore size, distribution and morphology). Current empirical modeling approaches, however, do not represent all irradiation test data well. We develop a machine learning approach to thermal conductivity modeling that does not require a priori knowledge of a specific material microstructure and system of interest. Our approach allows re-searchers to probe dependency of thermal conductivity on a variety of reactor operating and material conditions. The purpose of building such a model is to allow for improved predictive capabilities linking structure-property-processing-performance relationships in the system of interest (here, irradiated nuclear fuel), which could lead to improved experimental test planning and characterization. The uranium-molybdenum system is the fuel system studied in this work, and historic irradiation test data is leveraged for model development. Our model achieved a mean absolute percent error of approximately 10%. Results indicate our model generalizes well to never before seen data, and thus use of deep learning methods for material property predictions from limited, historic irradiation data is a viable approach. This work challenges the current paradigm in materials science, where material property models are based on limited experimental data fitted to low-dimensionality phenomenological models, or by simulations in which fundamental equations are explicitly solved. The work presented here aims to demonstrate the potential and limitations of machine learning in the field of materials science and material property modeling.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Data-Driven Multi-Step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1985

JPL emergency support of TDRSS and compatible satellites

The Tracking and Data Relay Satellite System (TDRSS) will consist of three identical satellites in geosynchronous orbits and a dedicated ground receiving station. The first two satellites (TDRS East and TDRS West) will form the operational TDRS service network providing near-global real-time user satellite coverage. The third TDRS satellite will act as an in-orbit spare. Since the TDRSS satellite are supported by a single ground station, a method of providing emergency support for TDRS and user satellites is needed. The support to be provided by JPL's Deep Space Network is described.

Fanelli, N. A.↗
NASA NTRS1990

An 8.4-GHz dual-maser front-end system for Parkes reimplementation

An 8.4-GHz front-end system consisting of a feedhorn, a waveguide feed assembly, dual masers, and downconverters was reimplemented at Parkes as part of the Parkes Canberra Telemetry Array for the Voyager Neptune encounter. The front-end system was originally assembled by the European Space Agency and installed on the Parkes antenna for the Giotto project. It was also used on a time-sharing basis by the Deep Space Network as part of the Parkes Canberra Telemetry Array to enhance the data return from the Voyager Uranus encounter. At the conclusion of these projects in 1986, part of the system was then shipped to JPL on loan for reimplementation at Parkes for the Voyager Neptune encounter. New design and implementation required to make the system operable at Parkes included new microwave front-end control cabinets, closed-cycle refrigeration monitor system, noise-adding radiometer system, front-end controller assembly, X81 local oscillator multiplier, and refurbishment of the original dual 8.4-GHz traveling-wave masers and waveguide feed system. The front-end system met all requirements during the encounter and was disassembled in October 1989 and returned to JPL.

Trowbridge, D. L.↗
NASA NTRS2014

Is Model-Based Development a Favorable Approach for Complex and Safety-Critical Computer Systems on Commercial Aircraft?

A system is safety-critical if its failure can endanger human life or cause significant damage to property or the environment. State-of-the-art computer systems on commercial aircraft are highly complex, software-intensive, functionally integrated, and network-centric systems of systems. Ensuring that such systems are safe and comply with existing safety regulations is costly and time-consuming as the level of rigor in the development process, especially the validation and verification activities, is determined by considerations of system complexity and safety criticality. A significant degree of care and deep insight into the operational principles of these systems is required to ensure adequate coverage of all design implications relevant to system safety. Model-based development methodologies, methods, tools, and techniques facilitate collaboration and enable the use of common design artifacts among groups dealing with different aspects of the development of a system. This paper examines the application of model-based development to complex and safety-critical aircraft computer systems. Benefits and detriments are identified and an overall assessment of the approach is given.

Torres-Pomales, Wilfredo↗