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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 397 records · Page 22

All sources NASA NTRS DOE OSTI

NASA NTRS1995

Effect of training on blood volume and plasma hormone concentrations in the elderly

The purpose of this investigation was to determine the effects of 6 months of endurance training on resting plasma (PV) and blood volume (BV), and resting hormone and electrolyte concentrations in the elderly. Thirty-eight elderly men and women (ages 60-82 yr) were assigned to endurance exercise training (N = 29) or to control (N = 9) groups. Resting plasma levels of adrenocorticotropic hormone, vasopressin, aldosterone, norepinephrine, epinephrine, sodium, potassium, and protein were measured at the start (T1) and end (T2) of 26 wk of training. PV measurement was performed using the Evan's blue dye technique. Endurance training consisted of uphill treadmill walking or stairclimbing exercise 3 times.wk-1, 30-45 min.d-1, at 75-84% of maximal heart rate reserve. The exercise group increased VO2max by 11.2% (P < or = 0.05) and increased resting PV and BV by 11.2% and 12.7% (P < or = 0.05), respectively. Hormone and electrolyte levels in the exercise group remained unchanged; all variables were unchanged in the control group. These results are similar to findings in younger individuals. Because plasma hormone concentrations were maintained despite a chronically elevated BV, endurance training in healthy, elderly subjects may be associated with a resetting of volume receptors.

Clinical Trial↗

NASA NTRS1992

Effects of training on muscle O2 transport at VO2max

To quantify the relative contributions of convective and peripheral diffusive components of O2 transport to the increase in leg O2 uptake (VO2leg) at maximum O2 uptake (VO2max) after 9 wk of endurance training, 12 sedentary subjects (age 21.8 +/- 3.4 yr, VO2max 36.9 +/- 5.9 ml.min-1.kg-1) were studied. VO2max, leg blood flow (Qleg), and arterial and femoral venous PO2, and thus VO2leg, were measured while the subjects breathed room air, 15% O2, and 12% O2. The sequence of the three inspirates was balanced. After training, VO2max and VO2leg increased at each inspired O2 concentration [FIO2; mean over the 3 FIO2 values 25.2 +/- 17.8 and 36.5 +/- 33% (SD), respectively]. Before training, VO2leg and mean capillary PO2 were linearly related through the origin during hypoxia but not during room air breathing, suggesting that, at 21% O2, VO2max was not limited by O2 supply. After training, VO2leg and mean capillary PO2 at each FIO2 fell along a straight line with zero intercept, just as in athletes (Roca et al. J. Appl. Physiol. 67: 291-299, 1989). Calculated muscle O2 diffusing capacity (DO2) rose 34% while Qleg increased 19%. The relatively greater rise in DO2 increased the DO2/Qleg, which led to 9.9% greater O2 extraction. By numerical analysis, the increase in Qleg alone (constant DO2) would have raised VO2leg by 35 ml/min (mean), but that of DO2 (constant Qleg) would have increased VO2leg by 85 ml/min, more than twice as much. The sum of these individual effects (120 ml/min) was less (P = 0.013) than the observed rise of 164 ml/min (mean). This synergism (explained by the increase in DO2/Qleg) seems to be an important contribution to increases in VO2max with training.

NASA Discipline Musculoskeletal↗

NASA NTRS2016

Human Research Program: Long Duration, Exploration-Class Mission Training Design

This is a presentation to the International Training Control Board that oversees astronaut training for ISS. The presentation explains the structure of HRP, the training-related work happening under the different program elements, and discusses in detail the research plan for the Training Risk under SHFHSHFE. The group includes the crew training leads for all the space agencies involved in ISS: Japan, Europe, Russia, Canada, and the US.

crew training↗

NASA NTRSDate not supplied

Three-Dimensional Structure and Modeling of a Normal Bifurcated Shock Train from Experimental Measurements

