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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Cable-fault locator

Inexpensive system automatically indicates location of short-circuited section of power cable. Monitor does not require that cable be disconnected from its power source or that test signals be applied. Instead, ground-current sensors are installed in manholes or at other selected locations along cable run. When fault occurs, sensors transmit information about fault location to control center. Repair crew can be sent to location and cable can be returned to service with minimum of downtime.

Cason, R. L.↗
NASA NTRS1978

Satellite Power Systems (SPS) concept definition study. Volume 7: SPS program plan and economic analysis

The economic and programmatic requirements for a recommended SPS solar photovoltaic baseline concept were analyzed. Costs are determined for the DDT&E; initial capital investment (covers initial procurement and emplacement of each SPS plant and equipment); replacement capital investment (capital asset replacement over the SPS operating life); operations and maintenance (expendables, minor maintenance, repair crews); and taxes/insurance.

Hanley, G.↗
NASA NTRS1980

Underground Coal Mining

Computer program models coal-mining production, equipment failure and equipment repair. Underground mine is represented as collection of work stations requiring service by production and repair crews alternately. Model projects equipment availability and productivity, and indicates proper balance of labor and equipment. Program is in FORTRAN IV for batch execution; it has been implemented on UNIVAC 1108.

Hill, G. M.↗
NASA NTRS1991

RSM 1.0 user's guide: A resupply scheduler using integer optimization

The Resupply Scheduling Model (RSM) is a PC based, fully menu-driven computer program. It uses integer programming techniques to determine an optimum schedule to replace components on or before a fixed replacement period, subject to user defined constraints such as transportation mass and volume limits or available repair crew time. Principal input for RSJ includes properties such as mass and volume and an assembly sequence. Resource constraints are entered for each period corresponding to the component properties. Though written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user defined resource constraints. Presented here is a step by step procedure for preparing the input, performing the analysis, and interpreting the results. Instructions for installing the program and information on the algorithms are given.

Viterna, Larry A.↗
NASA NTRS2005

Pre-Launch GOES-R Risk Reduction Activities for the Geostationary Lightning Mapper

The GOES-R Geostationary Lightning Mapper (GLM) is a new instrument planned for GOES-R that will greatly improve storm hazard nowcasting and increase warning lead time day and night. Daytime detection of lightning is a particularly significant technological advance given the fact that the solar illuminated cloud-top signal can exceed the intensity of the lightning signal by a factor of one hundred. Our approach is detailed across three broad themes which include: Data Processing Algorithm Readiness, Forecast Applications, and Radiance Data Mining. These themes address how the data will be processed and distributed, and the algorithms and models for developing, producing, and using the data products. These pre-launch risk reduction activities will accelerate the operational and research use of the GLM data once GOES-R begins on-orbit operations. The GLM will provide unprecedented capabilities for tracking thunderstorms and earlier warning of impending severe and hazardous weather threats. By providing direct information on lightning initiation, propagation, extent, and rate, the GLM will also capture the updraft dynamics and life cycle of convective storms, as well as internal ice precipitation processes. The GLM provides information directly from the heart of the thunderstorm as opposed to cloud-top only. Nowcasting applications enabled by the GLM data will expedite the warning and response time of emergency management systems, improve the dispatch of electric power utility repair crews, and improve airline routing around thunderstorms thereby improving safety and efficiency, saving fuel and reducing delays. The use of GLM data will assist the Bureau of Land Management (BLM) and the Forest Service in quickly detecting lightning ground strikes that have a high probability of causing fires. Finally, GLM data will help assess the role of thunderstorms and deep convection in global climate, and will improve regional air quality and global chemistry/climate modeling. The GLM has a robust design that benefits and improves upon its strong heritage of NASA-developed LEO predecessors, the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). GLM will have a substantially larger number of pixels within the focal plane, two lens systems, and multiple Real-Time Event Processors REPS for on-board event detection and data compression to provide continuous observations of the Americas and adjacent oceans.

Goodman, S. J.↗
NASA NTRS2012

Availability Estimation for Facilities in Extreme Geographical Locations

A value added analysis for the Reliability. Availability and Maintainability of McMurdo Ground Station was developed, which will be a useful tool for system managers in sparing, maintenance planning and determining vital performance metrics needed for readiness assessment of the upgrades to the McMurdo System. Output of this study can also be used as inputs and recommendations for the application of Reliability Centered Maintenance (RCM) for the system. ReliaSoft's BlockSim. a commercial Reliability Analysis software package, has been used to model the availability of the system upgrade to the National Aeronautics and Space Administration (NASA) Near Earth Network (NEN) Ground Station at McMurdo Station in the Antarctica. The logistics challenges due to the closure of access to McMurdo Station during the Antarctic winter was modeled using a weighted composite of four Weibull distributions. one of the possible choices for statistical distributions throughout the software program and usually used to account for failure rates of components supplied by different manufacturers. The inaccessibility of the antenna site on a hill outside McMurdo Station throughout one year due to severe weather was modeled with a Weibull distribution for the repair crew availability. The Weibull distribution is based on an analysis of the available weather data for the antenna site for 2007 in combination with the rules for travel restrictions due to severe weather imposed by the administrating agency, the National Science Foundation (NSF). The simulations resulted in an upper bound for the system availability and allowed for identification of components that would improve availability based on a higher on-site spare count than initially planned.

