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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Real-time antenna fault diagnosis experiments at DSS 13

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

Mellstrom, J.↗
NASA NTRS1999

The Future of the Deep Space Network: Technology Development for K2-Band Deep Space Communications

Projections indicate that in the future the number of NASA's robotic deep space missions is likely to increase significantly. A launch rate of up to 4-6 launches per year is projected with up to 25 simultaneous missions active [I]. Future high resolution mapping missions to other planetary bodies as well as other experiments are likely to require increased downlink capacity. These future deep space communications requirements will, according to baseline loading analysis, exceed the capacity of NASA's Deep Space Network in its present form. There are essentially two approaches for increasing the channel capacity of the Deep Space Network. Given the near-optimum performance of the network at the two deep space communications bands, S-Band (uplink 2.025-2.120 GHz, downlink 2.2-2.3 GHz), and X-Band (uplink 7.145-7.19 GHz, downlink 8.48.5 GHz), additional improvements bring only marginal return for the investment. Thus the only way to increase channel capacity is simply to construct more antennas, receivers, transmitters and other hardware. This approach is relatively low-risk but involves increasing both the number of assets in the network and operational costs.

Bhanji, Alaudin M.↗
NASA NTRS2001

Considerations and Architectures for Inter-Satellite Communications in Distributed Spacecraft Systems

This paper will identify the important characteristics and requirements necessary for inter-satellite communications in distributed spacecraft systems and present analysis results focusing on architectural and protocol comparisons. Emerging spacecraft systems plan to deploy multiple satellites in various "distributed" configurations ranging from close proximity formation flying to widely separated constellations. Distributed spacecraft configurations provide advantages for science exploration and operations since many activities useful for missions may be better served by distributing them between spacecraft. For example, many scientific observations can be enhanced through spatially separated platforms, such as for deep space interferometry. operating multiple distributed spacecraft as a mission requires coordination that may be best provided through inter-satellite communications. For example, several future distributed spacecraft systems envision autonomous operations requiring relative navigational calculations and coordinated attitude and position corrections. To conduct these operations, data must be exchanged between spacecraft. Direct cross-links between satellites provides an efficient and practical method for transferring data and commands. Unlike existing "bent-pipe" relay networks supporting space missions, no standard or widely-used method exists for cross-link communications. Consequently, to support these future missions, the characteristics necessary for inter-satellite communications need to be examined. At first glance, all of the missions look extremely different. Some missions call for tens to hundreds of nano-satellites in constant communications in close proximity to each other. Other missions call for a handful of satellites communicating very slowly over thousands to hundreds of thousands of kilometers. The paper will first classify distributed spacecraft missions to help guide the evaluation and definition of cross-link architectures and approaches. Based on this general classification, the paper will examine general physical layer parameters, such as frequency bands and data rates, necessary to support the missions. The paper will also identify classes of communication architectures that may be employed, ranging from fully distributed to centralized topologies. Numerous factors, such as number of spacecraft, must be evaluated when attempting to pick a communications architecture. Also important is the stability of the formation from a communications standpoint. For example, do all of the spacecraft require equal bandwidth and are spacecraft allowed to enter and leave a formation? The type of science mission being attempted may also heavily influence the communications architecture. In addition, the paper will assess various parameters and characteristics typically associated with the data link layer. The paper will analyze the performance of various multiple access techniques given the operational scenario, requirements, and communication topologies envisioned for missions. This assessment will also include a survey of existing standards and their applicability for distributed spacecraft systems. An important consideration includes the interoperability of the lower layers (physical and data link) examined in this paper with the higher layer protocols(network) envisioned for future space internetworking. Finally, the paper will define a suggested path, including preliminary recommendations, for defining and developing a standard for intersatellite communications based on the classes of distributed spacecraft missions and analysis results.

Edwards, Bernard↗
NASA NTRS2001

Ultra Low Power, Radiation Tolerant UHF Radio Technologies for In Situ Communication Applications

For future deep space missions, significant reductions in the mass and power requirements for short-range telecommunication systems will be critical in enabling a wide variety of new mission concepts. These possibilities include penetrators, gliders, miniature rovers, and sensor networks. Under joint funding from NASA's Cross Enterprise and JPL's Telecommunications and Mission technology programs, recent development activity has focused on the design of ultralow mass and power transceiver systems and subsystems suitable for operation in a flight environment. For these efforts, the functionality of the transceiver has been targeted towards a specific Mars communications scenario. However, the overall architecture is well suited to any short or medium range application where a remote probe will aperiodically communicate with a base station, possibly an orbiter, for the eventual purpose of relaying science information back to Earth. In 2001, these sponsors have been augmented with collaborative expertise and funding from JPL's Center for Integrated Space Microsystems in order to migrate existing concepts and designs to a System on a Chip (SOAC) solution. Additional information is contained in the original extended abstract.

