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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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NASA NTRSDate not supplied

International Space Station Space Environments Performance and Anomaly Resolution

The International Space Station is the largest and most complex on-orbit platform for space science utilization in low Earth orbit. The Space Environments Team addresses natural and induced environments for the ISS Program including external contamination, ionizing radiation, neutral atmosphere and solar ultraviolet radiation, plasma effects, and acoustics. For the ISS to fulfill its mission as a long-duration science platform, space environments effects are assessed, monitored, and controlled through design or operational mitigation. Interactions of ISS hardware with the natural and induced space environments, and the assessment and mitigation of those effects play a critical role in ISS mission operations. The Space Environments Team has complete system integration responsibility in these area for U.S./International Partner/Russian hardware, visiting vehicles, ISS payloads and operation. Lessons learned and processes developed for ISS are applicable to the design, assembly, and operations of long-duration space systems.

space environments↗
NASA NTRS1996

Fuzzy Q-Learning for Generalization of Reinforcement Learning

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

Berenji, Hamid R.↗
NASA NTRS2012

Lessons Learned for Improving Spacecraft Ground Operations

NASA has a unique history in processing the Space Shuttle fleet for launches. Some of this experience has been captured in the NASA Lessons Learned Information System (LLIS). This tool provides a convenient way for design engineers to review lessons from the past to prevent problems from reoccurring and incorporate positive lessons in new designs. At the Kennedy Space Center, the LLIS is being used to design ground support equipment for the next generation of launch and crewed vehicles. This paper describes the LLIS process and offers some examples.

Bell, Michael A.↗
NASA NTRS2005

Engineering Lessons Learned and Systems Engineering Applications

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned and technical standards. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. Systems Engineering has been defined (EINIS-632) as "an interdisciplinary approach encompassing the entire technical effort to evolve and verify an integrated and life-cycle balanced set of system people, product, and process solutions that satisfy customer needs". Designing reliable space-based systems has always been a goal for NASA, and many painful lessons have been learned along the way. One of the continuing functions of a system engineer is to compile development and operations "lessons learned" documents and ensure their integration into future systems development activities. They can produce insights and information for risk identification identification and characterization. on a new project. Lessons learned files from previous projects are especially valuable in risk

Gill, Paul S.↗
NASA NTRS2003

Drinking from the Fire Hose: Why the Flight Management System Can Be Hard to Train and Difficult to Use

The Flight Management Computer (FMC) and its interface, the Multi-function Control and Display Unit (MCDU) have been identified by researchers and airlines as difficult to train and use. Specifically, airline pilots have described the "drinking from the fire-hose" effect during training. Previous research has identified memorized action sequences as a major factor in a user s ability to learn and operate complex devices. This paper discusses the use of a method to examine the quantity of memorized action sequences required to perform a sample of 102 tasks, using features of the Boeing 777 Flight Management Computer Interface. The analysis identified a large number of memorized action sequences that must be learned during training and then recalled during line operations. Seventy-five percent of the tasks examined require recall of at least one memorized action sequence. Forty-five percent of the tasks require recall of a memorized action sequence and occur infrequently. The large number of memorized action sequences may provide an explanation for the difficulties in training and usage of the automation. Based on these findings, implications for training and the design of new user-interfaces are discussed.

Sherry, Lance↗
NASA NTRS2011

Lessons Learned from the Design, Certification, and Operation of the Space Shuttle Integrated Main Propulsion System (IMPS)

The Space Shuttle Integrated Main Propulsion System (IMPS) consists of the External Tank (ET), Orbiter Main Propulsion System (MPS), and Space Shuttle Main Engines (SSMEs). The IMPS is tasked with the storage, conditioning, distribution, and combustion of cryogenic liquid hydrogen (LH2) and liquid oxygen (LO2) propellants to provide first and second stage thrust for achieving orbital velocity. The design, certification, and operation of the associated IMPS hardware have produced many lessons learned over the course of the Space Shuttle Program (SSP). A subset of these items will be discussed in this paper for consideration when designing, building, and operating future spacecraft propulsion systems. This paper will focus on lessons learned related to Orbiter MPS and is the first of a planned series to address the subject matter.

