Results for “Training Time” — Page 27

NASA NTRSDate not supplied

NASA Extravehicular Activity Technology Roadmaps for Exploration

The National Aeronautics and Space Administration (NASA) has developed and matured many technologies over the decades to advance extravehicular activity (EVA) systems. Over the last 15 years, major steps were taken to advance the technology with the Exploration Extravehicular Mobility Unit (xEMU) government reference design at the Johnson Space Center (JSC) in Houston, Texas. The xEMU builds on the lessons learned of the Apollo, Space Shuttle, and International Space Station (ISS) EMUs, evolving the technology to increase performance for extreme environments. As NASA sets its goals toward Earth’s Moon and Mars, a spacesuit design tolerable of gravity and dust will be needed for these adverse environments. NASA has used roadmaps as the means of documenting actionable plans for strategizing technology developments needed to meet NASA’s mission and goals. To help reach and create a sustained presence on the Moon, NASA procured EVA services from industry through the Exploration EVA Services (xEVAS) contract. These services include certified contractor-provided spacesuits, tools, equipment, vehicle interfaces, and support to training and real-time operations. NASA will now focus on a mission to Mars. NASA leadership has set goals and objectives related to the Agency’s vision and a Moon to Mars (M2M) strategy. This paper presents an organizational framework to provide insight into how NASA’s vision is realized. Additionally, this paper covers the maturation and development of the spacesuit technology and reveals the EVA technology roadmaps for the M2M Program. These EVA roadmaps visualize an actionable path to EVA capabilities needed for Mars exploration.

Cinda Chullen↗

NASA NTRSDate not supplied

NASA Extravehicular Activity Technology Roadmaps for Exploration

The National Aeronautics and Space Administration (NASA) has developed and matured many technologies over the decades to advance extravehicular activity (EVA) systems. Over the last 15 years, major steps were taken to advance the technology with the Exploration Extravehicular Mobility Unit (xEMU) government reference design at the Johnson Space Center (JSC) in Houston, Texas. The xEMU builds on the lessons learned of the Apollo, Space Shuttle, and International Space Station (ISS) EMUs, evolving the technology to increase performance for extreme environments. As NASA sets its goals toward Earth’s Moon and Mars, a spacesuit design tolerable of gravity and dust will be needed for these adverse environments. NASA has used roadmaps as the means of documenting actionable plans for strategizing technology developments needed to meet NASA’s mission and goals. To help reach and create a sustained presence on the Moon, NASA procured EVA services from industry through the Exploration EVA Services (xEVAS) contract. These services include certified contractor-provided spacesuits, tools, equipment, vehicle interfaces, and support to training and real-time operations. NASA will now focus on a mission to Mars. NASA leadership has set goals and objectives related to the Agency’s vision and a Moon to Mars (M2M) strategy. This paper presents an organizational framework to provide insight into how NASA’s vision is realized. Additionally, this paper covers the maturation and development of the spacesuit technology and reveals the EVA technology roadmaps for the M2M Program. These EVA roadmaps visualize an actionable path to EVA capabilities needed for Mars exploration.

Cinda Chullen↗

NASA NTRSDate not supplied

A Simplified Model of VIPER Thermal Management System. Part II: Integrated Vehicle

NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) thermal management system (TMS) relies on four loop heat pipes (LHPs) to transport electronic waste heat to the vehicle cooling radiative surface and avoid overheating. The TMS has also ten constant conductance heat pipes (CCHPs) dedicated to balance the thermal load within the internal environment where the avionics boxes are mounted, also called warm electronic box (WEB), and to transport the heat from two of the science payload instruments. The TMS also uses two thermal straps to thermally link the batteries to the WEB. These thermal components, in addition to heaters, thermostat, multi-layer insulation (MLIs), and isolators forms the core of the VIPER TMS. The complex heat transport balance managed by the TMS is challenging to characterize and model. The more fidelity and granularity of a model, the more costly the computational resources needed and the longer the simulation and modeling time. When the priority is to provide quick but reliable assessments of the thermal performance or real time thermal feedback for training of console operators, simplified modeling tools are needed. To satisfy that need, this paper describes the effort to develop and correlate a model of VIPER TMS based on control volume approach. The correlation effort in particular focuses on hibernation, cold thermal balance, and hot thermal balance data from the integrated vehicle thermal vacuum (TVAC) test. Thus, the correlated model captures the heat leaks during hibernations and the performance at two extremes, bounding, operating scenarios.

