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International Space Station: explore 11 source-linked works published from 2009 to 2025, with original documents and citations.

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Sources: nasa. Collection updated 2026-09-15. Counts describe this index, not the complete source archives.

NASA NTRS2025

Erosion Results of the MISSE 9-15 Polymers and Composites Experiment 1-4 (PCE 1-4)

Polymers and other oxidizable materials on the exterior of spacecraft in the low Earth orbit (LEO) space environment can be eroded from reaction with atomic oxygen (AO). Therefore, in order to design durable spacecraft it is important to know the extent of erosion that will occur during a mission. This can be determined by knowing the LEO AO erosion yield, E y (volume loss per incident oxygen atom), of materials susceptible to AO reaction. In addition, recent flight experiments have shown that the AO E y can vary with the AO fluence and/or solar exposure. Therefore, obtaining AO E y data for materials flown on various spaceflight missions is important. NASA Glenn Research Center has flown numerous experiments as part of the Materials International Space Station Experiment (MISSE) missions on the exterior of the International Space Station (ISS) to characterize the LEO E y of polymers, composites, protective coatings, and other spacecraft materials. Recently, four Glenn experiments with 365 flight (F) samples were flown on ISS’s MISSE-Flight Facility (MISSE-FF). These experiments are the Polymers and Composites Experiment-1 (PCE-1) flown as part of the MISSE-9 mission, the PCE-2 flown as part of the MISSE-10 mission, the PCE-3 flown as part of the MISSE-12 and MISSE-15 missions, and the PCE-4 flown as part of the MISSE-13 mission. Although each experiment had numerous sample objectives, the primary objective was to determine the LEO AO E y of various spacecraft materials as a function of solar irradiation and AO fluence. This paper provides a summary of the erosion data for the PCE 1-4 AO E y samples. The AO E y for 150 samples flown in either the LEO ram, wake, zenith or nadir directions are provided. The AO ram fluence varied from 2.97×10 20 atoms/cm 2 after 0.89 years of direct space exposure (with relatively high levels of Si contamination) on MISSE 12 to 3.93×10 20 atoms/cm 2 after 1.17 years of direct space exposure on MISSE-10. The ram AO E y values for uncoated polymers range from 3.81×10 –25 cm 3 /atom for polytetrafluoroethylene (M9R-C20 F) exposed to an AO fluence of 3.44×10 20 atoms/cm 2 on MISSE-9 to 4.43×10 –23 cm 3 /atom for AO etched low density polyimide aerogel (M12R-C21 F) exposed to an AO fluence of 2.97×10 21 atoms/cm 2 on MISSE-12. Because of the low AO fluence and relatively high Si contamination, a number of PCE-3 wake samples experienced mass gain. Thus, AO E y values are not provided for these samples. Although there are calculated AO E y values for the zenith, wake and nadir samples, the ram AO E y for a particular material is a more reliable value in terms of AO exposure because the zenith, wake and nadir directions were exposed to either no or very little AO fluence and thus other space environmental factors (i.e. vacuum, thermal extremes and thermal cycling, and/or various types of radiation) are responsible for the mass loss.

Erosion yield↗
NASA NTRS2019

International Space Station Lithium-Ion Battery Status

When originally launched, the International Space Station (ISS) primary Electric Power System (EPS) used Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The electricity for the space station is generated by its solar arrays, which charge batteries during insolation for subsequent discharge during eclipse. The Ni-H2 batteries were designed to operate for ten years at a 35% depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. For service beyond that period, upgraded Li-Ion Orbital Replacement Units (ORUs) were designed. These are the largest Li-Ion batteries ever utilized for a human rated spacecraft. The first set of six Ni-H2 batteries was replaced by Li-Ion batteries in December 2016; the second set of six was launched in September 2018 and installed in March 2019. The third set of six were launched in September 2019. Three batteries were installed in September 2019, with the remaining three to be installed in January 2020. This paper will include a brief overview of the ISS Li-Ion battery system architecture, start up of the second and third set of 6 batteries and the on-orbit status of all 18 batteries, plus the status of the Li-Ion cell life testing.

Lithium-Ion↗
NASA NTRS2018

International Space Station Lithium-Ion Battery Status

When originally launched, the International Space Station (ISS) primary Electric Power System (EPS) used Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The electricity for the space station is generated by its solar arrays, which charge batteries during insolation for subsequent discharge during eclipse. The Ni-H2 batteries were designed to operate for ten years at a 35 percent depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. For service beyond that period, upgraded Li-Ion Orbital Replacement Units (ORUs) were designed. These are the largest Li-Ion batteries ever utilized for a human rated spacecraft. With the oldest 12 Ni-H2 battery ORUs cycling since September 2006, the first set of six was replaced by Li-Ion batteries in December 2016; the second set of six is planned for replacement in September 2018. This paper will include an overview of the ISS Li-Ion battery system architecture, on-orbit status of the first 6 batteries, start up of the second 6 batteries, and the status of the Li-Ion cell and ORU life cycle testing.

