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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

ACTS Operations Extended Through a University-Based Consortium

The Advanced Communications Technology Satellite (ACTS) program was slated for decommissioning in October 2000. With plans in place to move the spacecraft to an orbital graveyard and then shut the system down, NASA was challenged to consider the feasibility of extending operations for education and research purposes provided that an academic organization would be willing to cover operations costs. This was determined to be viable, and in the fall of 2000, NASA announced that it would consider extending operations. On March 19, 2001, NASA, the Ohio Board of Regents, and the Ohio University signed a Space Act Agreement to continue ACTS operations for 2 more years with options to extend operations up to a total of 4 years. To accomplish this, the Ohio University has formed a university-based consortium, the Ohio Consortium for Advanced Communications Technology (OCACT), and acts as the managing member. The Ohio University is responsible for the full reimbursement of NASA's operations costs, and does this through consortium membership. NASA retains the operating license of the spacecraft and has two contractors supporting spacecraft and master control station operations. This flexible arrangement between NASA and academia allows the education community to access a large communications satellite for learning about spacecraft operations and to use the system's transponders for communications applications. It also allows other organizations, such as commercial companies, to become consortium members and use the ACTS wideband Ka-band (30/20 GHz) payload. From the consortium members, six areas of interest have been identified.

Bauer, Robert A.↗
NASA NTRS2011

International Space Station Major Constituent Analyzer On-Orbit Performance

The Major Constituent Analyzer (MCA) is an integral part of the International Space Station (ISS) Environmental Control and Life Support System (ECLSS). The MCA is a mass spectrometer-based instrument designed to provide critical monitoring of six major atmospheric constituents; nitrogen, oxygen, hydrogen, carbon dioxide, methane, and water vapor. These gases are sampled continuously and automatically in all United States On-Orbit Segment (USOS) modules via the Sample Distribution System (SDS). The MCA is the primary tool for management of atmosphere constituents and is therefore critical for ensuring a habitable ISS environment during both nominal ISS operations and campout EVA preparation in the Airlock. The MCA has been in operation in the US Destiny Laboratory Module for over 10 years, and a second MCA has been delivered to the ISS for Node 3 operation. This paper discusses the performance of the MCA over the two past year, with particular attention to lessons learned regarding the operational life of critical components. Recent data have helped drive design upgrades for a new set of orbit-replaceable units (ORUs) currently in production. Several ORU upgrades are expected to increase expected lifetimes and reliability.

Gardner, Ben D.↗
DOE OSTI2022

Operator Insights and Usability Evaluation of Machine Learning Assistance for Power Grid Contingency Analysis

Introducing machine learning (ML) assistance into any established process comes with adoption barriers, including entrenched procedures, technological and human readiness levels, human-machine trust, and work culture resistance to change. These barriers are even greater in critical operations such as operating a national or regional power grid, in which both regulatory frameworks and the importance of maintaining reliability levels causes additional resistance to the adoption of new computational support. Developers of future systems and job aides must consider not only technical aspects, but also whether new systems are usable by power system operators. This work presents the methodology and results of a study to evaluate the usability and readiness of a prototype recommender system for power grid contingency analysis. We explore operator cognitive load and evaluate operator performance when solving a collection of scenarios both with and without recommender assistance. We also examine operator trust in the system. We report insights gained on the readiness of the system using a collection of evaluation techniques.

Human-Machine Teaming, Power Systems, usability ev↗
NASA NTRSDate not supplied

Automated Software for Crewed Spacecraft - Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles - the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a unique private-public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed along with evolution of the system in preparation for the second uncrewed test flight. Focus will center on how operations has learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft or mission anomalies.

Robert C. Dempsey↗
DOE OSTI2020

A physics-informed operator regression framework for extracting data-driven continuum models

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.

42 ENGINEERING↗
DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Cassini Spacecraft Attitude Control System: Flight Performance and Lessons Learned, 1997-2017

A sophisticated interplanetary spacecraft, Cassini/Huygens was launched on October 15, 1997. Since achieving orbit at Saturn in 2004, Cassini has collected science data throughout its four-year prime mission (2004–08), and has since been approved for first and second extended missions through September 2017. The Cassini Attitude and Articulation Control Subsystem (AACS) is perhaps the spacecraft subsystem that must satisfy the most mission and science pointing requirements. Since launch, the performance of the Cassini AACS design has been superb. All key mission and science requirements are met with significant margins. An overview of the flight performance of the Cassini attitude control system as well as AACS mission operation-centric lessons learned, from launch to 2017, are described by topics. Many of these lessons learned should be applicable to the safe operations of other interplanetary missions. Processes taken by the AACS operation team to guard against “human” errors are also outlined in this paper.

