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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Mojave Volatiles Prospector (MVP): Science and Operations Results from a Lunar Polar Rover Analog Field Campaign

The Mojave Volatiles Prospector (MVP) project is a science-driven field program with the goal of producing critical knowledge for conducting robotic exploration of the Moon. MVP feeds science, payload, and operational lessons learned to the development of a real-time, short-duration lunar polar volatiles prospecting mission. MVP achieved these goals through a simulated lunar rover mission to investigate the composition and distribution of surface and subsurface volatiles in a natural and a priori unknown environment within the Mojave Desert, improving our understanding of how to find, characterize, and access volatiles on the Moon.

xGDS real-time science tools↗
NASA NTRS2022

Juno Gravity Science: Five Years of Radio Science Operations with Ka-band Uplink

Since entering orbit on July 4, 2016, the Juno spacecraft has executed 34 closest approach passes of Jupiter, completing the prime mission. During each closest approach, called perijove, the spacecraft comes within 4,000 km of the cloud tops and the motion of the spacecraft becomes perturbed by the gravitational field of Jupiter. These small changes in the motion of the spacecraft are detected using the Juno Gravity Science Instrument by measuring the Doppler shift of the radio link between the Juno spacecraft and NASA’s Deep Space Network (DSN). During a majority of these closest approach passes, the 34-meter DSS-25 antenna transmits simultaneous X-band and Ka-band uplink to the spacecraft. Juno’s onboard X-band transponder and Ka-band translator phase-coherently return the signals back to Earth for reception at the same DSS-25 antenna. The precise frequency of these signals is measured by processing open-loop recordings of the signal. These measurements, characterized by ~5-10 micron/sec accuracies (after calibration of charged particle noise and Earth troposphere), have probed the gravity field of Jupiter to unprecedented precision, allowing for discoveries of Jupiter’s core size and depth of the zonal winds. Successful operations of the instrument during perijoves requires careful planning and coordination between DSN engineers, the Juno project, and the Juno science team. This work discusses the operations of the Juno Gravity Science Instrument after five years of prime mission operations. Lessons learned are documented to be applied to future missions and the Juno extended mission. Although the Juno extended mission formally started on August 1, 2021, on June 7, 2021, the trajectory was modified with a flyby of Ganymede, the third Galilean moon of Jupiter. Gravity and radio science investigations of Jupiter and its moons will continue to play a key role in Juno’s objectives during the extended mission.

Oudrhiri, Kamal↗
NASA NTRSDate not supplied

Training the Powered-Lift Evaluation Pilot

This poster describes a project to prepare pilots for a study assessing novel aircraft automation concepts for electric Vertical Takeoff and Landing (eVTOL) aircraft using NASA’s Vertical Motion Simulator (VMS). By exploring the operational and learning challenges related to transitioning between forward flight and vertical landing, we seek to establish baselines of pilot workload and aircraft handling qualities across varying atmospheric conditions and automation states. The simulated eVTOL design differentiates flight control allocations as a function of airspeed across four speed ranges as the vehicle transitions between fully thrust-borne lift and wing-borne lift. As speed increases, side stick controls command: translational ground speeds, vertical and lateral acceleration, vertical rate, vertical flight path angle, and bank angle. This novel approach to flight control allocation creates a significant learning challenge for pilots. Since initial eVTOL aircraft may have limitations on hover capabilities, automation and flight guidance cues also vary with airspeed to provide efficient landing profiles while still providing cues suitable for cruise flight. The NASA team prepared the study pilots to follow these flight guidance cues along curved Required Navigation Performance (RNP) approaches and along 6o and 12o glide paths to energy-efficient assistive-hover landing and goarounds. The pre-VMS preparation sought to prepare pilots from diverse levels of experience and background. To do this, NASA researchers designed and developed a fixed-based, large field-ofview simulator with terrain, structures, and air traffic. With one day of combined classroom learning and skill development in the fixedbase simulator, pilots were largely able to fly the simulated eVTOL in the VMS with sufficient mastery to provide handling quality assessments using the Cooper-Harper Handling Qualities Rating and workload assessments through the Bedford Workload Scale.