Martinez, Hugo E.↗
NASA NTRS1993

Application of hardness testing in foundry processing operations: A university and industry partnership

The objectives are the following: (1) to plan a tour of local manufacturing facility to observe hardness testing methods used and how they support the manufacturing process today; (2) to learn the operation of Brinell Hardness Testing and equipment set up with samples of accessory equipment for testing of various materials; (3) to use a Brinell scope (hand magnifier) to measure the diameter of the test indentation (in millimeters); (4) to use the hardness equation to determine the spherical area of the indentation and the calculation of the Brinell Hardness Number; (5) to use standard charts for ASTM standard size indenters to find the hardness number; and (6) to learn a method of conversion of the Brinell number to other hardness scales, their common usage, and also to convert the hardness number to the approximate tensile strength.

Martin, Donald H.↗
NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗
NASA NTRS1991

Approximate reasoning-based learning and control for proximity operations and docking in space

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

Berenji, Hamid R.↗
NASA NTRS2008

Refining the Ares V Design to Carry Out NASA's Exploration Initiative

NASA's Ares V cargo launch vehicle is part of an overall architecture for u.S. space exploration that will span decades. The Ares V, together with the Ares I crew launch vehicle, Orion crew exploration vehicle and Altair lunar lander, will carry out the national policy goals of retiring the Space Shuttle, completing the International Space Station program, and expanding exploration of the Moon as a steps toward eventual human exploration of Mars. The Ares fleet (Figure 1) is the product of the Exploration Systems Architecture study which, in the wake of the Columbia accident, recommended separating crew from cargo transportation. Both vehicles are undergoing rigorous systems design to maximize safety, reliability, and operability. They take advantage of the best technical and operational lessons learned from the Apollo, Space Shuttle and more recent programs. NASA also seeks to maximize commonality between the crew and cargo vehicles in an effort to simplify and reduce operational costs for sustainable, long-term exploration.

Creech, Steve↗
NASA NTRS2016

Expanding NASA and Roscosmos Scientific Collaboration on the International Space Station

The International Space Station (ISS) is a world-class laboratory orbiting in space. NASA and Roscosmos have developed a strong relationship through the ISS Program Partnership, working together and with the other ISS Partners for more than twenty years. Since 2013, based on a framework agreement between the Program Managers, NASA and Roscosmos are building a joint program of collaborative research on ISS. This international collaboration is developed and implemented in phases. Initially, members of the ISS Program Science Forum from NASA and TsNIIMash (representing Roscosmos) identified the first set of NASA experiments that could be implemented in the "near term". The experiments represented the research categories of Technology Demonstration, Microbiology, and Education. Through these experiments, the teams from the "program" and "operations" communities learned to work together to identify collaboration opportunities, establish agreements, and jointly plan and execute the experiments. The first joint scientific activity on ISS occurred in January 2014, and implementation of these joint experiments continues through present ISS operations. NASA and TsNIIMash have proceeded to develop "medium term" collaborations, where scientists join together to improve already-proposed experiments. A major success is the joint One-Year Mission on ISS, with astronaut Scott Kelly and cosmonaut Mikhail Kornienko, who returned from ISS in March, 2016. The teams from the NASA Human Research Program and the RAS Institute for Biomedical Problems built on their considerable experience to design joint experiments, learn to work with each other's protocols and processes, and share medical and research data. New collaborations are being developed between American and Russian scientists in complex fluids, robotics, rodent research and space biology, and additional human research. Collaborations are also being developed in Earth Remote Sensing, where scientists will share data from imaging systems mounted on ISS as well as other orbiting spacecraft to improve our understanding of the Earth and its climate. NASA and Roscosmos continue to encourage international scientific cooperation and expanded use of the ISS Laboratory. "Long-term", larger collaborations will achieve scientific objectives that no single national science team or agency can achieve on its own. The joint accomplishments achieved so far have paved the way for a stronger international scientific community and improved results and benefits from ISS.

