SEARCH · Search NASA

Results for “Training Principles”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRS2018

Applying Research-Based Training Principles: Towards Crew-Centered, Mission-Oriented Space Flight Training

This chapter describes a training approach that applies empirically derived principles of training to re-imagining the overall design of NASA’s space flight training program. The chapter is focused specifically on the design of astronaut training for NASA’s future deep space, exploration missions to Mars. We briefly describe NASA’s space flight training practices during the Apollo and Space Shuttle eras as well as NASA’s current practices for training astronauts for their missions to the International Space Station. We provide an overview of NASA’s current concepts for a mission to Mars to scope our training approach. We envision a new space flight training approach which we term “crew-centered, mission oriented” training, inspired by the design approach offered in the context of airline pilot training by Barshi (2015). We apply some of the training principles reviewed by Kole and his colleagues in the companion volume (Kole, Healy, Schneider & Barshi, 2019), as well as by other researchers in training science (e.g., Ericsson, Krampe, & Tesch-Römer, 1993; Healy & Bourne, 2012; Salas, Wilson, Priest and Guthrie, 2006), into real-world, practical guidelines for the particular context of training astronauts for a mission to Mars.processes over very long retention intervals.

Training↗

NASA NTRS2019

The Comprehensive LOFT

The training of pilots at major US air carriers has changed little over the years. These courses often begin in learning each and every sub-system of the particular airplane to be flown. The training course culminates in a series of training sessions in a full-motion, full-mission flight simulator. Most US airlines now incorporate one final simulator training session known as LOFT, Line Oriented Flight Training, where rather than going through a series of disconnected, isolated, flight maneuvers, the session is conducted as a flight from a departure airport to a destination airport. Flying for an Air Line, one is a Line Pilot, flying the "line." And so the LOFT session in the simulator is supposed to represent the reality of line operations and thus prepare the trainee to fly the line. But if the ultimate goal of the training is to produce a pilot who is ready to safely and efficiently fly the line, shouldn't all training be "line oriented"? This paper describes an approach to structuring airline flight training such that all training is "line oriented." This approach represents an opportunity to translate many of the training principles offered by Healy and her colleagues into training specifications.

Training Principles↗

NASA NTRS2017

ISS Training Best Practices and Lessons Learned

Training our crew members for long duration exploration-class missions (LDEM) will have to be qualitatively and quantitatively different from current training practices. However, there is much to be learned from the extensive experience NASA has gained in training crew members for missions on board the International Space Station (ISS). Furthermore, the operational experience on board the ISS provides valuable feedback concerning training effectiveness. Keeping in mind the vast differences between current ISS crew training and training for LDEM, the needs of future crew members, and the demands of future missions, this ongoing study seeks to document current training practices and lessons learned. The goal of the study is to provide input to the design of future crew training that takes as much advantage as possible of what has already been learned and avoids as much as possible past inefficiencies. Results from this study will be presented upon its completion. By researching established training principles, examining future needs, and by using current practices in spaceflight training as test beds, this research project is mitigating program risks and generating templates and requirements to meet future training needs.

ISS↗

NASA NTRS2018

ISS Training Best Practices and Lessons Learned

Training our crew members for long-duration Deep Space Transport (DST) missions will have to be qualitatively and quantitatively different from current training practices. However, there is much to be learned from the extensive experience NASA has gained in training crew members for missions on board the International Space Station (ISS). Furthermore, the operational experience on board the ISS provides valuable feedback concerning training effectiveness. Keeping in mind the vast differences between current ISS crew training and training for DST missions, the needs of future crew members, and the demands of future missions, this ongoing study seeks to document current training practices and lessons learned. The goal of the study is to provide input to the design of future crew training that takes as much advantage as possible of what has already been learned and avoids as much as possible past inefficiencies. Results from this study will be presented upon its completion. By researching established training principles, examining future needs, and by using current practices in spaceflight training as test beds, this research project is mitigating program risks and generating templates and requirements to meet future training needs.

