Assessment of Occupation Radiation Exposure during Maintenance in ITER Upper Ports
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Harb, Moataz [ORNL] (ORCID:0000000200379087)↗
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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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Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting
Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.
Lin, Jiun-Ting [Lawrence Livermore National Labora↗
Hot corrosion of Co-Cr, Co-Cr-Al, and Ni-Cr alloys in the temperature range of 700-750 deg C
The effect of SO3 pressure in the gas phase on the Na2SO4 induced hot corrosion of Co-Cr, Ni-Cr, and Co-Cr-Al alloys was studied in the temperature range 700 to 750 C. The degradation of the Co-Cr and Ni-Cr alloys was found to be associated with the formation of liquid mixed sulfates (CoSO4-Na2SO4 or NiSO4-Na2SO4) which provided a selective dissolution of the Co or Ni and a subsequent sulfidation oxidation mode of attack which prevented the maintenance of a protective Cr2O3 film. A clear mechanism was not developed for the degradation of Co-Cr-Al alloys. A pitting corrosion morphology was induced by a number of different mechanisms.
Chiang, K. T.↗
Documenting the Development of Software
Some routine supervisory functions performed automatically. Program Management Facility (PMF) computer program integrated software-development and control system. Applicable to large software systems involving as many as several hundred programmers and one million lines of codes, it ensures timely and orderly planning, development, implementation, and documentation of software. Designed as support tool. Has many features providing efficient processing and utilization of space for development programmer. Incorporates security system to prevent improper maintenance. Provides full set of cross-referenced reports and supervisory functions for detailed management information. Written in assembler. IBM program TSO required.
Source record↗
Behavioral Health Program Element
The project goal is to develop behavioral health prevention and maintenance system for continued crew health, safety, and performance for exploration missions. The basic scope includes a) Operationally-relevant research related to clinical cognitive and behavioral health of crewmembers; b) Ground-based studies using analog environments (Antarctic, NEEMO, simulations, and other testbeds; c) ISS studies (ISSMP) focusing on operational issues related to behavioral health outcomes and standards; d) Technology development activities for monitoring and diagnostic tools; and e) Cross-disciplinary research (e.g., human factors and habitability research, skeletal muscle, radiation).
Leveton, Lauren B.↗
Enabling Human Space Exploration Missions Through Progressively Earth Independent Medical Operations (EIMO)
Goal: Current Space Medicine operations depend on terrestrial support to manage medical events. As astronauts travel to destinations such as the Moon, Mars, and beyond, distance will substantially limit this support and require increasing medical autonomy from the crew. This paper defines Earth Independent Medical Operations (EIMO) and identifies key elements of a conceptual EIMO system. Methods: The NASA Human Research Program Exploration Medical Capability Element held a 2-day conference at Johnson Space Center in Houston, TX with NASA experts representing all aspects of Space Medicine. Results: EIMO will be a process enabling progressively resilient deep space exploration systems and crews to reduce risk and increase mission success. Terrestrial assets will continue to provide pre-mission screening, planning, health maintenance, and prevention, while onboard medical care will increasingly be the purview of the crew. Conclusions: This paper defines and describes the key components of EIMO.
ExMC↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
J Lemery↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
Medical Operations↗
Earth Independent Medical Operations [EIMO] Definition Workshop
As the vanguard of human spaceflight increasingly transitions from one of low Earth orbit (LEO) to Lunar and subsequently Martian missions, there is a commensurate imperative for Earth-based medical authority to transition to space-based assets for the continued assurance of optimal astronaut health and performance. The transition to Earth Independent Medical Operations (EIMO) will be a process that enables progressively resilient systems and crews to reduce risk and enhance wellness and overall mission success for deep space exploration. Terrestrial assets will continue to be paramount in pre-mission screening, planning, maintenance, and prevention. Yet on-board care, response to unexpected medical events, and management of communication delays and dropouts will increasingly become the purview of the crew for primary management.
John Lemery↗
Preliminary Analysis of ISS Maintenance History and Implications for Supportability of Future Missions
The International Space Station (ISS) enables the study of supportability issues associated with long-duration human spaceflight. The ISS is a large, complex spacecraft that must be maintained by its crew. In contrast to the Space Shuttle Orbiter vehicle, but similar to spacecraft that will be component elements of future missions beyond low-Earth orbit, ISS does not return to the ground for servicing and provisioning of spares is severely constrained by transportation limits. Although significant technical support is provided by ground personnel, all hands-on maintenance tasks are performed by the crew. It is expected that future missions to distant destinations will be further limited by lack of resupply opportunities and will, eventually, become largely independent of ground support. ISS provides an opportunity to begin learning lessons that will enable future missions to be successful. Data accumulated over the first several years of ISS operations have been analyzed to gain a better understanding of maintenance-related workload. This analysis addresses both preventive and corrective maintenance and includes all U.S segment core systems. Systems and tasks that are major contributors to workload are identified. As further experience accrues, lessons will be learned that will influence future system designs so that they require less maintenance and, when maintenance is required, it can be performed more efficiently. By heeding the lessons of ISS it will be possible to identify system designs that should be more robust and point towards advances in both technology and design that will offer the greatest return on investment.
