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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Toward an embedded training tool for Deep Space Network operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor. The cognitive model was developed in Soar for the DSN's Link Monitor and Control (LMC) system; it led to several insights about learning in the task domain that were used to design an intelligent tutor called REACT that implements a method called 'impasse-driven tutoring'. REACT is one component of the LMC training system, which also includes a communications link simulator and a graphical user interface. A pilot study of the LMC training system indicates that REACT shows promise as an effective way for helping operators to quickly acquire expert skills.

Hill, Randall W., Jr.↗
NASA NTRS1993

Toward an Embedded Training Tool for Deep Space Network Operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor.

Johnson, W. Lewis↗
NASA NTRS1971

Simulation of Mariner Mars 1971 spacecraft

Preparation for the Mariner Mars 1971 mission is reported, including an extensive training program for operations personnel during which the primary source of spacecraft data was a computer program simulating the spacecraft. The objectives of a simulation model for training purposes differed from objectives appropriate to a design or analysis model. Model subsystems were designed to provide realistic telemetry data reflecting changes due both to commands and environmental parameters affecting the spacecraft at various times during the mission. The spacecraft is modeled along two separate functional lines. Boolean operations are concentrated in the spacecraft logic model, which determines the spacecraft state or mode, while mathematical operations or algorithms are executed in computational subsystem models. Although logic parameters are interrogated as a part of each computational pass, actual logic model processing occurs only when a change-of-state input is generated by the operations organization. The program design, some of the special characteristics of each of the modeled subsystems, and how the model was used in support of mission operations training are presented.

Ausman, N. E., Jr.↗
NASA NTRS1994

Stereoscopic, Force-Feedback Trainer For Telerobot Operators

Computer-controlled simulator for training technicians to operate remote robots provides both visual and kinesthetic virtual reality. Used during initial stage of training; saves time and expense, increases operational safety, and prevents damage to robots by inexperienced operators. Computes virtual contact forces and torques of compliant robot in real time, providing operator with feel of forces experienced by manipulator as well as view in any of three modes: single view, two split views, or stereoscopic view. From keyboard, user specifies force-reflection gain and stiffness of manipulator hand for three translational and three rotational axes. System offers two simulated telerobotic tasks: insertion of peg in hole in three dimensions, and removal and insertion of drawer.

Kim, Won S.↗
NASA NTRS1993

Vector Quantization With Emergent Codebook Structure

Proposed scheme under development for transmission of vector-quantized digital video images, vector quantizer codebook updated to adapt quantizer to changing signal statistics. Intended to be realized with electronic neural network. Codebook, which consists of patterns constituting video images, will undergo training during operation and scheme will develop codebooks ordered during training. System enables coding more compact, more immune to noise, and supports variable rate compression.

Ahalt, Stanley C.↗
NASA NTRS2019

Aura to Exit the A-Train? Aura Extended Mission Operations Concept

This presentation provides the future plans for the Earth Observing System (EOS) Aura satellite to the Aura Science Team community. It provides a summary of predictions for future fuel usage, maneuver schemes, and changes to the orbit parameters through the end of mission.

Aura Extended Mission Operations Concept↗
NASA NTRS2010

Space Flight Resource Management for ISS Operations

Although the astronaut training flow for the International Space Station (ISS) spans 2 years, each astronaut or cosmonaut often spends most of their training alone. Rarely is it operationally feasible for all six ISS crewmembers to train together, even more unlikely that crewmembers can practice living together before launch. Likewise, ISS Flight Controller training spans 18 months of learning to manage incredibly complex systems remotely in plug-and-play ground teams that have little to no exposure to crewmembers before a mission. How then do all of these people quickly become a team - a team that must respond flexibly yet decisively to a variety of situations? The answer implemented at NASA is Space Flight Resource Management (SFRM), the so-called "soft skills" or team performance skills. Based on Crew Resource Management, SFRM was developed first for shuttle astronauts and focused on managing human errors during time-critical events (Rogers, et al. 2002). Given the nature of life on ISS, the scope of SFRM for ISS broadened to include teamwork during prolonged and routine operations (O'Keefe, 2008). The ISS SFRM model resembles a star with one competency for each point: Communication, Cross-Culture, Teamwork, Decision Making, Team Care, Leadership/Followership, Conflict Management, and Situation Awareness. These eight competencies were developed with international participation by the Human Behavior and Performance Training Working Group. Over the last two years, these competencies have been used to build a multi-modal SFRM training flow for astronaut candidates and flight controllers that integrates team performance skills into the practice of technical skills. Preliminary results show trainee skill increases as the flow progresses; and participants find the training invaluable to performing well and staying healthy during ISS operations. Future development of SFRM training will aim to help support indirect handovers as ISS operations evolve further with the retirement of the Space Shuttle Program.

