SEARCH · Engineering Papers

Results for “Training Time”

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 163 records · Page 9

All sources NASA NTRS DOE OSTI

NASA NTRS1984

A new class of solar burst with MM-wave emission but only at the highest frequency (90 GHz)

High sensitivity and high time resolution solar observations at 90 GHz (lambda = 3.3 mm) have identified a unique impulsive burst on May 21, 1984 with emission that was more intense at this frequency than at lower frequencies. The first major time structure of the burst was over 10 times more intense at 90 GHz than at 30 GHz, 7 GHz, or 2.8 GHz.Only 6 seconds later, the 30 GHz impulsive structures started to be observed but still with lower intensity than at 90 GHz. Hard X-ray time structures at energies above 25 keV were almost identical to the 90 GHZ structures (to better than one second). All 90 GHz major time structures consisted of trains of multiple subsecond pulses with rise times as short as 0.03 sec and amplitudes large compared to the mean flux. When detectable, the 30 GHz subsecond pulses had smaller relative amplitude and were in phase with the corresponding 90 GHz pulses.

Kaufmann, P.↗

NASA NTRS1985

Solar burst with millimetre-wave emission at high frequency only

The first high sensitivity and high time-resolution observations of a solar burst taken simultaneously at 90 GHz and at 30 GHz are presented. These identify a unique impulsive burst on May 21, 1984 with fast pulsed emission that was considerably more intense at 90 GHz than at lower frequencies. Hard X-ray time structures at energies above 25 keV were almost identical to the 90 GHz structures to better than 1 s. The structure of the onset of the major 90 GHz burst coincided with the hard X-ray structure to within 128 ms. All 90 GHz major time structures consisted of trains of multiple subsecond pulses with rise times as short as 0.03 s and amplitudes that were large compared with the mean flux. When detectable, the 30 GHz subsecond pulses had smaller relative amplitude and were in phase with the corresponding 90 GHz pulses.

Kaufmann, P.↗

NASA NTRS1992

Carotid-cardiac baroreflex response and LBNP tolerance following resistance training

The purpose of this study was to examine the effect of lower body resistance training on cardiovascular control mechanisms and blood pressure maintenance during an orthostatic challenge. Lower body negative pressure (LBNP) tolerance, carotid-cardiac baroreflex function (using neck chamber pressure), and calf compliance were measured in eight healthy males before and after 19 wk of knee extension and leg press training. Resistance training sessions consisted of four or five sets of 6-12 repetitions of each exercise, performed two times per week. Training increased strength 25 +/- 3 (SE) percent (P = 0.0003) and 31 +/- 6 percent (P = 0.0004), respectively, for the leg press and knee extension exercises. Average fiber size in biopsy samples of m. vastus lateralis increased 21 +/- 5 percent (P = 0.0014). Resistance training had no significant effect on LBNP tolerance. However, calf compliance decreased in five of the seven subjects measured, with the group average changing from 4.4 +/- 0.6 ml.mm Hg-1 to 3.9 +/- 0.3 ml.mm Hg-1 (P = 0.3826). The stimulus-response relationship of the carotid-cardiac baroreflex response shifted to the left on the carotid pressure axis as indicated by a reduction of 6 mm Hg in baseline systolic blood pressure (P = 0.0471). In addition, maximum slope increased from 5.4 +/- 1.3 ms.mm Hg-1 before training to 6.6 +/- 1.6 ms.mm Hg-1 after training (P = 0.0141). Our results suggest the possibility that high resistance, lower extremity exercise training can cause a chronic increase in sensitivity and resetting of the carotid-cardiac baroreflex.

Tatro, D. L.↗

NASA NTRSDate not supplied

Isolator Shock Train Response to Dual-Mode Scramjet Throttling-Like Forcing

Typical dual-mode scramjet designs employ an isolator component situated between the inlet and combustor. The isolator fluidically buffers the inlet from the combustor via the formation of a shock train, an interconnected series of shock-boundary layer interactions. The isolator is required during ramjet-engine operation and at the lower edges of the scramjet operational envelope. In an operational engine, transient behavior imposes fluidic dynamics due to attitude adjustments, combustor throttling, aberrant resonances within the combustor, etc. To accommodate these transients, the shock train adjusts on both temporal and spatial scales. Sufficiently large downstream pressure increases in the combustor play a critical role during unstart, whereby strong compression waves propagate upstream in the isolator to ultimately force the shock train out the inlet, if it cannot stabilize to the pressure increase. This critically reduces the engine mass flow rate, potentially causing a catastrophic reduction in thrust and loss of the vehicle. A nearly instantaneous unstart can, in principle, be predicted by a ratio of the normal shock pressure jump in an inviscid isolator. However, lower-magnitude perturbations, below this bound, also induce unstarts. Understanding the transient behavior inherent in unsteady isolator operation is critical to reducing safety margin in isolator length and refining engine control algorithms to improve engine performance. The Isolator Dynamics Research Lab (IDRL) is a cold-flow, direct-connect isolator facility ideal for assessing the fundamental fluid dynamics of shock trains. The internal flow of a scramjet isolator is modeled using a 2D, symmetric Mach 2.5 nozzle followed by a 695 mm long, 25.4x50.8 mm rectangular isolator test section. A blockage cone on a high-speed ball screw actuator downstream of the isolator is pulsed to simulate the throttling behavior in an operational combustor. Wall flush high-speed static pressure transducers detect the streamwise shock train within the isolator. Time delays between the onset of the throttling and the response motion of the shock train are analyzed to assess the controlling scales and parameters. From this analysis, the response time of the isolator shock train system is found to depend primarily on the isolator back pressure time-rate-of-change but quickly saturates to a maximum propagation speed along the isolator. These results inform dual-mode scramjet engine designers of the critical parameters and limits of an unstart resistant engine.