training↗

NASA NTRS2009

Human Factors in Training

Future space missions will be significantly longer than current shuttle missions and new systems will be more complex than current systems. Increasing communication delays between crews and Earth-based support means that astronauts need to be prepared to handle the unexpected on their own. As crews become more autonomous, their potential span of control and required expertise must grow to match their autonomy. It is not possible to train for every eventuality ahead of time on the ground, or to maintain trained skills across long intervals of disuse. To adequately prepare NASA personnel for these challenges, new training approaches, methodologies, and tools are required. This research project aims at developing these training capabilities. By researching established training principles, examining future needs, and by using current practices in space flight training as test beds, both in Flight Controller and Crew Medical domains, this research project is mitigating program risks and generating templates and requirements to meet future training needs. Training efforts in Fiscal Year 08 (FY08) strongly focused on crew medical training, but also began exploring how Space Flight Resource Management training for Mission Operations Directorate (MOD) Flight Controllers could be integrated with systems training for optimal Mission Control Center (MCC) operations. The Training Task addresses Program risks that lie at the intersection of the following three risks identified by the Project: (1) Risk associated with poor task design (2) Risk of error due to inadequate information (3) Risk associated with reduced safety and efficiency due to poor human factors design

Barshi, Immanuel↗

NASA NTRS2010

Human Factors in Training

Future space missions will be significantly longer than current shuttle missions and new systems will be more complex than current systems. Increasing communication delays between crews and Earth-based support means that astronauts need to be prepared to handle the unexpected on their own. As crews become more autonomous, their potential span of control and required expertise must grow to match their autonomy. It is not possible to train for every eventuality ahead of time on the ground, or to maintain trained skills across long intervals of disuse. To adequately prepare NASA personnel for these challenges, new training approaches, methodologies, and tools are required. This research project aims at developing these training capabilities. By researching established training principles, examining future needs, and by using current practices in space flight training as test beds, both in Flight Controller and Crew Medical domains, this research project is mitigating program risks and generating templates and requirements to meet future training needs. Training efforts in Fiscal Year 09 (FY09) strongly focused on crew medical training, but also began exploring how Space Flight Resource Management training for Mission Operations Directorate (MOD) Flight Controllers could be integrated with systems training for optimal Mission Control Center (MCC) operations. The Training Task addresses Program risks that lie at the intersection of the following three risks identified by the Project: 1) Risk associated with poor task design; 2) Risk of error due to inadequate information; and 3) Risk associated with reduced safety and efficiency due to poor human factors design.

Barshi, Immanuel↗

NASA NTRS2017

The Effects of Long-Duration Spaceflight on Training Retention and Transfer

Training our crew members for long duration, exploration-class missions will have to maximize long-term retention and transfer of the trained skills. The expected duration of the missions, our inability to predict all the possible tasks the crew will be called upon to perform, and the low training-to-mission time ratio require that the training be maximally effective such that the skills acquired during training will be retained and will be transferrable across a wide range of specific tasks that are different from the particular tasks used during training. However, to be able to design training that can achieve these ambitious goals, we must first understand the ways in which long-duration spaceflight affects training retention and transfer. Current theories of training retention and transfer are largely based on experimental studies conducted at university laboratories using undergraduate students as participants. Furthermore, all such studies have been conducted on Earth. We do not know how well the results of these studies predict the performance of crew members. More specifically, we do not know how well the results of these studies predict the performance of crew members in space and especially during long-duration missions. To address this gap in our knowledge, the current on-going study seeks to test the null hypothesis that performance of university undergraduate students on Earth on training retention and transfer tests do in fact predict accurately the performance of crew members during long-duration spaceflights. To test this hypothesis, the study employs a single 16-month long experimental protocol with 3 different participant groups: undergraduate university students, crew members on the ground, and crew members in space. Results from this study will be presented upon its completion. This poster presents results of study trials of the two tasks used in this study: a data entry task and a mapping task. By researching established training principles, by examining future needs, and by using current practices in spaceflight training as test beds, this research project is mitigating program risks and generating templates and requirements to meet future training needs.

ISS↗

NASA NTRS2018

The Effects of Long-Duration Spaceflight on Training Retention and Transfer

training↗

NASA NTRS1984

Preparing for the unexpected - A psychologist's case for improved training

In the procedures designed to minimize human errors that lead to aircraft incidents, the improved human factor engineering and automation approaches must be supplemented by new training methods. Changes are suggested in the preprogrammed training principles which are currently based almost exclusively on the procedures-oriented environment, with insufficient training for cognitive processing and awareness. Use of the Line-Oriented Flight Training procedure, in which a training simulator is supplemented by a highly structured script or scenario to simulate the total line operational environment for the purpose of simultaneously training the entire flight crew, offers one way of providing pilots and other crewmembers with the experience of dealing with unexpected or stressful events. Of primary importance is maximal coordination between the aircraft captain and other crewmembers during the flight, which puts emphasis on the importance of teamwork and personal relations among all other crewmembers. The current FARs governing training and proficiency will have to be modified to accommodate new training appoaches.