Hasbrook, Pete↗
NASA NTRS2007

Automated Ground Umbilical Systems (AGUS) Project

All space vehicles require ground umbilical systems for servicing. Servicing requirements can include, but are not limited to, electrical power and control, propellant loading and venting, pneumatic system supply, hazard gas detection and purging as well as systems checkout capabilities. Of the various types of umbilicals, all require several common subsystems. These typically include an alignment system, mating and locking system, fluid connectors, electrical connectors and control !checkout systems. These systems have been designed to various levels of detail based on the needs for manual and/or automation requirements. The Automated Ground Umbilical Systems (AGUS) project is a multi-phase initiative to develop design performance requirements and concepts for launch system umbilicals. The automation aspect minimizes operational time and labor in ground umbilical processing while maintaining reliability. This current phase of the project reviews the design, development, testing and operations of ground umbilicals built for the Saturn, Shuttle, X-33 and Atlas V programs. Based on the design and operations lessons learned from these systems, umbilicals can be optimized for specific applications. The product of this study is a document containing details of existing systems and requirements for future automated umbilical systems with emphasis on design-for-operations (DFO).

Gosselin, Armand M.↗
NASA NTRS2017

EVA Systems Technology Gaps and Priorities 2017

Performance of Extra-Vehicular Activities (EVA) has been and will continue to be a critical capability for human space flight. Human exploration missions beyond LEO will require EVA capability for either contingency or nominal activities to support mission objectives and reduce mission risk. EVA systems encompass a wide array of products across pressure suits, life support systems, EVA tools and unique spacecraft interface hardware (i.e. EVA Translation Paths and EVA Worksites). In a fiscally limited environment with evolving transportation and habitation options, it is paramount that the EVA community's strategic planning and architecture integration products be reviewed and vetted for traceability between the mission needs far into the future to the known technology and knowledge gaps to the current investments across EVA systems. To ascertain EVA technology and knowledge gaps many things need to be brought together, assessed and analyzed. This includes an understanding of the destination environments, various mission concept of operations, current state of the art of EVA systems, EVA operational lessons learned, and reference advanced capabilities. A combined assessment of these inputs should result in well-defined list of gaps. This list can then be prioritized depending on the mission need dates and time scale of the technology or knowledge gap closure plan. This paper will summarize the current state of EVA related technology and knowledge gaps derived from NASA's Exploration EVA Reference Architecture and Operations Concept products. By linking these products and articulating NASA's approach to strategic development for EVA across all credible destinations an EVA could be done in, the identification of these gaps is then used to illustrate the tactical and strategic planning for the EVA technology development portfolio. Finally, this paper illustrates the various "touch points" with other human exploration risk identification areas including human health and performance.

Johnson, Brian J.↗
NASA NTRS2013

Forecasting Propagation and Evolution of CMEs in an Operational Setting: What Has Been Learned

One of the major types of solar eruption, coronal mass ejections (CMEs) not only impact space weather, but also can have significant societal consequences. CMEs cause intense geomagnetic storms and drive fast mode shocks that accelerate charged particles, potentially resulting in enhanced radiation levels both in ions and electrons. Human and technological assets in space can be endangered as a result. CMEs are also the major contributor to generating large amplitude Geomagnetically Induced Currents (GICs), which are a source of concern for power grid safety. Due to their space weather significance, forecasting the evolution and impacts of CMEs has become a much desired capability for space weather operations worldwide. Based on our operational experience at Space Weather Research Center at NASA Goddard Space Flight Center (http://swrc.gsfc.nasa.gov), we present here some of the insights gained about accurately predicting CME impacts, particularly in relation to space weather operations. These include: 1. The need to maximize information to get an accurate handle of three-dimensional (3-D) CME kinetic parameters and therefore improve CME forecast; 2. The potential use of CME simulation results for qualitative prediction of regions of space where solar energetic particles (SEPs) may be found; 3. The need to include all CMEs occurring within a ~24 h period for a better representation of the CME interactions; 4. Various other important parameters in forecasting CME evolution in interplanetary space, with special emphasis on the CME propagation direction. It is noted that a future direction for our CME forecasting is to employ the ensemble modeling approach.

forecasting↗
NASA NTRSDate not supplied

Insights and Observations From Operating A Laser Communication Terminal on the International Space Station (ISS)

