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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Application of a neural network as a potential aid in predicting NTF pump failure

The National Transonic Facility has three centrifugal multi-stage pumps to supply liquid nitrogen to the wind tunnel. Pump reliability is critical to facility operation and test capability. A highly desirable goal is to be able to detect a pump rotating component problem as early as possible during normal operation and avoid serious damage to other pump components. If a problem is detected before serious damage occurs, the repair cost and downtime could be reduced significantly. A neural network-based tool was developed for monitoring pump performance and aiding in predicting pump failure. Once trained, neural networks can rapidly process many combinations of input values other than those used for training to approximate previously unknown output values. This neural network was applied to establish relationships among the critical frequencies and aid in predicting failures. Training pairs were developed from frequency scans from typical tunnel operations. After training, various combinations of critical pump frequencies were propagated through the neural network. The approximated output was used to create a contour plot depicting the relationships of the input frequencies to the output pump frequency.

Rogers, James L.↗
NASA NTRS2024

ISS Technology Demonstrations for Future Spaceflight Medical Systems

Throughout the history of human spaceflight, crewmembers have experienced various in-flight medical conditions including illness and injury. Planned missions to the Moon and Mars will require capabilities to maintain the health of future space travelers. Mass, power, and volume available in the vehicles and habitats for these missions will be severely constrained; resupply of resources will be limited or non-existent, as will opportunities for evacuation to Earth. Furthermore, ground-based support will be hampered by communication latencies and blackouts. These vehicle and mission constraints will necessitate a medical system that has been efficiently planned, providing on-board procedural guidance in addition to a variety of medical devices and consumable resources. Medical capabilities required for the diagnosis and treatment of potential medical conditions during future spaceflight missions may include real-time health monitoring, medical imaging, and biomarker analyses ( e.g., blood or urine). Terrestrial medicine shares these needs, thus many of these medical capabilities could likely be satisfied by Commercial-Off-The-Shelf (COTS) devices and methodologies; however, in some cases the unique space environment and increased mission duration will drive the need to modify technologies and the way care is provided. NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) Element and Mars Campaign Office’s Exploration Medical Integrated Product Team (XMIPT) are working together to decrease medical risk during exploration missions. Flight-tested medical diagnostic and treatment technologies are necessary to effectively manage medical conditions relevant to exploration missions while meeting vehicle constraints, integrating with medical decision-support tools, and enabling increasingly Earth-independent operations. Several projects have leveraged the ISS as a testbed for exploration, including 1) i n- situ blood analysis, 2) medical inventory, 3) intravenous fluid generation, and 4) autonomous medical procedure guidance. Management of several in-flight medical conditions, such as bacterial and viral infections and acute radiation syndrome, is dramatically improved with ability to assess blood cell populations, electrolytes, and metabolites. I n December 2020 and January 2021 ExMC performed an ISS technology demonstration (Tech Demo) of the HemoCue® WBC DIFF analyzer (HemoCue, Brea, CA), a COTS device that was modified to enable functionality in a spaceflight environment. This Tech Demo marked the first time that hematology measurements were successfully performed real-time in microgravity. Also modified and demonstrated was the reusable Handheld Electrolytes and Lab Technology for Humans (rHEALTH) ONE analyzer (rHEALTH, Bedford, MA), which uses flow cytometry and sheath-based hydrodynamic focusing methodologies. The rHEALTH ONE ISS Tech Demo in May 2022 demonstrated test results obtained in-flight matched those on the ground. NASA currently relies on crew self-reporting to manage and maintain medical inventory on ISS.The ability to maintain an accurate inventory becomes more critical during long duration missions since the crew will need to be increasingly autonomous in finding and utilizing medical items, including those scenarios when alternative treatments need to be considered due to limited or no resupply. HRP’s Medical Consumables Tracking (MCT) project was developed by ZIN Technologies, Inc. (Cleveland, OH), and demonstrated real-time tracking of medical supplies aboard the ISS between December 2016 and July 2018. The MCT system design utilized Radio Frequency Identification Device (RFID) technology to perform automated inventory and was installed in the Crew Health Care System (CHeCS) Resupply Stowage Rack (RSR). The challenge of limited shelf life, exacerbated by the lack of resupply opportunities, affects a plethora of medical system components including consumables, pharmaceuticals, and intravenous (IV) fluid. In 2010, ExMC funded ZIN Technologies, Inc. (Cleveland, OH), to develop the Intravenous Fluid Generation (IVGEN) system. IV fluids were successfully generated with IVGEN using the potable water supply on ISS during ISS Expedition 23. The XMIPT is in the process of developing a miniaturized version of the original IVGEN hardware for a future Tech Demo aboard the ISS. Current ISS medical operations rely heavily on preflight training and real-time remote guidance, both of which become impractical or impossible for exploration missions. The primary goal of the Autonomous Medical Officer Support (AMOS) Software Tech Demos on ISS was to confirm telemedical proof-of-concept for autonomous medical imaging in an operational setting. This novel software tool shifts emphasis from preflight training and real-time remote guidance to in-flight just-in-time instruction, a new and necessary paradigm for crew medical autonomy. AMOS introduces a novel, streamlined skill management concept for exploration missions featuring comprehensive training and guidance modules for ultrasound examinations using the ISS Ultrasound 2 (a modified GE Vivid-q™; General Electric HealthCare, Chicago, IL). With no prior crew training or remote guidance, two Tech Demos on the ISS (April 2020 and June 2022) resulted in high quality, clinically useful image sets. We will provide a review of historical, current, and planned medical devices and technologies considered for inclusion within future spaceflight medical systems and summarize hardware development activities and medical device tech demos conducted on the ISS.