Lay, N. E.↗
NASA NTRS2019

Mars Exploration Rover Opportunity End of Mission Report

The Mars Exploration Rover (MER) Opportunity landed on Meridiani Planum on 25 January 2004 for a prime mission designed to last three months (90 sols). After more than fourteen years operating on the surface of Mars, the last communication from Opportunity occurred on sol 5111 (10 June, 2018) when a major dust storm reduced power on the solar panels to the point where further communications were not possible. Following the cessation of the dust storm several weeks later, the MER project radiated over 1000 commands to Mars in an attempt to elicit a response from the rover. Attempts were made utilizing the Deep Space Network X-Band and UHF relay via both Mars Odyssey and the Mars Reconnaissance Orbiter. Search and recovery efforts concluded on 12 February, 2019. It is the MER project’s assessment that the environmental window in which it would be most probable to recover Opportunity had passed by that time and that the rover would succumb to the extreme environmental conditions experienced during a winter on Mars. This report summarizes the major science accomplishments throughout the fourteen years of this mission, with a detailed focused on recent science accomplishments during the last extended mission (EM-11). This report also describes the mission engineering accomplishments and specific actions taken during the attempt to recover the vehicle after communications were lost during the major dust storm.

Callas, John L.↗
NASA NTRS1993

Cost and Reliability Study for a Large Array of Small Reflector Antennas for JPL/NASA Deep Space Network (DSN)

A study is performed to estimate the cost of an array of small aperture reflector antennas to be used instead or in conjunction with the current large reflector antennas of the JPL/NASA Deep Space Network (DSN), for servicing present and future NASA missions which involve smaller spacecraft with limited power and smaller, lower gain antennas. The advantages of the array configuration in terms of cost and reliability are studied. A probabilistic determination of the reliability and availability of the array as a function of the number of array elements and the availability of individual array elements is made. The impact of additional marginal elements on the operational availability of the array is studied. Parametric cost and reliability plots are presented and directions for further investigation are outlined.

Resch, G.↗
DOE OSTI2021

A hub and spoke approach to optimizing energy wheeling of renewable resources

The deployment of zero carbon renewable energy sources needs to increase significantly to support the goal of net zero greenhouse gas emissions by 2050. At the same time energy end use needs to decarbonize. This will change both energy supply and energy demand patterns, requiring the energy delivery infrastructure (grid-based transmission circuits) to become increasingly flexible to maintain security of supply everywhere and always. The integration of zero carbon renewable energy requires cross-border and cross energy system coupling and a fit-for-purpose design. Nowadays, energy systems are planned, designed and operated in silos with a strong national focus. However, large-scale offshore wind production needs to be transported to deep inland locations, across country borders. The increased peak generation capacity of renewable energy sources will, at times, significantly exceed demand (Matthew Langholtz, 2020). The traditional solution of continuously reinforcing and extending the electricity grid is not sustainable from a cost and societal perspective. This paper will, however, propose a deterministic approach on how networked (interconnected grid) Points of receipt (POR) to Points of Delivery (POD) can be optimized for wheeling renewable energy resources while minimizing energy cost with a hub and spoke approach. The statistical approach will be done via using existing daily energy market clearing prices, available transmission capacity and firm daily transmission prices in open access energy markets. Renewable energy targets, including specific offshore wind targets, need to be in line with the ramp-up as implied by the Paris Agreement. These targets are required to provide industry with a secure market outlook that allows them to build up supply chains accordingly. Optimizing wheeled energy paths from carbon neutral resources such as renewables make them not only cost competitive on the unit commitment stack, but also more accessible on the dispatch stack to other carbon heavy forms of generation such as coal and natural gas turbines (Matthew Langholtz, 2020). This correlates to maximizing renewable resource inertia (wind, solar, biomass) within an interconnected grid without having to consider additional expansion of resources via land purchases and de-forestation.

Mukherjee, Srijib↗
DOE OSTI2022

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. Here, this paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human–machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.

LSTM modeling↗
DOE OSTI2023

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.

36 MATERIALS SCIENCE↗
NASA NTRS1995

The Deep Space Network stability analyzer

A stability analyzer for testing NASA Deep Space Network installations during flight radio science experiments is described. The stability analyzer provides realtime measurements of signal properties of general experimental interest: power, phase, and amplitude spectra; Allan deviation; and time series of amplitude, phase shift, and differential phase shift. Input ports are provided for up to four 100 MHz frequency standards and eight baseband analog (greater than 100 kHz bandwidth) signals. Test results indicate the following upper bounds to noise floors when operating on 100 MHz signals: -145 dBc/Hz for phase noise spectrum further than 200 Hz from carrier, 2.5 x 10(exp -15) (tau =1 second) and 1.5 x 10(exp -17) (tau =1000 seconds) for Allan deviation, and 1 x 10(exp -4) degrees for 1-second averages of phase deviation. Four copies of the stability analyzer have been produced, plus one transportable unit for use at non-NASA observatories.