Loop Heat Pipe↗

NASA NTRSDate not supplied

A Simplified Model of VIPER Thermal Management System. Part II: Integrated Vehicle

NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) thermal management system (TMS) relies on four loop heat pipes (LHPs) to transport electronic waste heat to the vehicle cooling radiative surface and avoid overheating. The TMS has also ten constant conductance heat pipes (CCHPs) dedicated to balance the thermal load within the internal environment where the avionics boxes are mounted, also called warm electronic box (WEB), and to transport the heat from two of the science payload instruments. The TMS also uses two thermal straps to thermally link the batteries to the WEB. These thermal components, in addition to heaters, thermostat, multi-layer insulation (MLIs), and isolators forms the core of the VIPER TMS. The complex heat transport balance managed by the TMS is challenging to characterize and model. The more fidelity and granularity of a model, the more costly the computational resources needed and the longer the simulation and modeling time. When the priority is to provide quick but reliable assessments of the thermal performance or real time thermal feedback for training of console operators, simplified modeling tools are needed. To satisfy that need, this paper describes the effort to develop and correlate a model of VIPER TMS based on control volume approach. The correlation effort in particular focuses on hibernation, cold thermal balance, and hot thermal balance data from the integrated vehicle thermal vacuum (TVAC) test. Thus, the correlated model captures the heat leaks during hibernations and the performance at two extremes, bounding, operating scenarios.

Thermal Modeling↗

DOE OSTI2023

Automated recovery of rare earth permanent magnets from electric machines

A method for the automated recovery of rare earth permanent magnets from electric machines are provided. The method includes identifying electric machines in a mixed product stream for performing a unique robotic disassembly routine. Electric machines that are not identified are diverted to a robot training station, during which time the system and the method include implementing a suitable disassembly routine. A conveyor delivers the remaining electric machines to a rotary platform having multiple stations for the simultaneous disassembly of multiple electric machines. Permanent magnets are removed from the electric machines and are then sorted for recycling operations.

McIntyre, Timothy J.↗

DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗

NASA NTRS2010

Training Enhances Both Locomotor and Cognitive Adaptability to a Novel Sensory Environment

During adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform required mission tasks. The goal of this project is to develop a sensorimotor adaptability (SA) training program to facilitate rapid adaptation. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene that provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. The goal of our present study was to determine if SA training improved both the locomotor and cognitive responses to a novel sensory environment and to quantify the extent to which training would be retained. Methods: Twenty subjects (10 training, 10 control) completed three, 30-minute training sessions during which they walked on the treadmill while receiving discordant support surface and visual input. Control subjects walked on the treadmill but did not receive any support surface or visual alterations. To determine the efficacy of training all subjects performed the Transfer Test upon completion of training. For this test, subjects were exposed to novel visual flow and support surface movement, not previously experienced during training. The Transfer Test was performed 20 minutes, 1 week, 1, 3 and 6 months after the final training session. Stride frequency, auditory reaction time, and heart rate data were collected as measures of postural stability, cognitive effort and anxiety, respectively. Results: Using mixed effects regression methods we determined that subjects who received SA training showed less alterations in stride frequency, auditory reaction time and heart rate compared to controls. Conclusion: Subjects who received SA training improved performance across a number of modalities including enhanced locomotor function, increased multi-tasking capability and reduced anxiety during adaptation to novel discordant sensory information. Trained subjects maintained their level of performance over six months.

Bloomberg, J. J.↗

DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1953

Time-History Data of Maneuvers Performed by a Republic F84G Airplane During Squadron Operational Training

Preliminary results of one phase of a control-motion study program involving several jet fighter-type airplanes are presented in time-history form and are summarized as maximum measured quantities plotted against indicated airspeed. The results pertain to approximately 1,000 maneuvers performed by a Republic F-84G jet-fighter airplane during squadron operational training. The data include most tactical maneuvers of which the F-84G airplane is capable. Maneuvers were performed at pressure altitudes of 0 to 30,000 feet with indicated airspeeds ranging from the stalling speed to approximately 515 knots.