Astronaut health and performance↗
NASA NTRS1987

Group-level issues in the design and training of cockpit crews

Cockpit crews always operate in an organizational context, and the transactions between the crew and representatives of that context (e.g., organizational managers, air traffic controllers) are consequential for any crew's performance. For a complete understanding of crew performance a look beyond the traditional focus on individual pilots is provided to see how team- and organization-level factors can enhance (or impede) the ability of even well-trained individuals to work together effectively. This way of thinking about cockpit crews (that is, viewing them as teams that operate in organizations) offers some potentially useful avenues for thinking about next steps in the development of CRM training programs. Those possibilities are explored, emphasizing how they can enrich (not replace) individually-focussed CRM training.

Hackman, J. Richard↗
NASA NTRS1982

Study of EVA operations associated with satellite services

Extravehicular mobility unit (EMU) factors associated with satellite servicing activities are identified and the EMU improvements necessary to enhance satellite servicing operations are outlined. Areas of EMU capabilities, equipment and structural interfaces, time lines, EMU modifications for satellite servicing, environmental hazards, and crew training are vital to manned Eva/satellite services and as such are detailed. Evaluation of EMU capabilities indicates that the EMU can be used in performing near term, basic satellite servicing tasks; however, satellite servicing is greatly enhanced by incorporating key modifications into the EMU. The servicing missions involved in contamination sensitive payload repair are illustrated. EVA procedures and equipment can be standardized, reducing both crew training time and in orbit operations time. By standardizing and coordinating procedures, mission cumulative time lines fall well within the EMU capability.

Nash, J. O.↗
DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗
NASA NTRS2011

Flying Unmanned Aircraft: A Pilot's Perspective

The National Aeronautics and Space Administration (NASA) is pioneering various Unmanned Aircraft System (UAS) technologies and procedures which may enable routine access to the National Airspace System (NAS), with an aim for Next Gen NAS. These tools will aid in the development of technologies and integrated capabilities that will enable high value missions for science, security, and defense, and open the door to low-cost, extreme-duration, stratospheric flight. A century of aviation evolution has resulted in accepted standards and best practices in the design of human-machine interfaces, the displays and controls of which serve to optimize safe and efficient flight operations and situational awareness. The current proliferation of non-standard, aircraft-specific flight crew interfaces in UAS, coupled with the inherent limitations of operating UAS without in-situ sensory input and feedback (aural, visual, and vestibular cues), has increased the risk of mishaps associated with the design of the "cockpit." The examples of current non- or sub- standard design features range from "annoying" and "inefficient", to those that are difficult to manipulate or interpret in a timely manner, as well as to those that are "burdensome" and "unsafe." A concerted effort is required to establish best practices and standards for the human-machine interfaces, for the pilot as well as the air traffic controller. In addition, roles, responsibilities, knowledge, and skill sets are subject to redefining the terms, "pilot" and "air traffic controller", with respect to operating UAS, especially in the Next-Gen NAS. The knowledge, skill sets, training, and qualification standards for UAS operations must be established, and reflect the aircraft-specific human-machine interfaces and control methods. NASA s recent experiences flying its MQ-9 Ikhana in the NAS for extended duration, has enabled both NASA and the FAA to realize the full potential for UAS, as well as understand the implications of current limitations. Ikhana is a Predator-B/Reaper UAS, built by General Atomics, Aeronautical Systems, Inc., and modified for research. Since 2007, the aircraft has been flown seasonally with a wing-mounted pod containing an infrared scanner, utilized to provide real-time wildfire geo-location data to various fire-fighting agencies in the western U.S. The multi-agency effort included an extensive process to obtain flight clearance from the FAA to operate under special provisions, given that UAS in general do not fully comply with current airspace regulations (e.g. sense-and-avoid requirements).