Lee, Allan Y.↗
DOE OSTI2024

Application of deep learning methods for beam size control during user operation at the Advanced Light Source

Past research at the Advanced Light Source (ALS) provided a proof-of-principle demonstration that deep learning methods could be effectively employed to compensate for the significant perturbations to the transverse electron beam size induced by user-controlled adjustments of the insertion devices. However, incorporating these methods into the ALS’ daily operations has faced notable challenges. The complexity of the system’s operational requirements and the significant upkeep demands has restricted their sustained application during user operation. Here, we introduce the development of a more robust neural network (NN)-based algorithm that utilizes a novel online fine-tuning approach and its systematic integration into the day-to-day machine operations. Our analysis emphasizes the process of NN model selection, demonstrates the superior performance of the NN-based method over traditional feedback methods, and examines the effectiveness and resilience of the new algorithm during user-operation scenarios. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly

Vulnerability management is a very challenging and time-consuming task. For many organizations, security operators need to learn about the properties of vulnerabilities to prioritize and mitigate them. Due to the lack of automated tools for vulnerability assessment, operators usually manually search for and read related information from sources online. Recent advances in large language models, like ChatGPT, open up an opportunity for time savings and may prompt operators to use these models as vulnerability information sources. In this work, we evaluate the ability of ChatGPT and several of its siblings to accurately answer user questions about vulnerability properties as well as to provide information for how to mitigate a vulnerability. We also explore their summarization capabilities when multiple vulnerability advisory documents are provided. We find that the models perform poorly on information retrieval tasks, but they perform quite well on summarization.

McClanahan, Kylie↗
DOE OSTI2022

Learning Local Volt/VAR Controllers Toward Efficient Network Operation with Stability Guarantees: Preprint

This paper considers the problem of voltage regulation in distribution network. The primary motivation is to keep voltages within pre-assigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises of two main steps. In the first, exploiting historical data and for each DER, we learn a function representing desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme and numerical simulations illustrate the effectiveness of the proposed approach.

data-driven control↗
NASA NTRS2023

ECS Artemis II Upgrades

The National Aeronautics and Space Administration (NASA) actively works to further the expectations of space exploration and research. NASA has been able to develop innovative technology and methods that has allowed for continued discovery and innovation. NASA is in the midst of work for the Artemis mission, which is to return to the moon in an effort to prepare for future Mars exploration. After the successful launch of Artemis I last November, our sights have shifted to Artemis II, which is set to launch next year and bring humans into the lunar orbit for the first time in over fifty years. Artemis II will be the first crewed mission for the program and will represent another step forward in our mission to advance our knowledge of the universe around us. From there, the Artemis program will move onto building a permanent site on the moon that will allow us to eventually reach Mars. During my time at NASA, I was able to work with the NE-XF Branch, also known as the Environmental and Life Support Systems Branch. I specifically worked with the Environmental Control Systems (ECS) team. During my time here, construction on the system in the Vehicle Assembly Building (VAB) and at Launchpad 39B have been progressing at full force. ECS is used to provide processed and purged air at specific temperatures, pressures, and humidity’s to fulfill requirements necessary to support Orion and the SLS. While the Pad has been undergoing upgrades from the original Artemis I configuration, the VAB has a completely new ECS very similar to it. While both systems have been undergoing upgrades, we have been able to transition into testing the systems as we prepare for stacking in the VAB early next year. My role has allowed me to learn about how the systems work and function through walkdowns and visits out to both the Pad and the VAB. I’ve been able to see firsthand how the system operates and have learned how the system affects the vehicle. I’ve been able to shadow my mentor, my coworkers, and COMET operators to oversee the construction efforts of the system along with the testing of the software and the system itself. I even was able to aid in testing at the console myself at Pad 39B. Additionally, I am also revising and reviewing displays for Artemis IV that will be used to remotely control parts of the system. Eventually these displays will be used to support the future of Artemis.

Monique Toon↗
NASA NTRS1987

Performance improvement of robots using a learning control scheme

Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

Krishna, Ramuhalli↗
NASA NTRS2012

Defining Learning Space in a Serious Game in Terms of Operative and Resultant Actions

This paper explores the distinction between operative and resultant actions in games, and proposes that the learning space created by a serious game is a function of these actions. Further, it suggests a possible relationship between these actions and the forms of cognitive load imposed upon the game player. Association of specific types of cognitive load with respective forms of actions in game mechanics also presents some heuristics for integrating learning content into serious games. Research indicates that different balances of these types of actions are more suitable for novice or experienced learners. By examining these relationships, we can develop a few basic principles of game design which have an increased potential to promote positive learning outcomes.

