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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Coherent high-power RF wakefield generation by electron bunch trains in a metamaterial structure

In this paper, we present an experimental study of coherent high-power wakefield generation in a metamaterial (MTM) structure at 11.7 GHz by 65 MeV electron bunch trains at the Argonne Wakefield Accelerator (AWA), following a previous experiment, the Stage-I experiment, at the AWA. Both the Stage-II experiment, reported in this paper, and the Stage-I experiment were conducted using MTM structures, which are all-metal periodic structures with the period being much smaller than the wavelength. Differences between the two experiments include (1) structure length (Stage-I 8 cm and Stage-II 20 cm); (2) number of bunches used to excite the structure (Stage-I with two bunches, up to 85 nC of total charge; Stage-II with eight bunches, up to 224 nC of total charge); and (3) highest peak power measured (Stage-I 80 MW in a 2 ns pulse and Stage-II 380 MW in a 10 ns pulse). High-power radio frequency pulses were generated by reversed Cherenkov radiation of the electron beam due to the negative group velocity in the MTM structures. Because the radiation is coherent, a train of bunches with a proper spacing can build up to achieve a high peak power. The observed output power levels are very promising for future applications in direct collinear wakefield acceleration or in transfer to a second accelerator for two-beam acceleration.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Fine-Grained Exploitation of Mixed Precision for Faster CNN Training

As deep convolutional neural networks (CNNs) have become increasingly popular and successful at an ever-widening number of machine learning tasks specialized hardware has become increasingly available for training and deploying them. NVIDIA's recent Volta architecture includes tensor cores which perform a fused operation reduced and mixed precision (16-bit multiply, 32-bit accumulate). Recent research indicates that, typically, very little is lost (in terms of training accuracy) when half precision is used in place of single precision, and performance gains can be made by doing arithmetic in reduced precision. In this work we demonstrate that making layer-by-layer choices as to the arithmetic/data precision can lead to further performance improvement. In our study of 25,200 CNNs we demonstrate an average speedup (over purely half precision) of 1.27x and speedups as high as 3.64x by appropriately combining single and half precision arithmetic and data types on a layer-by-layer basis.

Johnston, Travis↗
NASA NTRS1973

An evaluation of the effects of bed rest, sleep deprivation and discontinuance of training on the physical fitness of highly trained young men

This experiment was conducted to determine what physiological effects result when highly trained subjects are confined to bed, deprived of sleep, or allowed to discontinue training. Results indicated: (1) There was a moderate increase in strength variables due to the training in this experiment but the stress which the subjects received caused a negligible change in strength variables. (2) The training program resulted in highly significant changes in specific bicycle ergometer variables indicating good increases in cardiopulmonary fitness. Five days of bed rest or fifty hours of sleep deprivation caused comparable drastic decreases in cardiopulmonary fitness. Post stress the subjects reverted to a normal daily schedule and after two weeks they had recovered about half of what they lost. (3) Cardiac output remains relatively constant at a constant work load, but stroke volume increases with conditioning and decreases with deconditioning due to stress.

Olree, H. D.↗
NASA NTRS1980

Payload Crew Training Complex (PCTC) utilization and training plan

The physical facilities that comprise the payload crew training complex (PCTC) are described including the host simulator; experiment simulators; Spacelab aft flight deck, experiment pallet, and experiment rack mockups; the simulation director's console; payload operations control center; classrooms; and supporting soft- and hardware. The parameters of a training philosophy for payload crew training at the PCTC are established. Finally the development of the training plan is addressed including discussions of preassessment, and evaluation options.

Self, M. R.↗
NASA NTRS2007

A Review of Training Methods and Instructional Techniques: Implications for Behavioral Skills Training in U.S. Astronauts (DRAFT)

Long-duration space missions (LDM) place unique physical, environmental and psychological demands on crewmembers that directly affect their ability to live and work in space. A growing body of research on crews working for extended periods in isolated, confined environments reveals the existence of psychological and performance problems in varying degrees of magnitude. The research has also demonstrated that although the environment plays a cathartic role, many of these problems are due to interpersonal frictions (Wood, Lugg, Hysong, & Harm, 1999), and affect each individual differently. Consequently, crewmembers often turn to maladaptive behaviors as coping mechanisms, resulting in decreased productivity and psychological discomfort. From this body of research, critical skills have been identified that can help a crewmember better navigate the psychological challenges of long duration space flight. Although most people lack several of these skills, most of them can be learned; thus, a training program can be designed to teach crewmembers effective leadership, teamwork, and self-care strategies that will help minimize the emergence of maladaptive behaviors. Thus, it is the purpose of this report is twofold: 1) To review the training literature to help determine the optimal instructional methods to use in delivering psychological skill training to the U.S. Astronaut Expedition Corps, and 2) To detail the structure and content of the proposed Astronaut Expedition Corps Psychological Training Program.