AAM↗
DOE OSTI2025

Emergency vacuum repairs in an aging accelerator: Case studies and lessons learned

Jefferson Lab operates the CEBAF electron accelerator at energies to 12 GeV for the Department of Energy Nuclear Physics program. The CEBAF injector beamline was designed and built in the early 1990s. Although we’ve upgraded and replaced many of the vacuum systems, we still have unique original components installed which operate daily. Over the past 3 years, we have had several vacuum leaks in ageing components leading to emergency repairs on a tight timeline. I’ll discuss the nature of these vacuum component failures, the difficulties in repair due to their ages, the lessons we’ve learned, and how we hope to minimize similar failures going forward.

Stutzman, Marcy [Thomas Jefferson National Acceler↗
NASA NTRS2015

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Machine learning↗
NASA NTRS1994

Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.

Lacaze, Alberto↗
NASA NTRS2009

Operations to Research: Communication of Lessons Learned

This presentation explores ways to build upon previous spaceflight experience and communicate this knowledge to prepare for future exploration. An operational approach is highlighted, focusing on selection and retention standards (disease screening and obtaining medical histories); pre-, in-, and post-flight monitoring (establishing degrees of bone loss, skeletal muscle loss, cardiovascular deconditioning, medical conditions, etc.); prevention, mitigation, or treatment (in-flight countermeasures); and, reconditioning, recovery, and reassignment (post-flight training regimen, return to pre-flight baseline and flight assignment). Experiences and lessons learned from the Apollo, Skylab, Shuttle, Shuttle-Mir, International Space Station, and Orion missions are outlined.

Fogarty, Jennifer↗
DOE OSTI2025

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2019

GOES I-M: A Retrospective Look at Image Navigation and Registration (INR), Jitter and Lessons Learned

The Geostationary Operational Environmental Satellite (GOES) I-M series of spacecraft was the second generation of United States meteorological observational platforms in geosynchronous orbit. They served as the principal Earth- viewing observational platforms for continuously monitoring dynamic weather events from the mid-1990s and into the 21st century. This paper will look back at the program framing key system attributes of the mission, which necessitated a multi-layered development approach to meet stringent meteorological instrument Line-of-Sight (LoS) pointing and pointing stability requirements. The overall approach involved understanding, correcting, and avoiding pointing errors across a broad frequency range including what would typically be called dynamic interaction and jitter. Background information will be provided covering the mission architecture and program drivers. The systems solution for man- aging and mitigating the deleterious influences of on-board disturbances in order to meet the challenging instrument LoS pointing and jitter requirements will be described, along with the ‘first of its kind’ Image Navigation and Registration system. A broad look back at the lessons learned that emerged from the GOES I- M experience will be presented, with the intent of capturing general and specific insights for developers of future missions having stringent payload instrument pointing requirements. These discussions will touch on such critical aspects as defining jitter and related pointing requirements, the importance of early system architectural decisions, understanding and reducing on-board disturbances, the balance of test and analysis, and the imperative for maximizing on-orbit operational flexibility in order to accommodate unexpected dynamic interactions.

Sudey, John↗
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
NASA NTRS2011

Science Operations Development for Field Analogs: Lessons Learned from the 2010 Desert RATS Test

Desert Research and Technology Studies (Desert RATS) is a multi-year series of hardware and operations tests carried out annually in the high desert of Arizona on the San Francisco Volcanic Field. Conducted since 1997, these activities are designed to exercise planetary surface hardware and operations in conditions where long-distance, multi-day roving is achievable. Such activities not only test vehicle subsystems through extended rough-terrain driving, they also stress communications and operations systems and allow testing of science operations approaches to advance human and robotic surface capabilities.