International Space Station↗
NASA NTRS2017

International Space Station Lithium-Ion Battery Start-Up and Cycling

The International Space Station (ISS) primary Electric Power System (EPS) was originally designed to use Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The electricity for the ISS is generated by its solar arrays, which charge batteries during insolation for subsequent discharge during eclipse. The Ni-H2 batteries were designed to operate at a 35 depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. In 2010, the ISS Program began the development of Lithium-Ion (Li-Ion) batteries to replace Ni-H2 batteries approaching the end of their useful life and concurrently funded a Li-Ion ORU (Orbital Replacement Unit) and cell life testing project. The first set of 6 Li-ion battery replacements was launched in December 2016 and deployed in January 2017. This paper will discuss the Li-ion battery on-orbit cycling and the status of the Li-Ion cell and ORU life cycle testing.

International Space Station↗
NASA NTRS2017

International Space Station Lithium-Ion Battery Start-Up

International Space Station Lithium-Ion Battery Start-Up.The International Space Station (ISS) primary Electric Power System (EPS) was originally designed to use Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The electricity for the space station is generated by its solar arrays, which charge batteries during insolation for subsequent discharge during eclipse. The Ni-H2 batteries are designed to operate at a 35 depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. As the oldest of the 48 Ni-H2 battery Orbital Replacement Units (ORUs) has been cycling since September 2006, these batteries are now approaching their end of useful life. In 2010, the ISS Program began the development of Lithium-Ion (Li-ion) batteries to replace the Ni-H2 batteries and concurrently funded a Li-Ion ORU and cell life testing project. The first set of 6 Li-ion battery replacements were launched in December 2016 and deployed in January 2017. This paper will discuss the Li-ion battery on-orbit start-up and the status of the Li-Ion cell and ORU life cycle testing.

Lithium-Ion↗
NASA NTRS2016

International Space Station Lithium-Ion Battery

The International Space Station (ISS) Electric Power System (EPS) currently uses Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The batteries are charged during insolation and discharged during eclipse. The Ni-H2 batteries are designed to operate at a 35 depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. Since the oldest of the 48 Ni-H2 battery Orbital Replacement Units (ORUs) has been cycling since September 2006, these batteries are now approaching their end of useful life. In 2010, the ISS Program began the development of Lithium-Ion (Li-ion) batteries to replace the Ni-H2 batteries and concurrently funded a Li-ion cell life testing project. This paper will include an overview of the ISS Li-Ion battery system architecture and the progress of the Li-ion battery design and development.

Battery↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Machine Learning↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. In a previous publication, we discussed how we selected the three tools based primarily on their ability to be certified for human spaceflight and described our plans for the prototype. This abstract is due October 23, 2009, and the Ares I-X launch is currently scheduled for October 27, 2009. If this abstract is accepted, then the full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes. Also, the paper will describe the three tools and how they are used in the prototype. It will include a description of the TEAMS models of the Ares I-X first-stage TVC and associated ground hydraulics and how we adapted the TVC model for use in real-time diagnostics. It will describe the SHINE rules used for fault detection and mode identification and the software architecture that interfaces the various pieces of existing software that are part of the prototype to one another. It will describe how we selected the sensor values and commands that were used to train the IMS model and how we optimized the number of clusters in the IMS model. It will include screen shots of the graphical display that we developed in Java to display the outputs of the three tools. Because Ares I-X data was not yet available to us while we were developing the prototype, we used historical data from the Space Shuttle's Solid Rocket Booster (SRB) TVCs and the associated ground hydraulics to train IMS and to test the entire prototype. Because most of the failure modes that we modeled have never occurred in the Shuttle we inserted simulated failures into the Shuttle data. The Ares I-X first-stage TVC is very similar to the SRB TVC and we expect the data will be very similar. After the launch, we will determine how similar the data actually is and report how any differences in the data affected the diagnostic accuracy of the prototype. Finally, although we did not get the prototype certified, we designed it in a way that it could be certified and wrote a preliminary certification plan. The paper will include a brief summary of how we considered the need for certification in the design of the prototype, how we tested the prototype before deploying it to Hangar AE, and how we would propose to get it certified if it were deployed as an operational system. The paper will conclude with a description of some of the challenges we faced and some of the lessons learned in developing and deploying the prototype.

International Space Station↗
NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.

Satellites↗
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
NASA NTRSDate not supplied

CLDP/ISS Glovebox Technical Interchange Meeting

This presentation is designed to provide a high-level overview of the Microgravity Science Glovebox (MSG) and the Life Sciences Glovebox (LSG) facilities onboard the International Space Station. In addition, it provides metrics and lessons-learned information intended for the Commercial Low-Earth Orbit Development Program (CLDP) Partners.

glovebox↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Erosion Results of the MISSE 9-15 Polymers and Composites Experiment 1-4 (PCE 1-4)2025-05-0120260005331nasa
International Space Station Lithium-Ion Battery Status2019-11-1920200000004nasa
International Space Station Lithium-Ion Battery Status2018-11-0120190002835nasa
International Space Station Lithium-Ion Battery Start-Up and Cycling2017-11-1420180003481nasa
International Space Station Lithium-Ion Battery Start-Up2017-04-2520170004524nasa
International Space Station Lithium-Ion Battery2016-04-1820160012048nasa
Ares I-X Ground Diagnostic Prototype2010-04-2020100027332nasa
Ares I-X Ground Diagnostic Prototype2010-04-2020130012584nasa
General Purpose Data-Driven Monitoring for Space Operations2009-04-0620090037065nasa
General Purpose Data-Driven System Monitoring for Space Operations2009-04-0620130004312nasa
CLDP/ISS Glovebox Technical Interchange MeetingNot supplied20260002199nasa

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