Pestana, Mark E.↗
DOE OSTI2021

Comparison of Tempered and Truncated Fractional Models

Tempered fractional operators are able to model effects that classical partial differential equations cannot quite capture. For example, this includes the super- and subdiffusion effects that are present in hydrology and geophysics models. However, tempered fractional operators are computationally intensive. We analyze a truncated variation of the fractional operators, which are less computationally intensive, in an effort to use them in place of the more complex tempered variation. In particular, we train parameters of the truncated operator using neural networks in order to optimize the difference of the actions of the two operators.

58 GEOSCIENCES↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

STS-106 Crew Training

Live footage of the STS-106 crewmembers shows Commander Terrence W. Wilcutt, Pilot Scott D. Altman, Mission Specialists Daniel C. Burbank, Edward T. Lu, Richard A. Mastracchio, Yuri Ivanovich Malenchenko, and Boris V. Morukov going through various training activities. These activities include SpaceHab Training at Kennedy Space Center (KSC), EVA Pre-Post Operations, Post Launch Operations, Rendezvous, Bailout, and Post Landing Egress Training at Johnson Space Center (JSC). The crew is also seen participating in a group photograph session.

Source record↗
NASA NTRS2010

Optimized Algorithms for Prediction Within Robotic Tele-Operative Interfaces

Robonaut, the humanoid robot developed at the Dexterous Robotics Labo ratory at NASA Johnson Space Center serves as a testbed for human-rob ot collaboration research and development efforts. One of the recent efforts investigates how adjustable autonomy can provide for a safe a nd more effective completion of manipulation-based tasks. A predictiv e algorithm developed in previous work was deployed as part of a soft ware interface that can be used for long-distance tele-operation. In this work, Hidden Markov Models (HMM?s) were trained on data recorded during tele-operation of basic tasks. In this paper we provide the d etails of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmi c approach. We show that all of the algorithms presented can be optim ized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. 1

Martin, Rodney A.↗
NASA NTRS2012

Innovating Training through Immersive Environments: Generation Y, Exploratory Learning, and Serious Games

Over the next decade, those entering Service and Joint Staff positions within the military will come from a different generation than the current leadership. They will come from Generation Y and have differing preferences for learning. Immersive learning environments like serious games and virtual world initiatives can complement traditional training methods to provide a better overall training program for staffs. Generation Y members desire learning methods which are relevant and interactive, regardless of whether they are delivered over the internet or in person. This paper focuses on a project undertaken to assess alternative training methods to teach special operations staffs. It provides a summary of the needs analysis used to consider alternatives and to better posture the Department of Defense for future training development.

Gendron, Gerald↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
NASA NTRS2018

To Maneuver or Not to Maneuver that Is the Question

The Earth Observing System (EOS) missions, Terra, Aqua, and Aura, are NASA's flagship Earth Science missions and are part of the International Earth Science Constellation (ESC). The ESC is composed of satellites that fly in the Afternoon Constellation (also known as the A-Train) and the Morning Constellation. All missions fly in 705-kilometer altitude, polar sun-synchronous orbits that are inclined 98.2 degrees. NASA's key operational priorities are to keep the satellites safe and maintain open and cooperative communications between all member missions. As the NASA Mission Director for the EOS Aqua and Aura satellites since their respective launches in 2002 and 2004, Mr. Guit has been responsible for the safety and operations of the satellites as they have successfully operated in the A-Train. Mr. Guit will present a recent EOS Aqua debris avoidance maneuver planning and execution challenge, how it was overcome, and how it affected the other member missions of the A-Train constellation.