Martin, Michael W.↗
NASA NTRS2004

Aerospace Meteorology Lessons Learned Relative to Aerospace Vehicle Design and Operations

Aerospace Meteorology came into being in the 1950s as the development of rockets for military and civilian usage grew in the United States. The term was coined to identify those involved in the development of natural environment models, design/operational requirements, and environment measurement systems to support the needs of aerospace vehicles, both launch vehicles and spacecraft. It encompassed the atmospheric environment of the Earth, including Earth orbit environments. Several groups within the United States were active in this area, including the Department of Defense, National Aeronautics and Space Administration, and a few of the aerospace industry groups. Some aerospace meteorology efforts were similar to those being undertaken relative to aviation interests. As part of the aerospace meteorology activities a number of lessons learned resulted that produced follow on efforts which benefited from these experiences, thus leading to the rather efficient and technologically current descriptions of terrestrial environment design requirements, prelaunch monitoring systems, and forecast capabilities available to support the development and operations of aerospace vehicles.

Vaughan, William W.↗
DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Updates and Lessons Learned from NuMI Beamline at Fermilab

The Neutrinos at the Main Injector (NuMI) beamline at Fermilab generates an intense muon neutrino beam for the NOvA (NuMI Off-axis 𝜈𝑒 Appearance) long baseline neutrino experiment. Over the years, the NuMI beamline has been pivotal in advancing neutrino physics, providing invaluable data and insights. This presentation offers updates and a comprehensive review of the lessons learned from the operation, maintenance, and monitoring of the NuMI beamline. Key topics include the optimization of beam performance, challenges in maintaining beamline stability, and proposed Machine Learning implementations to enhance monitoring. The talk aims to share best practices and provide a roadmap for future beamline projects, including the Long-Baseline Neutrino Facility (LBNF).

Wickremasinghe, Athula↗
NASA NTRS2016

Health Monitoring Survey of Bell 412EP Transmissions

Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.

mechanical components↗
NASA NTRSDate not supplied

Artemis Deep Space Habitation: Enabling a Sustained Human Presence on the Moon and Beyond

As NASA and its partners’ capabilities for human exploration of deep space continue to mature, so too does its roadmap toward a sustained crewed presence on the surface of the Moon and eventual human missions to Mars. The first launch of the Space Launch System and Orion crew vehicle, the contract award for the first demonstrations of a Human Landing System, and the beginning of construction on the initial elements of the lunar Gateway have marked major milestones toward NASA’s near-term exploration goals: a long-duration outpost in orbit around the Moon and the next footsteps on the lunar surface. At the same time, NASA is in the early phases of planning the capabilities that will be needed for long-term exploration. Among the common elements that will be required by long-duration stays on the lunar surface, transit to Mars, and Martian surface expeditions will be new habitats unlike any flown to date. NASA is currently working on development of both architectures for those habitats and on the technological advancements that will enable them, with an eye toward systems that will not only extend mission operations but also provide for living quarters that will keep the crew happy and healthy throughout their expeditions. Beyond the Gateway habitation needs, these capabilities will need to be defined and advanced to support the initial lunar surface missions and to prepare for human missions to the Mars system. The Surface Habitat is the current concept in consideration to serve as this initial surface habitat that will extend the crew mission durations. It will provide 30-to-60-day habitability for a crew of up to four allowing for the astronauts to explore farther and longer on each visit to the lunar surface. NASA is also currently reviewing opportunities to use current or near-term in-space habitation systems as proving grounds or precursors for keeping astronauts safe and healthy during future transits to Mars. Already, the International Space Station (ISS) is being used for implementation of next-generation life-support systems that will inform those used in exploration habitats, and the operations approach for ISS is providing lessons-learned for future science operations around or on the Moon.1U.S. Government work not protected by U.S. copyright While a suite of habitation concepts is currently under study within NASA, the agency is also working closely with U.S. industry through the Next Space Technologies for Exploration Partnerships (NextSTEP) activity to understand their concepts for commercially provided habitation capabilities as well as close coordination with international partners to understand their desires for in-space and surface habitation. This paper will provide a status of these concepts and partnership activities as well as potential future technology and architecture development paths.

Habitat↗