Hamer, Harold A.↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

NASA NTRS1999

Effects on Training Using Illumination in Virtual Environments

Camera based tasks are commonly performed during orbital operations, and orbital lighting conditions, such as high contrast shadowing and glare, are a factor in performance. Computer based training using virtual environments is a common tool used to make and keep CTW members proficient. If computer based training included some of these harsh lighting conditions, would the crew increase their proficiency? The project goal was to determine whether computer based training increases proficiency if one trains for a camera based task using computer generated virtual environments with enhanced lighting conditions such as shadows and glare rather than color shaded computer images normally used in simulators. Previous experiments were conducted using a two degree of freedom docking system. Test subjects had to align a boresight camera using a hand controller with one axis of rotation and one axis of rotation. Two sets of subjects were trained on two computer simulations using computer generated virtual environments, one with lighting, and one without. Results revealed that when subjects were constrained by time and accuracy, those who trained with simulated lighting conditions performed significantly better than those who did not. To reinforce these results for speed and accuracy, the task complexity was increased.

Maida, James C.↗

DOE OSTI2020

Real-time jitter correction in a photonic analog-to-digital converter

A real-time jitter meter is used to measure and digitally sample the pulse-to-pulse timing error in a laser pulse train. The jitter meter is self-referenced using a single-pulse delay line interferometer and measures timing jitter using optical heterodyne detection between two frequency channels of the pulse train. Jitter sensitivity down to 3×10 -10 fs 2 /Hz at 500 MHz has been demonstrated with a pulse-to-pulse noise floor of 1.6 fs. Furthermore, as a proof of principle, the digital correction of the output of a high-frequency photonic analog-to-digital converter (PADC) is demonstrated with an emulated jitter signal. Up to 23 dB of jitter correction, down to the noise floor of the PADC, is accomplished with radio-frequency modulation up to 40 GHz.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗

NASA NTRS2001

Effect of Exercise Training and +Gz Acceleration Training on Men

Countermeasures for reduction in work capacity (maximal oxygen uptake and strength) during spaceflight and enhanced orthostatic intolerance during re-entry, landing and egress from the return vehicle are continuing problems. The purpose for this study was to test the hypothesis that passive-acceleration training; supine, interval, exercise plus acceleration training and exercise combined with acceleration training would improve orthostatic tolerance in ambulatory men; and that addition of the aerobic exercise conditioning would not alter this improved tolerance from that of passive-acceleration training. Seven men (24-38 yr) underwent "Passive" training on the Ames human-powered centrifuge (HPC) for 30 min, "Exercise" training on the cycle ergometer with constant +Gz acceleration; and "Combined" exercise training at 40% to 90% of the HPC +Gz(max) exercise level. Maximal supine exercise loads increased significant (P<0.05) by 8.3% (Passive), 12.6% (Exercise), and by 15.4% (Combined) after training, but their post-training maximal oxygen uptakes and maximal heart rates were unchanged. Maximal time to fatigue (endurance) was unchanged with Passive was increased (P<0.05) with Exercise and Combined training. Thus, the exercise in the Exercise and Combined training Phases resulted in greater maximal loads and endurance without effect on maximal oxygen uptake or heart rate. There was a 4% to 6% increase (P<0.05) in all four quadriceps muscle volumes (right and left) after post-Combined training. Resting pre-tilt heart rate was elevated by 12.9% (P<0.05) only after Passive training suggesting that the exercise training attenuated the HR response. Plasma volume (% Delta) was uniformly decreased by 8% to 14% (P<0.05) at tilt-tolerance pre- vs. post-training indicating essentially no effect of training on the level of hypovolemia. Post-training tilt-tolerance time and heart rate were increased (P<0.05) only with Passive training by 37.8% and by 29.1%, respectively. Thus, addition of exercise training appeared to attenuate the increased Passive tilt-tolerance.