scramjet↗

NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

Erika Munshi↗

NASA NTRSDate not supplied

A Strategy for Advancing Earth Independent Medical Operations

Exploration medical operations to the Moon and Mars present unprecedented challenges for providing in-mission medical care. The greater distance from Earth is the primary hazard that drives the need for a medical operations paradigm shift from low earth orbit mission. Increasingly complex long duration and long distance missions will have resource constraints (ex: mass, power, volume, data), a paucity of resupply or evacuation opportunities, and disruptions in real-time communications. In order to advance a more autonomous medical approach, a multi-faceted strategy will need to optimize all aspects of human health and performance in space. This strategy will include: increasing onboard medical autonomy through the development of novel crew health and performance systems; decision support capabilities to augment astronauts’ abilities in preventing, diagnosing, and treating medical conditions; creating new procedures and training tools for skill maintenance and just-in-time training; and enabling rapid crew access to data from all on-board systems, leading to better-informed, real-time, autonomous decisions. Collectively, this approach can be referred to as “Earth Independent Medical Operations” (EIMO). The Exploration Medical Capability Element of NASA’s Human Research Program has undertaken a longitudinal planning process to consensus around EIMO. Technical interchange meetings will occur with key constituents in late 2022 and again in early 2023 to define EIMO in practical terms and identify key elements of an EIMO system, with greater conceptual articulation and dissemination planned for the second half of 2023. We will share insights into the strategy for internal and external consensus building around EIMO, with a particular focus on the approach to partner stakeholders within NASA as well as commercial agencies.

Jay Lemery↗

NASA NTRS2000

Isometric handgrip training reduces arterial pressure at rest without changes in sympathetic nerve activity

The purpose of this study was to determine whether isometric handgrip (IHG) training reduces arterial pressure and whether reductions in muscle sympathetic nerve activity (MSNA) mediate this drop in arterial pressure. Normotensive subjects were assigned to training (n = 9), sham training (n = 7), or control (n = 8) groups. The training protocol consisted of four 3-min bouts of IHG exercise at 30% of maximal voluntary contraction (MVC) separated by 5-min rest periods. Training was performed four times per week for 5 wk. Subjects' resting arterial pressure and heart rate were measured three times on 3 consecutive days before and after training, with resting MSNA (peroneal nerve) recorded on the third day. Additionally, subjects performed IHG exercise at 30% of MVC to fatigue followed by muscle ischemia. In the trained group, resting diastolic (67 +/- 1 to 62 +/- 1 mmHg) and mean arterial pressure (86 +/- 1 to 82 +/- 1 mmHg) significantly decreased, whereas systolic arterial pressure (116 +/- 3 to 113 +/- 2 mmHg), heart rate (67 +/- 4 to 66 +/- 4 beats/min), and MSNA (14 +/- 2 to 15 +/- 2 bursts/min) did not significantly change following training. MSNA and cardiovascular responses to exercise and postexercise muscle ischemia were unchanged by training. There were no significant changes in any variables for the sham training and control groups. The results indicate that IHG training is an effective nonpharmacological intervention in lowering arterial pressure.

NASA Discipline Neuroscience↗

NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗

NASA NTRS1983

Human factors in Spacelab - Crew training

At NASA-Ames Research Center's Life Sciences Flight Experiments Project Office two payloads for the Shuttle Spacelab are currently in development. The first payload, Spacelab-3, will launch in November 1984. Unique life sciences hardware designed to support animals in 0-g will fly for the first time. Flight crew training sessions for the Spacelab-3 astronauts began in June 1982. Human factors involvement is extensive. A thorough understanding of both the 1-g and 0-g environments is necessary. The weightlessness of the space environment creates special conditions; e.g., the time required for a 1-g laboratory experiment significantly increases in 0-g. The transportation of objects in 0-g uses different techniques than on earth. These considerations, plus others, are incorporated into the design of the Spacelab-3 crew training program.