An asymmetric, bifurcated normal shock train in a Mach 2 constant area, rectangular duct is investigated in the University of Michigan Direct Connect Isolator facility. High-speed schlieren imaging, wall static pressure measurements, surface oil flow visualization, and particle image velocimetry of a shock train are synthesized into a three-dimensional representation of shock train structure. This visualization is then used to inform the underlying flow physics of the distributed fluid dynamical processes along the structure. The detailed, three-dimensional morphology of the flow profile entering the shock train is shown to have a significant impact on the separated flow morphology of the shock train. This results in skewed supersonic core flow with alternating separation bubbles in the surrounding boundary layers. Finally, the efficacy of the prevailing pseudo-shock models in the literature is analyzed with the support of the available flow measurements. None are found to simultaneously model both the pressure and streamwise Mach number profile of the UMDCI pseudo-shock.

Hypersonics↗

NASA NTRSDate not supplied

Three-Dimensional Structure and Modeling of a Normal Bifurcated Shock Train from Experimental Measurements

An asymmetric, normal bifurcated shock train in a Mach 2 constant area, rectangular duct is investigated in the University of Michigan Direct Connect Isolator facility. High-speed schlieren imaging, wall static pressure measurements, surface oil flow visualization, and particle image velocimetry of the shock train are synthesized into a three-dimensional representation. This visualization is then used to inform the underlying flow physics of the distributed fluid dynamical processes along the shock train. The detailed, three-dimensional morphology of the flow profile entering the shock train is shown to have a significant impact on the separated flow morphology of the shock train. This results in a skewed supersonic core flow with alternating separation bubbles in the surrounding boundary layers. Finally, the efficacy of the prevailing pseudo-shock models in the literature is analyzed with the support of the available flow measurements. None are found to model both the pressure and streamwise Mach number profile of the UMDCI pseudo-shock. Note: This presentation is accompanied by the video presentation that can be downloaded for viewing and is formatted as an mp4 with a runtime of 10 min 15 secs.

Hypersonics↗

DOE OSTI2021

Cybersecurity Training Curriculum Analysis through the application of Machine Learning Text Classification and Natural Language Processing

In the domain of cybersecurity, there are a wide variety of education and training courses offered by a variety of training providers (commercial vendors, governmental, and academic institutions). Unfortunately, a comprehensive cross-provider mapping of the body of course offerings does not currently exist. Furthermore, although individual providers have made efforts, an accepted cross-provider mapping of education and training courses (or categories) to an accepted framework of work-roles by cybersecurity work-role and competency level has not yet been developed. The inability to compare training courses by topic or cybersecurity work-role and the level of difficulty (competency level) affects all organizations in identifying and selecting potential training opportunities for their personnel performing cybersecurity duties. In this work, we report on our application of Machine Learning (ML) Text Classification and Natural Language Processing (NLP) methodologies to begin to create a process to organize an accurate and thorough catalog of course offerings by work-role and competency level. Through the analysis of text-based course attributes (description, learning objectives, prerequisites, etc.) along with the text-based attributes for identified cybersecurity work-roles, our ML efforts examined methods to determine the work-role "best-fit" for each course as well as the competency level through action-verb text comparison.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

3D Virtual Simulation for Radiation Safety and Survey Training

3D virtual technologies have been widely used in remote training. Training integrated with 3D visualization technologies can enhance students’ engagement and reduce cost. The Applied Visualization Lab collaborates with College of Eastern Idaho on creating a 3D desktop application that simulate a pipe environment for radiation safety and survey training. Students can learn to perform radiation and contamination surveys remotely on their desktop. This simulation provides random scenarios, guided instructions, user interactions, and visual and sound feedback. It will promote utilizing virtual training for education outreach and minimize radiation and contamination exposure during the training.