Breidenthal, Julian C.↗
NASA NTRS2013

Dawn Orbit Determination Team: Modeling and Fitting of Optical Data at Vesta

The Dawn spacecraft was launched on September 27th, 2007. Its mission is to consecutively rendezvous with and observe the two largest bodies in the main asteroid belt, Vesta and Ceres. It has already completed over a year's worth of direct observations of Vesta (spanning from early 2011 through late 2012) and is currently on a cruise trajectory to Ceres, where it will begin scientific observations in mid-2015. Achieving this data collection required careful planning and execution from all Dawn operations teams. Dawn's Orbit Determination (OD) team was tasked with reconstruction of the as-flown trajectory as well as determination of the Vesta rotational rate, pole orientation and ephemeris, among other Vesta parameters. Improved knowledge of the Vesta pole orientation, specifically, was needed to target the final maneuvers that inserted Dawn into the first science orbit at Vesta. To solve for these parameters, the OD team used radiometric data from the Deep Space Network (DSN) along with optical data reduced from Dawn's Framing Camera (FC) images. This paper will de-scribe the initial determination of the Vesta ephemeris and pole using a combination of radiometric and optical data, and also the progress the OD team has made since then to further refine the knowledge of Vesta's body frame orientation and rate with these data.

imagery↗
NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗
NASA NTRS2015

The Role of Margin in Link Design and Optimization

Link analysis is a system engineering process in the design, development, and operation of communication systems and networks. Link models that are mathematical abstractions representing the useful signal power and the undesirable noise and attenuation effects (including weather effects if the signal path transverses through the atmosphere) that are integrated into the link budget calculation that provides the estimates of signal power and noise power at the receiver. Then the link margin is applied which attempts to counteract the fluctuations of the signal and noise power to ensure reliable data delivery from transmitter to receiver. (Link margin is dictated by the link margin policy or requirements.) A simple link budgeting approach assumes link parameters to be deterministic values typically adopted a rule-of-thumb policy of 3 dB link margin. This policy works for most S- and X-band links due to their insensitivity to weather effects. But for higher frequency links like Ka-band, Ku-band, and optical communication links, it is unclear if a 3 dB link margin would guarantee link closure. Statistical link analysis that adopted the 2-sigma or 3-sigma link margin incorporates link uncertainties in the sigma calculation. (The Deep Space Network (DSN) link margin policies are 2-sigma for downlink and 3-sigma for uplink.) The link reliability can therefore be quantified statistically even for higher frequency links. However in the current statistical link analysis approach, link reliability is only expressed as the likelihood of exceeding the signal-to-noise ratio (SNR) threshold that corresponds to a given bit-error-rate (BER) or frame-error-rate (FER) requirement. The method does not provide the true BER or FER estimate of the link with margin, or the required signalto-noise ratio (SNR) that would meet the BER or FER requirement in the statistical sense. In this paper, we perform in-depth analysis on the relationship between BER/FER requirement, operating SNR, and coding performance curve, in the case when the channel coherence time of link fluctuation is comparable or larger than the time duration of a codeword. We compute the "true" SNR design point that would meet the BER/FER requirement by taking into account the fluctuation of signal power and noise power at the receiver, and the shape of the coding performance curve. This analysis yields a number of valuable insights on the design choices of coding scheme and link margin for the reliable data delivery of a communication system - space and ground. We illustrate the aforementioned analysis using a number of standard NASA error-correcting codes.

Cheung, K.↗
DOE OSTI2024

A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma

Undocumented Orphaned Wells (UOWs) are wells without an operator that have limited or no documentation with regulatory authorities. An estimated 310,000 to 800,000 UOWs exist in the United States (US), whose locations are largely unknown. These wells can potentially leak methane and other volatile organic compounds to the atmosphere, and contaminate groundwater. In this study, we developed a novel framework utilizing a state-of-the-art computer vision neural network model to identify the precise locations of potential UOWs. The U-Net model is trained to detect oil and gas well symbols in georeferenced historical topographic maps, and potential UOWs are identified as symbols that are further than 100 m from any documented well. A custom tool was developed to rapidly validate the potential UOW locations. We applied this framework to four counties in California and Oklahoma, leading to the discovery of 1301 potential UOWs across >40,000 km 2 . We confirmed the presence of 29 UOWs from satellite images and 15 UOWs from magnetic surveys in the field with a spatial accuracy on the order of 10 m. This framework can be scaled to identify potential UOWs across the US since the historical maps are available for the entire nation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