Fischer, Gerd M.↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repairs Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, it will be necessary to understand how crew repair and maintenance timelines are impacted by mission operations and technology changes. Past work has been done using historical ISS data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available data sets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat, and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA International Space Station (ISS) maintenance and crew time data are logged into two central databases, the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS) respectively. Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. MDC provides a detailed catalog of failure events and an overview of the failure’s required maintenance and OPTimIS provides a description of crew activities and crew time durations dedicated to maintenance. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair times for each failure event. Data is also classified by the outcome of each repair event, whether the failed component was replaced or whether it was repaired in place. The entire maintenance activity dataset is then categorized based on the class of failed component to allow for a statistically significant sample size for each class and to provide accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time To Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRS2020

Risk and Performance Assessment of Generic Mission Architectures: Showcasing the Artemis Mission

A has initiated a strong push to return face. In this work, we astronaut assess performance and risk for proposed mission architectures using a new Mission Architecture Risk Assessment (MARA) tool. The MARA tool can produce statistics about the availability of components and overall performance of the mission considering potential failures of any of its components. In a Monte Carlo approach, the tool repeats the mission simulation multiple times while a random generator lets modules fail according to their failure rates. The results provide statistically meaningful insights into the overall performance of the chosen architecture. A given mission architecture can be freely replicated in the tool, with the mission timeline and basic characteristics of employed mission modules (habitats, rovers, power generation units, etc.) specified in a configuration file. Crucially, failure rates for each module need to be known or estimated. The tool performs an event-driven simulation of the mission and accounts for random failure events. Failed modules can be repaired, which takes crew time but restores operations. In addition to tracking individual modules, MARA can assess the availability of predefined functions throughout the mission. For instance, the function of resource collection would require a rover to collect the resources, a power generation unit to charge the rover, and a resource processing module. Together, the modules that are required for a given function are called a functional group. Similarly, we can assess how much crew time is available to achieve a mission benefit (e.g. research, building a base, etc) as opposed to spending crew time on repairs. Here we employ the method on the proposed NASA Artemis mission. Artemis aims to return United States astronauts to the lunar surface by 2024. Results provide insights into mission failure probabilities, up- and downtime for individual modules and crew-time resources spent on the repair of failed modules. The tool also allows us to tweak the mission architecture in order to find setups that produce more favorable mission performance. As such, the tool can be an aid in improving the mission architect abling cost-benefit analysis for mission improvement.

Rumpf, Clemens M.↗
NASA NTRS1994

SpaceHab 1 maintenance experiment

The SpaceHab 1 flight on STS-57 served as a test platform for evaluation of two space station payloads. The first payload evaluated a space station maintenance concept using a sweep signal generator and a 48-channel logic analyzer to perform fault detection and isolation. Crew procedures files, test setup diagram files, and software to configure the test equipment were created on the ground and uplinked on the astronauts' voice communication circuit to perform tests in flight. In order to use these files, the portable computer was operated in a multi-window configuration. The test data transmitted to the ground allowing the ground staff to identify the cause of the fault and provide the crew with the repair procedures and diagrams. The crew successfully repaired the system under test. The second payload investigated hand soldering and de-soldering of standard components on printed circuit (PC) boards in zero gravity. It also used a new type of intra-vehicular foot restraints which uses the neutral body posture in zero-g to provide retention of the crew without their conscious attention.

Bohannon, Jackie W.↗
NASA NTRS1999

STS-96 Crew Training, Mission Animation, Crew Interviews, STARSHINE, Discovery Rollout and Repair of Hail Damage

Live footage shows the crewmembers of STS-96, Commander Kent V. Rominger, Pilot Rick D. Husband, Mission Specialists Ellen Ochoa, Tamara E. Jernigan, Daniel T. Barry, Julie Payette and Valery Ivanovich Tokarev during various training activities. Scenes include astronaut suit-up, EVA training in the Virtual Reality Lab, Orbiter space vision training, bailout training, and crew photo session. Footage also shows individual crew interviews, repair activities to the external fuel tank, and Discovery's return to the launch pad. The engineers are seen sanding, bending, and painting the foam used in repairing the tank. An animation of the deployment of the STARSHINE satellite, International Space Station, and the STS-96 Mission is presented. Footage shows the students from Edgar Allen Poe Middle School sanding, polishing, and inspecting the mirrors for the STARSHINE satellite. Live footage also includes students from St. Michael the Archangel School wearing bunny suits and entering the clean room at Goddard Space Flight Center.

