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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

The Logistic Path from the International Space Station to the Moon and Beyond

The period from the loss of the Space Shuttle Columbia in February 2003 to resumption of Space Shuttle flights, planned for May 2005, has presented significant challenges to International Space Station (ISS) maintenance operations. Sharply curtailed upmass capability has forced NASA to revise its support strategy and to undertake maintenance activities that have significantly expanded the envelope of the ISS maintenance concept. This experience has enhanced confidence in the ability to continue to support ISS in the period following the permanent retirement of the Space Shuttle fleet in 2010. Even greater challenges face NASA with the implementation of the Vision for Space Exploration that will introduce extended missions to the Moon beginning in the period of 2015 - 2020 and ultimately see human missions to more distant destinations such as Mars. The experience and capabilities acquired through meeting the maintenance challenges of ISS will serve as the foundation for the maintenance strategy that will be employed in support of these future missions.

Watson, J. K.↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
NASA NTRS2024

Hypergol Maintenance Facility North, SWMU 090, Year 3 Air Sparge System Performance Monitoring Report, Kennedy Space Center, Florida

This Performance Monitoring Report (PMR) presents Year 3 Air Sparge (AS) System operation, maintenance, and monitoring (OM&M) activities and performance monitoring results for the AS Interim Measure (IM) at the Hypergol Maintenance Facility North (HMFN) at Kennedy Space Center (KSC), Florida. HMFN has been designated Solid Waste Management Unit (SWMU) 090 under the KSC Resource Conservation and Recovery Act Corrective Action Program. The timeframe for activities included in this report extends from September 2022 to August 2023. The HMFN AS IM was implemented in 2019-2020 to treat a chlorinated solvent groundwater plume that resulted from historical operations supporting the National Aeronautics and Space Administration (NASA) Space Program. The system includes 213 AS wells, with screen depths ranging from 25 feet to 45 feet below land surface (bls) and treats approximately 1.62 acres of contaminated groundwater. The objective of the AS IM is to actively reduce groundwater concentrations exceeding Florida Department of Environmental Protection Natural Attenuation Default Concentrations (NADCs) (identified as the High Concentration Plume [HCP]) to levels that facilitate transition into a Long-Term Monitoring program. The AS IM targets all of the HCP, except for a source zone area within the HCP where trichloroethene (TCE) concentrations exceed 11,000 micrograms per liter (μg/L) at depths greater than 45 feet bls. This is because site lithology was found to not be conducive to the AS treatment technology at these deeper depths. NASA will re-visit plans for potentially utilizing another remedial technology for residual deeper contamination following completion of the AS IM. OM&M activities and results from Year 3 indicate that the AS system at HMFN is operating as designed and is meeting performance criteria. Groundwater performance monitoring results indicate that following the third year of AS system operation, TCE concentrations in wells across all treated depth intervals have been reduced by an average of more than 99 percent. Overall, the areal extent of the plume showed a reduction following Year 3 of AS operations; however, continued operation of the AS system is required to meet the IM objective.

VOCs↗
NASA NTRS1981

Heat engine requirements for advanced solar thermal power systems

Requirements and constraints are established for power conversion subsystems, including heat engine, alternator and auxiliaries, of dish concentrator solar thermal power systems. In order to be competitive with conventional power systems, it is argued that the heat engine should be of less than 40 kW rated output, in a subsystem with an efficiency of at least 40% at rated output and at least 37% at half power. An interval between major overhauls of 50,000 hours is also desirable, along with minor maintenance and lubrication not more than four times a year requiring no more than one man-hour each time, and optimal reliability. Also found to be important are the capability for hybrid operation using heat from a solar receiver, fuel-fired combustor or both simultaneously, operation at any attitude, stability to transients in input power and output loading, operation at ambient temperatures from -30 to 50 C, and compatibility with environmental and safety requirements. Cost targets include a price of $180/kWe, and operation, maintenance and replacement costs averaging $0.001/kWh for 30 years of operation.

Jaffe, L. D.↗
DOE OSTI2023

ARM FY2024 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s radar capability aims to provide high-quality radar observations to the scientific community, advancing the understanding of clouds and precipitation processes and ultimately improving climate models. ARM operates radars across a spectrum of frequencies with diverse scanning capacities (Table 1) to meet these scientific goals. The maintenance, operations, and management of these distributed radars at sites in various climate regimes require a substantial staffing commitment. However, the existing staffing resources are constrained, making it impractical to sustain the consistent high-quality operations that are expected by the ARM user community. In response, ARM needs to develop priorities for the radar team each year. This report lays out the forthcoming priorities for Financial Year 2024 (FY24), aiming to establish a structure for the ongoing management of radar operations and data processing.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Considerations for a design and operations knowledge support system for Space Station Freedom