Eppler, D. B.↗
NASA NTRS2011

Transient Plume Model Testing Using LADEE Spacecraft Attitude Control System Operations

We have learned it is conceivable that the Neutral Mass Spectrometer on board the Lunarr Atmosphere Dust Environment Explorer (LADEE) could measure gases from surface-reflected Attitude Control System (ACS) thruster plume. At minimum altitude, the measurement would be maximized, and gravitational influence minimized ("short" time-of-flight (TOF) situation) Could use to verify aspects of thruster plume modeling Model the transient disturbance to NMS measurements due to ACS gases reflected from lunar surface Observe evolution of various model characteristics as measured by NMS Species magnitudes, TOF measurements, angular distribution, species separation effects

Woronowicz, Michael↗
DOE OSTI2025

Lessons from commissioning of the cryogenic system for the Short-Baseline Neutrino Detector at Fermilab

Results from commissioning and first year of operations of the cryogenic system of the Short-Baseline Neutrino Detector (SBND) and its membrane cryostat installed at the Fermi National Accelerator Laboratory are described. The SBND detector is installed in a 200 m$^3$ membrane cryostat filled with liquid argon, which serves both as target and as active media. For the correct operation of the detector, the liquid argon must be kept in very stable thermal conditions while the contamination of electronegative impurities must be consistently kept at the level of small fractions of parts per billion. The detector is operated in Booster Neutrino Beams (BNB) at Fermilab for the search of sterile neutrinos and measurements of neutrino-argon cross sections. The cryostat and the cryogenic systems also serve as prototypes for the much larger equipment to be used for the LBNF/DUNE experiment. Since its installation in 2018-2023 and cooldown in spring of 2024, the cryostat and the cryogenic system have been commissioned to support the detector operations. The lessons learned through installation, testing, commissioning, cooldown, and initial operations are described.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Lessons from commissioning of the cryogenic system for the Short-Baseline Neutrino Detector at Fermilab

Results from commissioning and first year of operations of the cryogenic system of the Short-Baseline Neutrino Detector (SBND) and its membrane cryostat installed at the Fermi National Accelerator Laboratory are described. The SBND detector is installed in a 200 m3 membrane cryostat filled with liquid argon, which serves both as target and as active media. For the correct operation of the detector, the liquid argon must be kept in very stable thermal conditions while the contamination of electronegative impurities must be consistently kept at the level of small fractions of parts per billion. The detector is operated in Booster Neutrino Beams (BNB) at Fermilab for the search of sterile neutrinos and measurements of neutrino-argon cross sections. The cryostat and the cryogenic systems also serve as prototypes for the much larger equipment to be used for the LBNF/DUNE experiment. Since its installation in 2018-2023 and cooldown in spring of 2024, the cryostat and the cryogenic system have been commissioned to support the detector operations. The lessons learned through installation, testing, commissioning, cooldown, and initial operations are described.

Geynisman, Michael [Fermilab]↗
NASA NTRS2005

Atmospheric/Space Environment Support Lessons Learned Regarding Aerospace Vehicle Design and Operations

In modern government and aerospace industry institutions the necessity of controlling current year costs often leads to high mobility in the technical workforce, "one-deep" technical capabilities, and minimal mentoring for young engineers. Thus, formal recording, use, and teaching of lessons learned are especially important in the maintenance and improvement of current knowledge and development of new technologies, regardless of the discipline area. Within the NASA Technical Standards Program Website http://standards.nasa.gov there is a menu item entitled "Lessons Learned/Best Practices". It contains links to a large number of engineering and technical disciplines related data sets that contain a wealth of lessons learned information based on past experiences. This paper has provided a small sample of lessons learned relative to the atmospheric and space environment. There are many more whose subsequent applications have improved our knowledge of the atmosphere and space environment, and the application of this knowledge to the engineering and operations for a variety of aerospace programs.

Vaughan, William W.↗