3D↗

DOE OSTI2022

A Subcritical Assembly for Training and Education Use at the Oak Ridge National Laboratory

A near-final design has been completed for a new subcritical assembly at Oak Ridge National Laboratory (ORNL). This subcritical assembly will support the Nuclear Criticality Safety Program (NCSP) training and education program to augment current subcritical assembly capabilities at Sandia National Laboratories (SNL) and at the National Criticality Experiments Research Center (NCERC). Other collaborations have been proposed with the Sunrise Consortium and others. This proposed subcritical assembly uses legacy AGN-201M research reactor fuel plates that are available from the Y-12 National Security Complex. This subcritical assembly contains approximately 620 grams of 235 U as UO 2 particulates distributed homogeneously in radiation-stabilized polyethylene. The fuel plates will have a graphite neutron reflector to obtain a core multiplication, M, from 10 to 50, corresponding to a k eff of 0.9 to 0.95, respectively. The subcritical assembly will be able to support at least four experiments for the training courses: (1) the addition of fissile material to increase neutron multiplication (mass), (2) a core separation experiment (interaction), (3) the effect of adding moderators to the core (moderation), and (4) the effect of adding neutron absorbers to the core (poison/absorption). The proposed ORNL subcritical assembly will provide additional student bandwidth to support NCSP training courses and to provide for some backup capacity if nuclear facility operations are disrupted at SNL or NCERC due to some unforeseen issue. The subcritical assembly will be designed to be an inherently safe subcritical assembly—subcritical under all normal and abnormal conditions—and it will provide the capability to conduct hands-on training to support NCSP and general nuclear criticality safety (NCS) staff training and qualification goals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Offshore Wind Workforce Safety Standards and Training Resource

The standardization of safety training requirements for people working at sea to build and operate offshore wind energy projects, adoption of those standards, and alignment with offshore safety training curriculum development was identified as a high-priority workforce gap in the U.S. Offshore Wind Workforce Assessment (Stefek, 2022). Safety is a top priority for the offshore wind industry; however, there has been uncertainty surrounding what unique safety standards and training will be needed and how various offshore wind safety stakeholders will be involved. The scope of safety training for this resource includes workers who build and operate offshore wind energy projects at sea and defines the stakeholders in the complex ecosystem. This fact sheet aims to help reduce these uncertainties by: 1) Delineating jurisdictional lines, while clarifying roles and responsibilities amongst stakeholders; 2) Identifying key actions or partnerships that can encourage further success with safety training and workforce development.

offshore wind↗

DOE OSTI2021

3D Virtual Simulation for Radiation Safety and Survey Training

3D↗

DOE OSTI2021

Scalable balanced training of conditional generative adversarial neural networks on image data

Here, we propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

QUBO formulations for training machine learning models

Abstract Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

97 MATHEMATICS AND COMPUTING↗

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 OSTI2022

Training neural networks using Metropolis Monte Carlo and an adaptive variant

Abstract We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis MC can train a neural net with an accuracy comparable to that of gradient descent (GD), if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network’s structure or neuron activations are strongly heterogenous, and we introduce an adaptive Monte Carlo algorithm (aMC) to overcome these limitations. The intrinsic stochasticity and numerical stability of the MC method allow aMC to train deep neural networks and recurrent neural networks in which the gradient is too small or too large to allow training by GD. MC methods offer a complement to gradient-based methods for training neural networks, allowing access to a distinct set of network architectures and principles.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Training Spiking Neural Networks Using Combined Learning Approaches

Spiking neural networks (SNNs), the class of neural networks used in neuromorphic computing, are difficult to train using traditional back-propagation techniques. Spike timingdependent plasticity (STDP) is a biologically inspired learning mechanism that can be used to train SNNs. Evolutionary algorithms have also been demonstrated as a method for training SNNs. In this work, we explore the relationship between these two training methodologies. We evaluate STDP and evolutionary optimization as standalone methods for training networks, and also evaluate a combined approach where STDP weight updates are applied within an evolutionary algorithm. We also apply Bayesian hyperparameter optimization as a meta learner for each of the algorithms. We find that STDP by itself is not an ideal learning rule for randomly connected networks, while the inclusion of STDP within an evolutionary algorithm leads to similar performance, with a few interesting differences. This study suggests future work in understanding the relationship between network topology and learning rules.

Elbrecht, Daniel↗

DOE OSTI2020

Layer-Parallel Training of Deep Residual Neural Networks

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Finally, using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

97 MATHEMATICS AND COMPUTING↗