Source record↗
NASA NTRS1975

The Skylab program - An overview

A brief survey is made of significant aspects of the Skylab missions, with emphasis on atmospheric control, electrical power, stabilization and attitude control, prevention of instrument contamination, habitability of the spacecraft, in-flight maintenance and repair, and crew training. Skylab, unlike previous manned spacecraft, had a two-gas atmosphere of oxygen and nitrogen. The station's 25-kW capability was the largest electrical system ever flown in space. Skylab was the first flight application of large control-moment gyroscopes for attitude control. The missions provided significant scientific data in the fields of solar physics, biomedicine, earth resources, and materials processing. Particularly important was the finding of no physical limitation to men's ability to work in space for long periods.

Disher, J. H.↗
NASA NTRS2001

STS-97 Mission Highlights Resource Tape

A continuation of 'STS-97 Mission Highlights Resource Tape, Part 1 of 3' (document ID 20010020281) and 'STS-97 Mission Highlights Resource Tape, Part 2 of 3' (document ID 20010020282), the activities of flight days seven through eleven are seen. Footage includes the crew discussing repair procedures for the Solar Array Wing (SAW) with mission managers, shots of the shuttle's payload bay, the International Space Station (ISS) with Earth in the background, the Node Micrometeoroid/Orbital Debris Shield removal, the spacewalks performed by Mission Specialists Joseph Tanner and Carlos Noriega, the undocking of Endeavour and ISS, the Orbital Maneuvering System (OMS) firing, the payload bay doors closing, and the landing sequence of Endeavour. The Aurora Borealis and a night view of the French Rivera are seen from space.

Source record↗
NASA NTRS2002

STS-113/Endeavour/ISS 11A Pre-Launch - Launch On Orbit - Landing - Crew Egress

The crew of STS-113 and the Expedition 6 crew of the International Space Station (ISS) are introduced leaving the suitup room and while being assisted in their seats onboard Space Shuttle Endeavour. The shuttle's crew consisted of Commander Jim Wetherbee, Pilot Paul Lockhart and Mission Specialists Michael Lopez-Alegria and John Herrington. The Expedition 6 crew consisted of Commander Ken Bowersox, Flight Engineer Nikolai Budarin and NASA ISS Science Officer Don Pettit. Clips of the Endeavour's night launch are shown from these cameras: Beach Tracker, VAB, Pad A, OTV-60, OTV-70, Tower 1, UCS-15, Press Site Grandstand, Cocoa Beach DOAMS, Playalinda Beach DOAMS, UCS-23, and the In-Cabin Camera. While on-orbit, highlights include the docking of Endeavour with the ISS, the Change of Command Ceremony by the Expedition 5 and 6 crews of the ISS, the mating of the P1 and S0 Trusses, and three extravehicular activities (EVAs) by astronauts Lopez-Allegria and Herrington. The shuttle crew also repairs a leak in the onboard Carbon Dioxide Removal Assembly. Clips of the shuttle's landing are shown from these cameras: VAB, Tower 1, Midfield, Runway South End, Runway North End, Tower 2, Playalinda DOAMS, UCS-3 Infrared, UCS-23, Midfield Infrared, and Pilot Point of View (PPOV).

Source record↗
NASA NTRS2014

What to Expect When Your Workplace is in Deep Space

Working life on a vehicle going to Mars would have some things in common with going to work on Earth, but most would have that twist to remind you that you're not on Earth anymore. Regardless of where we are or what we're working on humans need to eat, sleep, stay healthy and stay active and alert to perform well on the job. Studies on Earth have shown how important each element is to an individual's wellbeing and job performance. To travel in space we create a vehicle that provides the basic needs required by humans, these include carrying supplies of air, water and food. However we also need the protective shell to carry the humans, all their supplies and the systems to ensure that people can breathe, stay warm, address all bodily functions and stay healthy in space. In addition to just surviving the new environments, work tasks such as equipment maintenance and repair, normal crew operations and special science experiments will be performed. Some of the factors that will affect the crew performance include: environmental adaptation to weightlessness, dealing with cramped living quarters, physical changes caused by space travel, and how the tools, equipment, training and support information are used throughout the voyage. Different conditions can affect how the crew performs their work; we need to know more about living and working under these conditions to have successful human exploration in space.

DeMott, Diana↗
NASA NTRS2014

What to Expect When Your Workplace is in Deep Space

Working life on a vehicle going to Mars would have some things in common with going to work on Earth, but most would have that twist to remind you that you're not on Earth anymore. Regardless of where we are or what we're working on humans need to eat, sleep, stay healthy and stay active and alert to perform well on the job. Studies on Earth have shown how important each element is to an individual's wellbeing and job performance. To travel in space we create a vehicle that provides the basic needs required by humans, these include carrying supplies of air, water and food. However we also need the protective shell to carry the humans, all their supplies and the systems to ensure that people can breathe, stay warm, address all bodily functions and stay healthy in space. In addition to just surviving the new environments, work tasks such as equipment maintenance and repair, normal crew operations and special science experiments will be performed. Some of the factors that will affect the crew performance include: environmental adaptation to weightlessness, dealing with cramped living quarters, physical changes caused by space travel, and how the tools, equipment, training and support information are used throughout the voyage. Different conditions can affect how the crew performs their work; we need to know more about living and working under these conditions to have successful human exploration in space.

DeMott, Diana↗