Watson, Kevin J.↗
Space Shuttle Orbital Maneuvering Subsystem (OMS) Engine Propellant Leakage Ball-Valve Shaft Seals
Evidence of propellant leakage across ball-valve shaft seals has been noted during the disassembly of five flight engines and one test engine at the NASA Lyndon B. Johnson Space Center, White Sands Test Facility. Based on data collected during the disassembly of these five engines, the consequences of propellant leakage across the ball-valve shaft seals can be divided into four primary areas of concern: Damage to the ball-valve pinion shafts, damage to sleeved bearings inside the ball-valve and actuator assemblies, degradation of the synthetic rubber o-rings used in the actuator assemblies, and corrosion and degradation to the interior of the actuator assemblies. The exact time at which leakage across the ball-valve shaft seals occurs has not been determined, however, the leakage most likely occurs during engine firings when, depending on the specification used, ball-valve cavity pressures range as high as 453 to 550 psia. This potential pressure range for the ball-valve cavities greatly exceeds the acceptance leakage test pressure of 332 psia. Since redesign and replacement of the ball-valve shaft seals is unlikely, the near term solution to prevent damage that occurs from shaft-seal leakage is to implement a routine overhaul and maintenance program for engines in the fleet. Recommended repair, verification, and possible preventative maintenance measures are discussed in the paper.
Lueders, Kathy↗
An Analysis of Barriers Preventing the Widespread Adoption of Predictive and Prescriptive Maintenance in Aviation
The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.
Christopher Teubert↗
Instruction manual model 600F, data transmission test set
Information necessary for the operation and maintenance of the Model 600F Data Transmission Test Set is presented. A description is contained of the physical and functional characteristics; pertinent installation data; instructions for operating the equipment; general and detailed principles of operation; preventive and corrective maintenance procedures; and block, logic, and component layout diagrams of the equipment and its major component assemblies.
Source record↗
Applicability of a Crack-Detection System for Use in Rotor Disk Spin Test Experiments Being Evaluated
Engine makers and aviation safety government institutions continue to have a strong interest in monitoring the health of rotating components in aircraft engines to improve safety and to lower maintenance costs. To prevent catastrophic failure (burst) of the engine, they use nondestructive evaluation (NDE) and major overhauls for periodic inspections to discover any cracks that might have formed. The lowest cost fluorescent penetrant inspection NDE technique can fail to disclose cracks that are tightly closed during rest or that are below the surface. The NDE eddy current system is more effective at detecting both crack types, but it requires careful setup and operation and only a small portion of the disk can be practically inspected. So that sensor systems can sustain normal function in a severe environment, health-monitoring systems require the sensor system to transmit a signal if a crack detected in the component is above a predetermined length (but below the length that would lead to failure) and lastly to act neutrally upon the overall performance of the engine system and not interfere with engine maintenance operations. Therefore, more reliable diagnostic tools and high-level techniques for detecting damage and monitoring the health of rotating components are very essential in maintaining engine safety and reliability and in assessing life.
Abdul-Aziz, Ali↗
Fire prevention on airplanes. Part I
Various methods for preventing fires in airplanes are presented with most efforts centering around prevention of backfires, new engine and carburetor designs, as well as investigations on different types of fuels.
EXHAUST GASES - FIRE PREVENTION↗
NASA Exploration Systems Maintainability Standards for Artemis and Beyond
This assessment was requested by the NASA Engineering and Safety Center (NESC), which, based on findings from the NESC study “Safe Human Expeditions Beyond Low Earth Orbit (LEO)” (Valinia et al., 2022), determined its topic to be an underrecognized critical and urgent Agency need, due to impending Artemis vehicle procurements. The principal objective of this assessment was to review and update current Agency-level maintainability requirements for space systems to support crew on expeditions beyond LEO in both preventive and corrective maintenance. This report contains the results of the NESC assessment.
Orbital Replacement Units↗
The NASA Aviation Safety Program: Overview
In 1997, the United States set a national goal to reduce the fatal accident rate for aviation by 80% within ten years based on the recommendations by the Presidential Commission on Aviation Safety and Security. Achieving this goal will require the combined efforts of government, industry, and academia in the areas of technology research and development, implementation, and operations. To respond to the national goal, the National Aeronautics and Space Administration (NASA) has developed a program that will focus resources over a five year period on performing research and developing technologies that will enable improvements in many areas of aviation safety. The NASA Aviation Safety Program (AvSP) is organized into six research areas: Aviation System Modeling and Monitoring, System Wide Accident Prevention, Single Aircraft Accident Prevention, Weather Accident Prevention, Accident Mitigation, and Synthetic Vision. Specific project areas include Turbulence Detection and Mitigation, Aviation Weather Information, Weather Information Communications, Propulsion Systems Health Management, Control Upset Management, Human Error Modeling, Maintenance Human Factors, Fire Prevention, and Synthetic Vision Systems for Commercial, Business, and General Aviation aircraft. Research will be performed at all four NASA aeronautics centers and will be closely coordinated with Federal Aviation Administration (FAA) and other government agencies, industry, academia, as well as the aviation user community. This paper provides an overview of the NASA Aviation Safety Program goals, structure, and integration with the rest of the aviation community.
Shin, Jaiwon↗
Mitigation of Biofouling in Plant Watering Systems Using AgXX, a Novel Surface Treatment
The development of plant growth systems with high yields and low maintenance for food production is a key focus area for NASA. One of the remaining technical challenges is keeping the plant watering systems clean without affecting plant growth, requiring consumables, or demanding crew time. Plant watering systems, such as the one onboard the International Space Station (ISS), provide a nutrient rich environment for biofilm formation. Frequent maintenance is necessary to prevent biofouling, which currently requires crew time and a mechanical means of cleaning. Better solutions are needed. The current ISS practices for biofilm mitigation in the water recovery and distribution system include the use of biocides (silver ion or iodine) along with regular maintenance (e.g. flushing, filter replacement). These biocide-based strategies could be problematic for plant watering systems, such as in Ohalo, the Exploration Garden, and the APH, due to incompatibilities of the biocides with plants. We propose the application of AgXX, a novel antifouling surface treatment that meets the above requirements. This paper will report on an initial study that was completed to determine whether AgXX would be effective in a plant nutrient solution, and whether it would negatively impact plant growth in a hydroponics-type system.
Tesia D. Irwin↗