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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1975

Implementation of the 64-meter-diameter Antennas at the Deep Space Stations in Australia and Spain

The management and construction aspects of the Overseas 64-m Antenna Project in which two 64-m antennas were constructed at the Tidbinbilla Deep Space Communications Complex in Australia, and at the Madrid Deep Space Communications Complex in Spain are described. With the completion of these antennas the Deep Space Network is equipped with three 64-m antennas spaced around the world to maintain continuous coverage of spacecraft operations. These antennas provide approximately a 7-db gain over the capabilities of the existing 26-m antenna nets. The report outlines the project organization and management, resource utilization, fabrication, quality assurance, and construction methods by which the project was successfully completed. Major problems and their solutions are described as well as recommendations for future projects.

Bartos, K. P.↗
NASA NTRS1978

The deep space network

Deep Space Network progress in flight support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations are reported.

Source record↗
NASA NTRS1983

The Telecommunications and Data Acquisition Report

Developments in programs in telecommunication and data acquisition in space communications, radio navigation, radio science, and ground based radio astronomy are reported. Activities of the deep space network (DSN) and its associated ground communication facility (GCF) in planning, supporting research and technology, implementation, and in operations are outlined. The publication of reports on the application of radio interferometry at microwave frequencies for geodynamic measurements are presented. Implementation and operation for searching the microwave spectrum is reported.

Posner, E. C.↗
NASA NTRS1988

The Telecommunications and Data Acquisition Report

In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported. Also included is TDA funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA.

Posner, E. C.↗
NASA NTRS1989

The Telecommunications and Data Acquisition Report

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are presented. Activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) related to DSN advanced systems, systems implementation, and DSN operations are addressed. In addition, recent developments in the NASA SETI (Search for Extraterrestrial Intelligence) sky survey are summarized.

Posner, E. C.↗
NASA NTRS1989

The Telecommunications and Data Acquisition Report

Developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. Activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported in space communications, radio navigation, radio science, and ground-based radio and radar astronomy.

Posner, E. C.↗
NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (Marco) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased data rates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Kahan, Daniel↗
NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (MarCO) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phaased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased datarates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Vilnrotter, Victor↗
DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Conceptual Design of a Communication-Based Deep Space Navigation Network

As the need grows for increased autonomy and position knowledge accuracy to support missions beyond Earth orbit, engineers must push and develop more advanced navigation sensors and systems that operate independent of Earth-based analysis and processing. Several spacecraft are approaching this problem using inter-spacecraft radiometric tracking and onboard autonomous optical navigation methods. This paper proposes an alternative implementation to aid in spacecraft position fixing. The proposed method Network-Based Navigation technique takes advantage of the communication data being sent between spacecraft and between spacecraft and ground control to embed navigation information. The navigation system uses these packets to provide navigation estimates to an onboard navigation filter to augment traditional ground-based radiometric tracking techniques. As opposed to using digital signal measurements to capture inherent information of the transmitted signal itself, this method relies on the embedded navigation packet headers to calculate a navigation estimate. This method is heavily dependent on clock accuracy and the initial results show the promising performance of a notional system.

Anzalone, Evan J.↗
NASA NTRS1973

Space flight operations

Under contract to the National Aeronautics and Space Administration, the Jet Propulsion Laboratory (JPL) of the California Institute of Technology manages and operates the ground facilities required to support unmanned spacecraft in missions to the moon, the planets, and beyond. A worldwide network of tracking stations, known as the Deep Space Network (DSN), was established to communicate with spacecraft. The Mission Control and Computing Center at JPL houses the mission control personnel and the computer facilities that command and control spacecraft in flight. Communications between the MCCC and the tracking stations, as well as communications among the stations, are the responsibility of the DSN Ground Communications Facility, which connects all parts of the ground system with telephone, teletype, and high-speed data lines. The operation of the ground system is outlined, and the various missions which were successfully supported are described.

Source record↗
NASA NTRS1995

How to Take 30 Years Off the Life of an Earth-Orbiter Network

In the mid 1960's the NASA/JPL Deep Space Network installed a global 26-meter antenna network to support a large group of Low Earth Orbiters and Highly Elliptical Orbiters. Although this network was equipped with then state-of-the-art equipment, operations were labor- intensive. A study is discussed which takes a close look at re- engineering the 26-meter antenna network from all aspects.

re-engineering evaluation deep space network↗
DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Advances in Modeling Solar System Internet Structures and their Data Flows

With an ever-increasing presence in space, there is also an increasing burden on existing communications infrastructure. We are heading towards an inflection point where the traditional approach of scheduled, single-path communications for space will no longer be viable. One answer is Delay Tolerant Networking (DTN), which takes the once disparate system of point-to-point links and unifies them in a networked architecture, thereby making communications more scalable. However, much work remains for discovering and harnessing the underlying theory of DTN. For example, in the terrestrial setting the interplay between routing domains is well-understood, however this is not the case in DTNs. In this paper, we build up the fundamental foundations of DTN, with an emphasis on modeling time varying networks and data flows across them, with examples of cross-domain routing in a DTN. A lofty goal of DTN is to enable the so-called Solar System Internet (SSI), which implies a standardized and robust suite of protocols. These protocols include routing across disconnected networks using store, carry, and forward mechanisms, which is necessary due to the disconnections, delays, and mobility intrinsic to space networks. Due to these factors, each of which generalize traditional networking, there is a deep and rich theory of DTNs. Here we build off of past successes to broaden this theory while striving to keep actionable results a goal for future implementations and operations. The approach includes modeling the unicast, broadcast, and multicast communications using the language of hypergraphs, which capture the geometric properties of such networked communications algebraically. Also inherent to these networks is their time-varying nature, particularly given mobility, and hence we also cultivate modeling techniques that respect this time dependence. This leads us to develop models using tools from category theory and algebraic geometry, which provide a language well-suited to describing synchronization and optimization over such networks. We also introduce and study a novel generalization of curvature applicable to time-evolving networks, which provides quantitative controls on diffusion processes on the network. Because an interplanetary network would feature links with propagation delays the preclude discovery (feedback) mechanisms, they will always feature a scheduled component. However, it is beneficial to support discovery where possible. While DTNs do not yet have strong definitions for their analogues of autonomous systems or network areas, we show how to join dynamic and schedule-based routing domains, using the language of sheaves, which marks progress towards such definitions. We conclude with a discussion of the progress made, as well as suggestions for future work.

Delay Tolerant Networking↗