Schmidt, Lacey L.↗
NASA NTRS2019

Training for Long-Duration Space Missions: A Literature Review into Skill Retention and Generalizability

In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.

training↗
NASA NTRS2019

Training for Long-Duration Space Missions: A Literature Review into Skill Retention and Generalizatility

In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.

Pieters, Marc A.↗
NASA NTRS2017

Robonaut 2 and You: Specifying and Executing Complex Operations

Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.

Baker, William↗
NASA NTRS1989

Graphic overlays in high-precision teleoperation: Current and future work at JPL

In space teleoperation additional problems arise, including signal transmission time delays. These can greatly reduce operator performance. Recent advances in graphics open new possibilities for addressing these and other problems. Currently a multi-camera system with normal 3-D TV and video graphics capabilities is being developed. Trained and untrained operators will be tested for high precision performance using two force reflecting hand controllers and a voice recognition system to control two robot arms and up to 5 movable stereo or non-stereo TV cameras. A number of new techniques of integrating TV and video graphics displays to improve operator training and performance in teleoperation and supervised automation are evaluated.

Diner, Daniel B.↗
DOE OSTI2024

Enhanced Carbon Storage Forecasting via Cross-Geology Transfer Learning

Rapid simulation of the spatiotemporal evolution of pressure & saturation for SACROC 1. Neural operator was trained on only 153 simulation runs 2. Trained to account for heterogeneity and variations/uncertainties in engineering, fluids, and geology 3. Pressure forecast has less than 1% error 4. Saturation forecast has less than 2% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute. Rapid simulation of the spatiotemporal evolution of pressure & saturation for IBDP 1. Transfer Learning was implemented on the SACROC-based Neural Operator that was trained on only 20 simulation runs for IBDP Site 2. SACROC and IBDP Sites have several significant differences in geology and engineering parameters. 3. Pressure forecast has less than 5 psi error 4. Saturation forecast has less than 7% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute and only 20 simulations for training/validation.

Misra, Siddharth↗
NASA NTRS2011

The Final Count Down: A Review of Three Decades of Flight Controller Training Methods for Space Shuttle Mission Operations

Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. As the space shuttle program ends in 2011, a review of how training for STS-1 was conducted compared to STS-134 will show multiple changes in training of shuttle flight controller over a thirty year period. This paper will additionally give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams have been trained certified over the life span of the space shuttle. The training methods for developing flight controllers have evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The reader will learn what it is like to perform a simulation as a shuttle flight controller. Finally, the paper will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors.

Dittemore, Gary D.↗
NASA NTRS1985

Space shuttle/mesoscale lightning experiment

A payload integration plan (PIP) is now being developed with Johnson Space Center integration personnel which covers management, structural thermal, electrical power/avionics, training, ground operations, safety requirements, etc., in support of this experiment. If a Shuttle flight can be identified, researchers hope to conduct the experiment in the late summer or fall of 1985. Some preliminary TV lightning data has been collected by Shuttle crews on 41D and 51D and researchers are doing an analysis of it. Additional flights will be conducted to obtain data on Mesoscale Lightning Observations. Researchers will continue to study how to improve the data collection using the onboard TV cameras. By more interaction with the crews who have used the TV camera to obtain TV of lightning, they are planning to optimize the crew time and have better TV camera operations management to produce more useful data. As the crews are better trained in the use of the gain control settings and/or camera iris operations, the quality of the TV data will be improved.

Vaughan, O. H., Jr.↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