Hysong, Sylvia J.↗
NASA NTRS2010

Simulation Training Versus Real Time Console Training for New Flight Controllers

For new flight controllers, the two main learning tools are simulations and real time console performance training. These benefit the new flight controllers in different ways and could possibly be improved. Simulations: a) Allow for mistakes without serious consequences. b) Lets new flight controllers learn the working style of other new flight controllers. c) Lets new flight controllers eventually begin to feel like they have mastered the sim world, so therefore they must be competent in the real time world too. Real time: a) Shows new flight controllers some of the unique problems that develop and have to be accounted for when dealing with certain payloads or systems. b) Lets new flight controllers experience handovers - gathering information from the previous shift on what the room needs to be aware of and what still needs to be done. c) Gives new flight controllers confidence that they can succeed in the position they are training for when they can solve real anomalies. How Sims could be improved and more like real-time ops for the ISS Operations Controller position: a) Operations Change Requests to review. b) Fewer anomalies (but still more than real time for practice). c) Payload Planning Manager Handover sheet for the E-1 and E-3 reviews. d) Flight note in system with at least one comment to verify for the E-1 and E-3 reviews How the real time console performance training could be improved for the ISS Operations Controller position: a) Schedule the new flight controller to be on console for four days but with a different certified person each day. This will force them to be the source of knowledge about every OCR in progress, everything that has happened in those few days, and every activity on the timeline. Constellation program flight controllers will have to learn entirely from simulations, thereby losing some of the elements that they will need to have experience with for real time ops. It may help them to practice real time console performance training in the International Space Station or Space Shuttle to gather some general anomaly resolution and day-to-day task management skills.

Heaton, Amanda↗
DOE OSTI2021

Delivering Effective Virtual Energy-Focused Trainings: Successful Strategies and Lessons Learned from the Virtual Cohort In-Plant Training Pilot

While there are always barriers to energy efficiency efforts, few challenges have been as severe as the global COVID-19 pandemic. Many manufacturers throughout the United States and the world have been trying to determine how to safely open and operate their plants. To minimize the number of people working within facilities and maximize the physical distance between workers, only essential facility engineers are allowed into plants to ensure proper operations. The Department of Energy’s Better Plants Program created In-Plant Trainings (INPLTs) to help partners develop in-house expertise in energy efficiency. Delivering INPLTs in the traditional in-person fashion is infeasible under these circumstances. To continue providing INPLTs during this difficult time, the Better Plants Program piloted Virtual Cohort In-Plant Trainings (VINPLTs) on wastewater treatment and ammonia industrial refrigeration systems from October 27 to November 19, 2020. This paper provides an overview of the pilot, then discusses successful strategies and lessons learned from this pilot for delivering effective virtual energy-focused trainings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

The Second ARM Training and Science Application Event: Training the Next Generation of Atmospheric Scientists

The Atmospheric Radiation Measurement (ARM) user facility (Turner and Ellingson 2016) collects valuable observations of atmospheric variables affecting the Earth’s radiation budget. These include properties of aerosol, cloud, precipitation, dynamic, and thermodynamic fields. The ARM user facility also supports routine high-resolution modeling efforts at its Southern Great Plains (SGP) site, and over the years it has had significant impacts on cloud-resolving and Earth system model development (Krueger et al. 2016; Randall et al. 2016). In an effort to promote use of the ARM data by the next generation of atmospheric scientists, the second ARM Summer Training and Science Applications Event on observations and modeling of clouds and precipitation took place from 14 to 21 July 2018. The event was sponsored by the ARM User Facility and was catered toward graduate students and early career scientists interested in observations and modeling of aerosol, cloud, and precipitation processes. Theoretical and practical instructions on the application of ground- and aircraft-based observations from a wide array of active and passive sensors, and finescale-resolution model simulations were provided during the training. This informed the participants about innovative methods for using the ARM data and numerical models to address complex scientific questions. The event was locally organized by the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and was held at the National Weather Center in Norman, Oklahoma. The training was attended by 12 instructors and 24 participants.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Dynamic training devices in CRM training

Pilot training effectiveness and flying safety of a seasonal tour flight company are described. The change from single pilot to two pilot operated twin otters is examined. The use of the ATC 810 training device, its possibilities and training capacity is outlined. Problem areas which may arise, emergency system and pilot/passenger interaction are analyzed.