The Integrated Laser Communication Relay Demonstration (LCRD) Low Earth Orbit User Modem and Amplifier Terminal (ILLUMA-T) is the first user platform for NASA’s first laser communication relay, LCRD. ILLUMA-T is hosted on the International Space Station (ISS) as an external payload of the Japanese Experiment Module – Exposed Facility (JEM-EF) situated in slot 3. The LCRD payload is hosted on the STPSat-6 spacecraft, which orbits Earth in a geostationary orbit at position 112 W. The ILLUMA-T-to-LCRD system provides a bidirectional, space-to-ground laser communication relay link transferring data up to 1.2 Gbps utilizing two optical ground stations (OGS), OGS-1 in California and OGS-2 in Hawai’i. This paper describes lessons learned from operating the ILLUMA-T system and examines topics such as the end-to-end system availability, predictive versus actual ephemeris, acquisition adjustments, modem timing and handshaking, the terminal’s physical placement and accommodations, and experiment development, execution, and analysis. The authors of this paper operate both the LCRD and ILLUMA-T laser terminals on a daily basis including pass planning, procedure execution, acquisition analysis, and real-time terminal commanding and telemetry monitoring. LCRD is a joint project involving NASA Goddard Space Flight Center (GSFC), the California Institute of Technology Jet Propulsion Laboratory (JPL), and Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). The ILLUMA-T payload is managed by NASA GSFC in Greenbelt, Maryland whose partners include the ISS program office at NASA’s Johnson Space Center (JSC) in Houston, Texas; the Huntsville Operations Support Center (HOSC) at Marshall Space Flight Center (MSFC) in Huntsville, Alabama; the Glenn Research Center (GRC) in Cleveland, Ohio; and the MIT LL in Lexington, Massachusetts. ILLUMA-T is funded by the Space Communications and Navigation (SCaN) Program at NASA Headquarters in Washington, D.C.

Laser Communications↗
NASA NTRS1990

The Johnson Space Center Management Information Systems (JSCMIS): An interface for organizational databases

The Management Information and Decision Support Environment (MIDSE) is a research activity to build and test a prototype of a generic human interface on the Johnson Space Center (JSC) Information Network (CIN). The existing interfaces were developed specifically to support operations rather than the type of data which management could use. The diversity of the many interfaces and their relative difficulty discouraged occasional users from attempting to use them for their purposes. The MIDSE activity approached this problem by designing and building an interface to one JSC data base - the personnel statistics tables of the NASA Personnel and Payroll System (NPPS). The interface was designed against the following requirements: generic (use with any relational NOMAD data base); easy to learn (intuitive operations for new users); easy to use (efficient operations for experienced users); self-documenting (help facility which informs users about the data base structure as well as the operation of the interface); and low maintenance (easy configuration to new applications). A prototype interface entitled the JSC Management Information Systems (JSCMIS) was produced. It resides on CIN/PROFS and is available to JSC management who request it. The interface has passed management review and is ready for early use. Three kinds of data are now available: personnel statistics, personnel register, and plan/actual cost.

Bishop, Peter C.↗
NASA NTRS2011

Lunar Reconnaissance Orbiter (LRO) Thermal On-Orbit Performance

The Lunar Reconnaissance Orbiter (LRO) was launched on June 18, 2009. The nominal mission ended on September 15, 2010 and LRO is now on a four-year extended mission. The LRO performances in hot and cold cases are compared to pre-launch analysis predicts, and operational lessons learned are discussed. One instrument has required tighter-than-anticipated thermal control, and two others have frequently requested unanticipated calibration maneuvers that had to be evaluated for their thermal performance. A series of off nadir thermal analyses of the entire orbiter were performed prior to launch, and these predictions are compared to actual maneuvers, with a discussion of the process by which maneuvers can be rapidly evaluated for thermal concerns. On December 21st, 2010, LRO experienced its first severe Lunar Eclipse. Operationally, this required the Spacecraft to pre-heat its main avionics panel in order to minimize control heater power during the period when the Earth blocks the sun from the moon. The operational design and in-flight performance are summarized.

Baker, Charles↗