Greenleaf, John E.↗

NASA NTRS2001

The Aqua-Aura Train

This talk will focus on the afternoon constellation of EOS platforms and the scientific benefits that arise from this formation. The afternoon EOS constellation or the "A-train" will provide unprecedented information on clouds and aerosols. At 1:30 PM crossing time EOS-Aqua begins the train with the MODIS, CERES and AIRS instruments making aerosol, cloud, radiation budget , temperature and water vapor measurements. AMSR-E will also make total column water measurements. Following Aqua by one minute, Cloudsat will make active radar precipitation measurements as and PICASSOCENA will make lidar measurements of clouds and aerosols. Fourteen minutes later, EOS-Aura will pass through the same space making upper troposphere water vapor and ice profiles as well as some key trace gases associated with convective processes (MLS and HIRDLS). Additional measurements of aerosols will be made by Aura's OMI instrument.

Schoeberl, Mark↗

NASA NTRS1979

Effects of tunnel and station size on the costs and service of subway transit systems

The feasibility of less spacious, less costly underground rail mass transit system designs is studied. The major cost saving expected from alternative tunnel designs results from using precast concrete segment liners in place of steel. The saying expected for a two-foot decrease in the diameter of twin, single track tunnels is about two million dollars per route mile from 13 million dollars for precast concrete segment liners (a saving of about 16%). The cost per route-mile of a double track tunnel appears to be 15 to 25% higher than for the twin, single track tunnels. The effective cost saving expected from stations with four-car train capability instead of the usual eight-car trains is nearly 25% or seven million dollars per route mile. The saving in station costs can be obtained while improving service to the user (lower transit time and less waiting for trains) up to a capacity of 36,000 riders per hour in each direction.

Dayman, B., Jr.↗

NASA NTRS1992

Space life sciences perspectives for Space Station Freedom

It is now generally acknowledged that the life science discipline will be the primary beneficiary of Space Station Freedom. The unique facility will permit advances in understanding the consequences of long duration exposure to weightlessness and evaluation of the effectiveness of countermeasures. It will also provide an unprecedented opportunity for basic gravitational biology, on plants and animals as well as human subjects. The major advantages of SSF are the long duration exposure and the availability of sufficient crew to serve as subjects and operators. In order to fully benefit from the SSF, life sciences will need both sufficient crew time and communication abilities. Unlike many physical science experiments, the life science investigations are largely exploratory, and frequently bring unexpected results and opportunities for study of newly discovered phenomena. They are typically crew-time intensive, and require a high degree of specialized training to be able to react in real time to various unexpected problems or potentially exciting findings. Because of the long duration tours and the large number of experiments, it will be more difficult than with Spacelab to maintain astronaut proficiency on all experiments. This places more of a burden on adequate communication and data links to the ground, and suggests the use of AI expert system technology to assist in astronaut management of the experiment. Typical life science experiments, including those flown on Spacelab Life Sciences 1, will be described from the point of view of the demands on the astronaut. A new expert system, 'PI in a Box,' will be introduced for SLS-2, and its applicability to other SSF experiments discussed. (This paper consists on an abstract and ten viewgraphs.)

Young, Laurence R.↗

NASA NTRS1992

A teleoperation training simulator with visual and kinesthetic force virtual reality

A force-reflecting teleoperation training simulator with a high-fidelity real-time graphics display has been developed for operator training. A novel feature of this simulator is that it enables the operator to feel contact forces and torques through a force-reflecting controller during the execution of the simulated peg-in-hole task, providing the operator with the feel of visual and kinesthetic force virtual reality. A peg-in-hole task is used in our simulated teleoperation trainer as a generic teleoperation task. A quasi-static analysis of a two-dimensional peg-in-hole task model has been extended to a three-dimensional model analysis to compute contact forces and torques for a virtual realization of kinesthetic force feedback. The simulator allows the user to specify force reflection gains and stiffness (compliance) values of the manipulator hand for both the three translational and the three rotational axes in Cartesian space. Three viewing modes are provided for graphics display: single view, two split views, and stereoscopic view.

Kim, Won S.↗