Foushee, H. C.↗

DOE OSTI2026

Full-stack Quantification of Variability in Predicting Ion Transport Properties using Machine-learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is therefore crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from limitations in first-principles training data, intrinsic MLIP model errors in representing the data, and the statistical noise introduced during subsequent MD simulations. Using ion transport in Li7P3S11 as a case study, we systematically assess the impact of training set size and selection, neural network stochasticity, and MD sampling statistics on predicted diffusivity and activation energy. We find that when using equivariant MLIP architectures with standard MD protocols, uncertainty arising from MD sampling dominates over model-induced errors. In contrast, MLIP errors relative to the underlying first-principles data are consistently minor. Given this, there are two main routes to improving the accuracy of predictions based on MLIP potentials: adopting higher accuracy reference data generation methods, and improving the MD sampling statistics.

36 MATERIALS SCIENCE↗

NASA NTRS2020

Towards Crew-Centered, Mission-Oriented Space Flight Training

This poster describes a training approach that applies empirically derived principles of training to reimagining the overall design of NASA's space flight training program. The poster is focused specifically on the design of astronaut training for NASA's future deep space, exploration missions to Mars. We briefly describe NASA's space flight training practices during the Apollo and Space Shuttle eras as well as NASA's current practices for training astronauts for their missions to the International Space Station. We provide an overview of NASA's current concepts for a mission to Mars to scope our training approach. We envision a new space flight training approach which we term crew-centered, mission oriented training, inspired by the design approach offered in the context of airline pilot training by Barshi. We apply research-based training principles reviewed by Kole and his colleagues, as well as by other researchers in training science, into real-world, practical guidelines for the particular context of training astronauts for a mission to Mars.

Dempsey, Donna L.↗

NASA NTRS2000

Resistance exercise countermeasures for space flight: implications of training specificity

While resistance exercise should be a logical choice for prevention of strength loss during unloading, the principle of training specificity cannot be overlooked. Our purpose was to explore training specificity in describing the effect of our constant load exercise countermeasure on isokinetic strength performance. Twelve healthy men (mean +/- SD: 28.0 +/- 5.2 years, 179.4 +/- 3.9 cm, 77.5 +/- 13.6 kg) were randomly assigned to no exercise or resistance exercise (REX) during 14 days of bed rest. REX performed five sets of leg press exercise to volitional fatigue (6-10 repetitions) every other day. Unilateral isokinetic concentric-eccentric knee extension testing performed before and on day 15 prior to reambulation included torque-velocity and power-velocity relationships at four velocities (0.52, 1.75, 2.97, and 4.19 rad s-1), torque-position relationship, and contractile work capacity (10 repetitions at 1.05 rad s-1). Two (group) x 2 (time) ANOVA revealed no group x time interactions; thus, groups were combined. Across velocities, angle-specific torque fell 18% and average power fell 20% (p < 0.05). No velocity x time or mode (concentric/eccentric) x time interactions were noted. Torque x position decreased on average 24% (p < 0.05). Total contractile work dropped 27% (p < 0.05). Results indicate bed rest induces rapid and marked reductions in strength and our constant load resistance training protocol did not prevent isokinetic strength losses. Differences between closed-chain training and open-chain testing may explain the lack of protection.

Randomized Controlled Trial↗

DOE OSTI2025

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

bio-inspired computing↗

DOE OSTI2024

Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.

42 ENGINEERING↗

NASA NTRS1967

Eddy current testing - Classroom training handbook

Eddy current principles, equipment, methods, and applications in nondestructive testing reviewed in classroom training textbook

Source record↗

NASA NTRS1975

Spacelab simulation using a Lear Jet aircraft: Mission no. 4 (ASSESS program)

The fourth ASSESS Spacelab simulation mission utilizing a Lear Jet aircraft featured trained experiment operators (EOs) in place of the participating scientists, to simulate the role and functions of payload specialists in Spacelab who may conduct experiments developed by other scientists. The experiment was a broadband infrared photometer coupled to a 30-cm, open port, IR telescope. No compromises in equipment design or target selection were made to simplify operator tasks; the science goals of the mission were selected to advance the mainline research program of the principle investigator (PI). Training of the EOs was the responsibility of the PI team and consisted of laboratory sessions, on-site training during experiment integration, and integrated mission training using the aircraft as a high-fidelity simulator. The EO permission experience in these several disciplines proved adequate for normal experiment operations, but marginal for the identification and remedy of equipment malfunctions. During the mission, the PI utilized a TV communication system to assist the EOs to overcome equipment difficulties; both science and operations were successfully implemented.

Reller, J. O., Jr.↗

DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