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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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All sourcesNASA NTRSDOE OSTI
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 NTRS2000

Differences in Acceleration Training and Exercise Training on Resting Cardiovascular Variables

The relative effects of alternating exercise vs. acclamation training an mean blood pressure (BP, Finapres), cardiac output (CO, BoMed) and peripheral resistance (PR, calculated) were evaluated. Six healthy men (33$\pm$(SD)6 yr. 178$\pm$4 cm, 86$\pm$6 kg) underwent exercise training (ET, n=3): supine on a cycle ergometer (40 to 90\% Vo$_{2}$ max) during exposure to constant+1G$_{z}$ for $\sim$30 min/day for 14 days on NASA's 1.9m Human Powered Centrifuge (HPC). They also underwent oscillatory (between +1 G$ {z}$and$\sim$2.5G$_{z}$) acceleration training (AT, n=3) for $\sim$30 min/day for 14 days on the HPC. After four weeks of ambulatory deconditioning, training protocols were switched. AT increased resting CO by 9.MpmS(SE)3.2\% (p$less than$0.05) with no effect on BF, and ET decreased BP by 9.2$\pm$4.6\% (p$less than$0.08) as well as spectral power of PR by 41$\pm$9\% (p$less than$0.05). The major effect of acceleration training was to increase resting cardiac output while that of exercise mining was to decrease resting blood pressure.

Evans, J. M.↗
NASA NTRS2002

Exercise Versus +Gz Acceleration Training

Decreased working capacity and "orthostatic" intolerance are two major problems for astronauts during and after landing from spaceflight in a return vehicle. The purpose was to test the hypotheses that (1) supine-passive-acceleration training, supine-interval-exercise plus acceleration training, and supine exercise plus acceleration training will improve orthostatic tolerance (OT) in ambulatory men; and that (2) addition of aerobic exercise conditioning will not influence this enhanced OT from that of passive-acceleration training. Seven untrained men (24-38 yr) underwent 3 training regimens (30 min/d x 5d/wk x 3wk on the human-powered centrifuge - HPC): (a) Passive acceleration (alternating +1.0 Gz to 50% Gzmax); (b) Exercise acceleration (alternating 40% - 90% V02max leg cycle exercise plus 50% of HPCmax acceleration); and (c) Combined intermittent exercise-acceleration at 40% to 90% HPCmax. Maximal supine exercise workloads increased (P < 0.05) by 8.3% with Passive, by 12.6% with Exercise, and by 15.4% with Combined; but maximal V02 and HR were unchanged in all groups. Maximal endurance (time to cessation) was unchanged with Passive, but increased (P < 0.05) with Exercise and Combined. Resting pre-tilt HR was elevated by 12.9% (P < 0.05) only after Passive training, suggesting that exercise training attenuated this HR response. All resting pre-tilt blood pressures (SBP, DBP, MAP) were not different pre- vs. post-training. Post-training tilt-tolerance time and HR were increased (P < 0.05) only with Passive training by 37.8% and by 29.1%, respectively. Thus, addition of exercise training attenuated the increased Passive tilt tolerance. Resting (pre-tilt) and post-tilt cardiac R-R interval, stroke volume, end-diastolic volume, and cardiac output were all uniformly reduced (P < 0.05) while peripheral resistance was uniformly increased (P < 0.05) pre-and post-training for the three regimens indicating no effect of any training regimen on those cardiovascular variables. Plasma volume (% delta) was uniformly decreased by 8% to 14% (P < 0.05) at tilt-tolerance pre- vs. post-training for all regimens indicating no effect of these training regimens on the level of vascular fluid shifts.

Greenleaf, John E.↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