Engineering and operations of modern engineered systems depend critically upon detailed design and operations knowledge that is accurate and authoritative. A design and operations knowledge support system (DOKSS) is a modern computer-based information system providing knowledge about the creation, evolution, and growth of an engineered system. The purpose of a DOKSS is to provide convenient and effective access to this multifaceted information. The complexity of Space Station Freedom's (SSF's) systems, elements, interfaces, and organizations makes convenient access to design knowledge especially important, when compared to simpler systems. The life cycle length, being 30 or more years, adds a new dimension to space operations, maintenance, and evolution. Provided here is a review and discussion of design knowledge support systems to be delivered and operated as a critical part of the engineered system. A concept of a DOKSS for Space Station Freedom (SSF) is presented. This is followed by a detailed discussion of a DOKSS for the Lyndon B. Johnson Space Center and Work Package-2 portions of SSF.

Erickson, Jon D.↗
NASA NTRS1992

A vision architecture for the extravehicular activity retriever

The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools, equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This report documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios will be discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, complementary usage of the sensors and redundant capabilities.

Magee, Michael↗
NASA NTRS1993

A vision system planner for increasing the autonomy of the Extravehicular Activity Helper/Retriever

The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools and equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This paper documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios are discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, and complementary usage of the sensors and redundant capabilities.

Magee, Michael↗
NASA NTRS2023

Hypergol Maintenance Facility North SWMU 090 Year 2 Air Sparge System Performance Monitoring Report Kennedy Space Center, Florida

This Air Sparge (AS) System Performance Monitoring Report (PMR) presents Year 2 operation, maintenance, and monitoring (OM&M) activities and performance monitoring results for the AS Interim Measure (IM) at the Hypergol Maintenance Facility North (HMFN) at Kennedy Space Center (KSC), Florida. HMFN has been designated Solid Waste Management Unit (SWMU) 090 under the KSC Resource Conservation and Recovery Act Corrective Action Program. The timeframe for activities included in this report extends from August 2021 to August 2022.

Christopher Hook↗
NASA NTRS2011

Hardware Design Improvements to the Major Constituent Analyzer

The Major Constituent Analyzer (MCA) onboard the International Space Station (ISS) is designed to monitor the major constituents of the ISS's internal atmosphere. This mass spectrometer based system is an integral part of the Environmental Control and Life Support System (ECLSS) and is a primary tool for the management of ISS atmosphere composition. As a part of NASA Change Request CR10773A, several alterations to the hardware have been made to accommodate improved MCA logistics. First, the ORU 08 verification gas assembly has been modified to allow the verification gas cylinder to be installed on orbit. The verification gas is an essential MCA consumable that requires periodic replenishment. Designing the cylinder for subassembly transport reduces the size and weight of the maintained item for launch. The redesign of the ORU 08 assembly includes a redesigned housing, cylinder mounting apparatus, and pneumatic connection. The second hardware change is a redesigned wiring harness for the ORU 02 analyzer. The ORU 02 electrical connector interface was damaged in a previous on-orbit installation, and this necessitated the development of a temporary fix while a more permanent solution was developed. The new wiring harness design includes flexible cable as well as indexing fasteners and guide-pins, and provides better accessibility during the on-orbit maintenance operation. This presentation will describe the hardware improvements being implemented for MCA as well as the expected improvement to logistics and maintenance.

Combs, Scott↗
NASA NTRSDate not supplied

Development of a Human Systems Integration Plan

NASA defines Human Systems Integration (HSI) as part of the overall systems engineering and acquisition strategy for space systems. The HSI Plan defines how HSI activities will be implemented across the lifecycle of the mission, as required by NPR 7123.1C, NASA Systems Engineering Processes and Requirements, and NPR 8705.2C Human-Rating Requirements for Space Systems. The goal of this presentation is to share with government and industry how an HSI Plan can be implemented. The presentation will cover HSI implementation for flight systems, vehicle processing, and interfaces. These are divided into six NASA HSI Domains: human factors engineering, operations resources, safety, training, maintainability and supportability, habitability and environment. HSI activities go across the mission’s lifecycle from pre-formulation and acquisition through design, development, operations, maintenance, and decommissioning. The HSI Plan includes a description of the HSI activities and products that are essential for human rating, operability, maintainability, supportability, and affordability of the mission systems. It also describes the role of the HSI Team required as part of the Human Rating process. The HSI Plan utilizes the operational expertise within NASA to ensure designs and testing are successful, leading to acceptable human spaceflight vehicles.

