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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

ATLAS LTCS Vertically Challenged System Lessons Learned

Re-planning of LTCS TVAC testing and supporting RTA (Receiver Telescope Assembly) Test Plan and Procedure document preparation. The Laser Thermal Control System (LTCS) is designed to maintain the lasers onboard Advanced Topographic Laser Altimeter System (ATLAS) at their operational temperatures. In order to verify the functionality of the LTCS, a thermal balance test of the thermal hardware was performed. During the first cold start of the LTCS, the Loop Heat Pipe (LHP) was unable to control the laser mass simulators temperature. The control heaters were fully on and the loop temperature remained well below the desired setpoint. Thermal analysis of the loop did not show these results. This unpredicted behavior of the LTCS was brought up to a panel of LHP experts. Based on the testing and a review of all the data, there were multiple diagnostic performed in order to narrow down the cause. The prevailing theory is that gravity is causing oscillating flow within the loop, which artificially increased the control power needs. This resulted in a replan of the LTCS test flow and the addition of a GSE heater to allow vertical operation.

Loop Heat Pipe↗
DOE OSTI2021

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

We report a distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

X-29 flight control system: Lessons learned

Two X-29A aircraft were flown at the NASA Dryden Flight Research Center over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35-percent negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low- and high-angle-of-attack programs. Through out the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

Clarke, Robert↗
DOE OSTI2023

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

NASA Ares I Launch Vehicle Roll and Reaction Control Systems Lessons Learned

On April 15, 2010 President Barak Obama made the official announcement that the Constellation Program, which included the Ares I launch vehicle, would be canceled. NASA s Ares I launch vehicle was being designed to launch the Orion Crew Exploration Vehicle, returning humans to the moon, Mars, and beyond. It consisted of a First Stage (FS) five segment solid rocket booster and a liquid J-2X Upper Stage (US) engine. Roll control for the FS was planned to be handled by a dedicated Roll Control System (RoCS), located on the connecting interstage. Induced yaw or pitch moments experienced during FS ascent would have been handled by vectoring of the booster nozzle. After FS booster separation, the US Reaction Control System (ReCS) would have provided the US Element with three degrees of freedom control as needed. The lessons learned documented in this paper will be focused on the technical designs and producibility of both systems along with the partnership between NASA and Boeing, who was on contract to build the Ares I US Element, which included the FS RoCS and US ReCS. In regards to partnership, focus will be placed on integration along with technical work accomplished by Boeing with special emphasis on each task order. In summary, this paper attempts to capture key lessons learned that should be helpful in the development of future launch vehicle RCS designs.

Butt, Adam↗
NASA NTRSDate not supplied

Lessons Learned in Systems Engineering Availability and Recommendations for Mission Technical Leaders

In spaceflight missions at Goddard Space Flight Center (GSFC), the Mission System Engineer (MSE) is the technical leader of the overall engineering team and also is the Independent Engineering Technical Authority. The responsibility of this role includes the definition of the mission design architecture, concept of operations, and mission requirements, management of risk throughout the development, and verification and validation of the final system performance and function amongst other duties. This responsibility inherently requires time management, enabling focus on a balanced development with appropriate risk. Time is the most valuable resource of the MSE. The system engineer’s availability to interact with the development team (often product or component design leads and technicians, often in different worksites) to discover and mitigate mission risks during development is key to mission success. This paper presents examples from Lunar Reconnaissance Orbiter, Landsat 9, and Neutron star Interior Composition ExploreR (NICER) which represent in-house and out-of-house hardware builds. These examples demonstrate how interactions between the Mission Systems Engineers and other project and partner engineers result in discovery of critical risks, leading to early mitigation with significant cost and performance savings. These three missions would have suffered test failures or on-orbit failures had their MSEs not set aside time to visit engineers and technicians that were working on key pieces of space flight hardware. Availability is more than just time; it is openness to listen to concerns and questions. It begins by building a level of trust in the team that it is safe to ask questions or share concerns without the fear of blame or additional workload. It also requires enabling informal conversations (over lunch, coffee, in the clean room, or at the team members desk, etc.) where key information can be exchanged, and team members may even provide an easy-to-implement mitigation idea for another subsystem. Availability is a highly valuable commodity and completely non-obvious to protect and optimize. The natural tendency of engineers is to keep themselves busy with solving problems that they know about. This paper is encouraging MSEs to resist this tendency to try and solve all the complex problems themselves and actively devote daily time to learning and solving problems that are found with informal communications with other team members.

Lessons Learned↗
NASA NTRS1992

Some problems with the design of self-learning management systems

In this paper some problems in the design of management systems for complex objects are discussed. Considering the absence of adequate models and the fact that human expertise in the management of non-stationary objects becomes obsolete quickly, the use of self-learning together with a two-step optimization of on-line control rules is suggested. To prepare for the object analysis, a set of definitions has been proposed. Traditional and fuzzy sets approaches are used in the analysis. To decrease the reaction time of the control system, we propose the development of control rules without feedback.

Flikop, Ziny↗
NASA NTRS1994

A hypertext system that learns from user feedback

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation). Besides providing an hypertext interface for browsing large documents, the CID system automatically acquires and reuses the context in which previous searches were appropriate. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. Thus, the user continually augments and refines the intelligence of the retrieval system. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time. We successfully tested the CID system with users of the Space Station Freedom requirements documents. We are currently extending CID to other application domains (Space Shuttle operations documents, airplane maintenance manuals, and on-line training). We are also exploring the potential commercialization of this technique.

Mathe, Nathalie↗
DOE OSTI2021

Artificial Intelligence for Energy Systems Cybersecurity

Artificial intelligence and machine learning systems have the potential to influence the future design and implementation of cybersecurity systems for the power grid. These systems may enhance the overall operation of the power system by leveraging and making sense of massive amounts of data. However, we must also understand how AI/ML will need to be protected from cyber threat actors. We discuss the existing insights the NREL team has developed using AI/ML systems and then present resources including ESIF and the Cyber Energy Emulation Platform that can be used to generate training data and insights. We end by offering suggestions on priority research paths for AI in cybersecurity.

artificial intelligence↗
NASA NTRS2000

Crew/Automation Interaction in Space Transportation Systems: Lessons Learned from the Glass Cockpit

The progressive integration of automation technologies in commercial transport aircraft flight decks - the 'glass cockpit' - has had a major, and generally positive, impact on flight crew operations. Flight deck automation has provided significant benefits, such as economic efficiency, increased precision and safety, and enhanced functionality within the crew interface. These enhancements, however, may have been accrued at a price, such as complexity added to crew/automation interaction that has been implicated in a number of aircraft incidents and accidents. This report briefly describes 'glass cockpit' evolution. Some relevant aircraft accidents and incidents are described, followed by a more detailed description of human/automation issues and problems (e.g., crew error, monitoring, modes, command authority, crew coordination, workload, and training). This paper concludes with example principles and guidelines for considering 'glass cockpit' human/automation integration within space transportation systems.

Rudisill, Marianne↗
NASA NTRS1990

Analytical learning and term-rewriting systems

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques.

Laird, Philip↗
DOE OSTI2021

A Hybrid System for Learning Classical Data in Quantum States

Deep neural network-powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore’s Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high intensity training data. Meanwhile, quantum computing has exhibited its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrates quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world’s largest supercomputers. To this end, quantum-based learning becomes an area of interest, with the promising of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 66.67% faster.

Stein, Samuel A.↗