Lawver, J.↗
DOE OSTI2021

Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21): 15-Year Evaluation of an Innovative Research Training Program

To evaluate the 15-year impact of a transdisciplinary research training program for graduate students, postdoctoral fellows, and clinical trainees focused on radiation science, entitled Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21) with a primary objective to build capacity in radiation research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

America’s Cutting Edge CNC machining and metrology training

This paper describes the workforce development activities supported by America’s Cutting Edge (ACE), a national initiative for machine tool technology development and advancement. ACE is supported by the Department of Defense Industrial Base Analysis and Sustainment (IBAS) program from the Office of Industrial Policy. Both the online and in-person components of the computer numerically controlled (CNC) machining and metrology training programs are summarized and participation information is provided.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Decomposing the Train-Scheduling Problem into Integer-Optimal Polytopes

This paper presents conditions for which the linear relaxation for the train-scheduling problem is integer optimal. These conditions are then used to identify how to partition a general problem’s feasible region into integer-optimal polytopes. Such an approach yields an extended formulation that contains far fewer binary variables. Our computational experiments show that this approach results in significant computational savings. Moreover, this approach scales well when the train-scheduling problem is modeled using smaller time increments, allowing for higher fidelity models to be solved without significantly increasing the required computational time.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

A-Train Datalist - A New GES DISC Service to Allow One-Stop Shopping for A-Train Data

The currently available services at the Goddard Earth Sciences Data Information Services Center (GES DISC) only allow users to select variables from a single data set at a time. Because entire variables from a data set are often displayed, user selection of variables of interest can be overwhelming. At the American Geophysical Union (AGU) 2016 Fall Meeting, GES DISC unveiled a new service called Datalist: a collection of predefined or user-defined data variables from one or more archived data sets. Our science support team has been curating Datalists and providing added value to the user community.Originally known as Afternoon Constellation, A-Train includes six currently on polar-orbiting Earth observation satellites: OCO-2, GCOM-W1, Aqua, CALIPSO, CloudSat, and Aura, which travel a few minutes apart from each other. This constellation arrangement has enabled coordinated science observations further forming comprehensive pictures of Earth weather and climate that are readily for use in crucial studies such as climate change.GES DISC Datalists are based on the software architecture of the new GES DISC website (also unveiled at the AGU 2016 Fall Meeting). The GES DISC science support team has created a Datalist to support the A-Train Data Depot (ATDD). Using pre-defined Datalist should hopefully save users significant effort in their data searches.

A-Train data ordering↗
NASA NTRS2021

Acoustic Flight Simulator Architecture, Noise Training Aid Manual, and Its Training Benefits

The Revolutionary Vertical Lift Technology project at NASA is researching source noise and response to reduce the noise impact of vertical takeoff and landing aircraft. To aid in this effort to address community noise, Dr. Eric Greenwood has developed real-time helicopter noise modeling that uses noise hemispheres generated from measured acoustic data as a basis for informing predictions the ground noise footprint of a particular helicopter model. This report describes a depiction of the predicted ground noise footprint and related information on how to reduce the ground noise footprint. Additionally, it details how to use the acoustic flight simulator and its associated programs. Also included are the results from a brief survey asking potential users about what they thought about the noise training aid aspect of the acoustic flight simulator and whether the predicted ground noise footprint display would be useful in learning about how to decrease noise. In summary, potential users have indicated that the noise training aid would be beneficial in learning how their vehicle’s state affects noise.

noise, training, helicopter, simulation↗
DOE OSTI2025

Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing

This paper introduces a Physics-Informed Neural Network (PINN) technique that co-trains neural networks (NNs) that represent each function in a system of equations to simultaneously solve equations representing an out-of-autoclave (OOA) cure process while conducting optimization in adherence to process requirements. Specifically, this co-training approach benefits from using NNs to represent OOA inputs (air temperature profile) and outputs (part and tool temperature profiles and degree of cure). Production requirements can then be levied on the inputs, such as maximum air temperature and minimum cure cycle, and simultaneously on the outputs, such as degree of cure, maximum part temperature, and part temperature rate limits. The technique is validated with finite element (FE) simulations and physical experiments for curing a Toray T830H-6 K/3900-2D composite panel. Furthermore, this novel approach efficiently models and optimizes the OOA cure process.

Composite curing↗
DOE OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗
DOE OSTI2021

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss--Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Finally, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

97 MATHEMATICS AND COMPUTING↗