Orbital debris↗
DOE OSTI2024

Virtual Environment Platform for OT/IT Training Enhancement

TRADITIONAL TECHNIQUES OT/IT Concepts Operational Technology (OT) and Information Technology (IT) concepts can often be difficult to visualize Teaching Methods Traditional teaching methods lack the intuitive & immersive aspects of hands-on activities Caveat: Unless taught by Team B! Physical Limitations Digital Twins require existing systems/hardware to mirror UPDATED TECHNIQUES Virtual Environment By making use of a virtual environment, we can represent abstract concepts in a more approachable and digestible way Increased Engagement Students are more engaged with the activities and are more likely to retain the information they are given. New/Emerging Technologies As the system is currently growing and developing, the technologies in use, as well as those represented by the system, stay up-to-date.

Deroller, Nicholas F.↗
NASA NTRS1972

Tracking and data system support for the Mariner Mars 1971 mission. Prelaunch phase through first trajectory correction maneuver, volume 1

The DSN command system provided the capability to enter commands in a computer at the deep space stations for transmission to the spacecraft. The high-rate telemetry system operated at 16,200 bits/sec. This system will permit return to DSS 14 of full-resolution television pictures from the spacecraft tape recorder, plus the other science experiment data, during the two playback periods of each Goldstone pass planned for each corresponding orbit. Other features included 4800 bits/sec modem high-speed data lines from all deep space stations to Space Flight Operations Facility (SFOF) and the Goddard Space Flight Center, as well as 50,000 bits/sec wideband data lines from DSS 14 to the SFOF, thus providing the capability for data flow of two 16,200 bits/sec high-rate telemetry data streams in real time. The TDS performed prelaunch training and testing and provided support for the Mariner Mars 1971/Mission Operations System training and testing. The facilities of the ETR, DSS 71, and stations of the MSFN provided flight support coverage at launch and during the near-earth phase. The DSSs 12, 14, 41, and 51 of the DSN provided the deep space phase support from 30 May 1971 through 4 June 1971.

Laeser, R. P.↗
NASA NTRS2005

Contingency Operations Support to NASA Johnson Space Center Medical Operations Division

The Wyle Laboratories Contingency Operations Group provides support to the NASA Johnson Space Center (JSC) Medical Operations Division in the event of a space flight vehicle accident or JSC mishap. Support includes development of Emergency Medical System (EMS) requirements, procedures, training briefings and real-time support of mishap investigations. The Contingency Operations Group is compliant with NASA documentation that provides guidance in these areas and maintains contact with the United States Department of Defense (DOD) to remain current on military plans to support NASA. The contingency group also participates in Space Operations Medical Support Training Courses (SOMSTC) and represents the NASA JSC Medical Operations Division at contingency exercises conducted worldwide by the DOD or NASA. The events of September 11, 2001 have changed how this country prepares and protects itself from possible terrorist attacks on high-profile targets. As a result, JSC is now considered a high-profile target and thus, must prepare for and develop a response to a Weapons of Mass Destruction (WMD) incident. The Wyle Laboratories Contingency Operations Group supports this plan, specifically the medical response, by providing expertise and manpower.

Stepaniak, Philip↗
DOE OSTI2020

Conceptual Design Report -- eSPS

The design of a primary electron beam facility at CERN is described. It re-enables the SPS as an electron accelerator, and leverages the development invested in CLIC technology for its injector and as accelerator R&D infrastructure. The facility would be relevant for several of the key priorities in the 2020 update of the European Strategy for Particle Physics, such as an electron-positron(e-p) Higgs factory, accelerator R&D, dark sector physics, and neutrino physics. In addition, it could serve experiments in nuclear physics. The electron beam delivered by this facility would provide access to light dark matter production significantly beyond the targets predicted by a thermal dark matter origin, and for natures of dark matter particles that are not accessible by direct detection experiments. It would also enable electro-nuclear measurements crucial for precise modelling the energy dependence of neutrino-nucleus interactions, which is needed to precisely measure neutrino oscillations as a function of energy. The implementation of the facility is the natural next step in the development of X-band high-gradient acceleration technology, a key technology for compact and cost-effective electron/positron linacs. It would also become the only facility with multi-GeV drive bunches and truly independent electron witness bunches for plasma wakefield acceleration. The facility would be used for the development and studies of a large number of components and phenomena for a future e-p Higgs and electroweak factory as the first stage of a next circular collider at CERN, and its cavities in the SPS would be the same type as foreseen for such a future collider. The operation of the SPS with electrons would train a new generation of scientists. The facility could be made operational in about five years and would operate in parallel and without interference with Run 4 at the LHC.

43 PARTICLE ACCELERATORS↗