Jackelynne Silva-Martinez↗
DOE OSTI2023

Early-Stage Radiation Safety Analysis for the Spallation Neutron Source Second Target Station Bunker Operations

The Second Target Station project at Oak Ridge National Laboratory will develop a cold neutron source to meet growing experimental needs. This paper describes calculations of the residual dose rates associated with the monolith shield plug and the beamline bunker, two key conventional operations and radiation safety features. While neutron production is active, the instrument hall outside the bunker must be generally accessible with dose rates of less than 0.25 mrem/h. When neutron production is halted, the bunker must be accessible for hands-on maintenance operations. Further, these two requirements form the cause for the assessments reported herein of residual dose rates caused by the monolith shield plug and residual dose rates in the bunker. The monolith shield plug was shown to not produce significant dose rates inside the bunker after a 20-year lifetime, and the residual dose rates inside the bunker for the case of an operating beamline were shown to reasonably allow for hands-on maintenance. These calculations are based on preliminary design models of the relevant systems. Additionally, an example showing the significance of considering neutron supermirror physics in transport calculations that track nuclide production and destruction rates to produce gamma sources for residual dose rate calculations is included. The example shows that if neutron supermirror physics is not considered, dose rate fields may be significantly underpredicted.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Equipment Self-Assessment Guide Checklist

This Equipment Self-Assessment Checklist is designed for asset owners and operators (AOOs) responsible for the deployment, operation, maintenance, or cybersecurity oversight of grid systems and digital energy technologies. It provides a structured inspection checklist for evaluating the security, integrity, and operational trustworthiness of equipment across substations, generation sites, distributed energy resources (DERs), and control environments.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
NASA NTRS1973

Lower payload costs through refurbishment and module replacement.

Payload benefits to be derived from refurbishment can be related to individual spacecraft programs directly and for planning purposes to the entire shuttle mission model. In the case of the large space telescope program, cost savings obtained through the use of the shuttle for maintenance operations have been estimated to be in the range from 30 to 40%. This saving is realized over an operational lifetime of 15 years by reducing, through refurbishment on orbit, the number of flight units along with 'optimized' reliability goals commensurate with periodic maintenance revisits at one-year intervals. Shuttle-era payload implications are discussed together with a pressurized on-orbit maintenance configuration, an earth observation satellite, and some typical teleoperator-serviced spacecraft.

Murphy, J. T.↗
NASA NTRS2005

CEDAR/TIMED: Thermospheric Vertical Wind Observations from Three Sites in the Northern Auroral Zone

The objective of this project was to operate ground based Fabry-Perot Interferometers at several points under the auroral zone to analyze and quantify the vertical wind in the thermosphere. These measurements were made in conjunction with TIMED, especially GUVI data, to relate the observed wind to the resulting mixing and compositional changes in the thermosphere. The ground based wind measurements were obtained from a scanning Doppler imager (SDI) in Poker Flat, and a vertically aligned Fabry Perot Imager (FPI) in Inuvik. A third FPI at Eagle, Alaska, was operated for a brief overlapping period as well. The SDI at Poker Flat had been in operation for several years, and was continued to run with little support from this grant. The much more expensive operation, maintenance, and data acquisition of the remote Inuvik FPI was made possible with funds from this project. During the 2003/2004 and 2004/2005 seasons, we operated the Inuvik FPI from September to April during hours of darkness. Two trips to service the instrument were required per year, and a local caretaker was funded to help keep the instrument going during the winter seasons. The data were transfered via modem and phone line to Poker Flat and were then analyzed to obtain wind and temperature at the altitude of the auroral green line OI(557.7 nm). The final data product was archived and transferred to the GEDDS system at Poker Flat were it is available on the web: http://gedds.pfrr.alaska.edu/. The data set is also available from the CEDAR data base: http://cedarweb.hao.ucar.edu/.

Lummerzheim, D.↗
DOE OSTI2020

GOOML: Geothermal Operational Optimization with Machine Learning

Geothermal Operational Optimization with Machine Learning (GOOML) is a project focused on maximizing increased availability and capacity from existing industrial-scale geothermal generation assets. The GOOML project will develop a suite of machine learning-based algorithms that analyze historical production datasets and provide predictive setpoints for geothermal field operations. Historical datasets from New Zealand and the US will provide the input to develop digital geothermal system twins which allow prediction of market conditions, maintenance operations and steamfield optimization. The algorithms will identify key parameters within fields and suggest setpoints for components of the system to maintain optimal generation. Set-points can be instructed to follow mass flow restrictions, generation maximization and optimal field/reservoir balance and give field operators a guide by which generation can be optimized. The datasets that will be used to develop GOOML are sourced from operating geothermal fields in New Zealand and the United States with varying degrees of complexity. This will ensure that most geothermal systems can utilize the GOOML tool to assist in optimizing operations. GOOML aims to achieve a step-change in geothermal operations by developing state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind automated, intelligent geothermal system model.

algorithms↗