Junge, M. K.↗

NASA NTRS2015

Effects of False Tilt Cues on the Training of Manual Roll Control Skills

This paper describes a transfer-of-training study performed in the NASA Ames Vertica lMotion Simulator. The purpose of the study was to investigate the effect of false tilt cues on training and transfer of training of manual roll control skills. Of specific interest were the skills needed to control unstable roll dynamics of a mid-size transport aircraft close to the stall point. Nineteen general aviation pilots trained on a roll control task with one of three motion conditions: no motion, roll motion only, or reduced coordinated roll motion. All pilots transferred to full coordinated roll motion in the transfer session. A novel multimodal pilot model identification technique was successfully applied to characterize how pilots' use of visual and motion cues changed over the course of training and after transfer. Pilots who trained with uncoordinated roll motion had significantly higher performance during training and after transfer, even though they experienced the false tilt cues. Furthermore, pilot control behavior significantly changed during the two sessions, as indicated by increasing visual and motion gains, and decreasing lead time constants. Pilots training without motion showed higher learning rates after transfer to the full coordinated roll motion case.

simulators↗

NASA NTRS1996

Higher-Order Neural Networks Recognize Patterns

Networks of higher order have enhanced capabilities to distinguish between different two-dimensional patterns and to recognize those patterns. Also enhanced capabilities to "learn" patterns to be recognized: "trained" with far fewer examples and, therefore, in less time than necessary to train comparable first-order neural networks.

Reid, Max B.↗

NASA NTRS2011

Theoretically-Driven Infrastructure for Supporting Healthcare Teams Training at a Military Treatment Facility

The Team Resource Center (TRC) at Naval Medical Center Portsmouth (NMCP) currently hosts a tri-service healthcare teams training course three times annually . The course consists of didactic learning coupled with simulation exercises to provide an interactive educational experience for healthcare professionals. The course is also the foundation of a research program designed to explore the use of simulation technologies for enhancing team training and evaluation. The TRC has adopted theoretical frameworks for evaluating training readiness and efficacy, and is using these frameworks to guide a systematic reconfiguration of the infrastructure supporting healthcare teams training and research initiatives at NMCP.

Turner, Robert T.↗

NASA NTRS2009

Time-to-Fatigue and Intramuscular pH Measured via NIRS During Handgrip Exercise in Trained and Sedentary Individuals

In exercising muscles force production and muscular endurance are impaired by a decrease in intramuscular pH. The effects of aerobic training (AT) on preventing acidosis and prolonging exercise time in muscles not specifically targeted by the training are unknown. Purpose: To compare interstitial pH, measured non-invasively with near infrared spectroscopy (NIRS), in the flexor digitorum profundus (FDP) during rhythmic handgrip exercise in sedentary subjects and those who participate in AT activities that target the lower body. Methods: Maximal isometric force (MIF) was measured on three separate days in AT (n=5) and sedentary (n=8) subjects using a handgrip dynamometer (HGD). Isometric muscular endurance (IME) was measured during five trials, each separated by at least 48 hrs. For each IME trial subjects rhythmically squeezed (4 sec at 40% of MVC) and relaxed (2 sec) to fatigue or failure to reach the target force in three consecutive contractions or four non-consecutive contractions. Interstitial pH was derived from spectra collected using a NIRS sensor adhered to the skin over the FDP. The first four IME trials served to familiarize subjects with the protocol; the fifth trial was used for analysis. NIRS-derived pH was averaged in 30 sec increments. Between group differences in MIF and exercise time were tested using paired t-tests. A repeated measures ANOVA was used to analyze effects of AT and exercise time on pH. Results: MIF was not different between groups (mean SD; aerobic=415.6 95.4 N vs. sedentary =505.1 107.4 N). Time to fatigue was greater in the AT than in the sedentary group (mean SD: 611 173 sec vs. 377 162 sec, p<0.05). pH was not different between groups at any time point. Average pH decreased (p<0.05) in both groups from rest (pH=7.4) through 90 sec of exercise (pH=6.9), but did not decrease further throughout the remainder of exercise. Conclusion: Although between group differences in pH were not detected, differences during the onset of exercise may exist with a more frequent sampling. AT individuals appear to better tolerate decreased interstitial pH and are able to continue submaximal muscular work, possibly due to psychological familiarization to muscular fatigue and/or systemic physiological benefits.

Everett, M. E.↗

NASA NTRS2016

Patterns of Activity in A Global Model of A Solar Active Region

In this work we investigate the global activity patterns predicted from a model active region heated by distributions of nanoflares that have a range of frequencies. What differs is the average frequency of the distributions. The activity patterns are manifested in time lag maps of narrow-band instrument channel pairs. We combine hydrodynamic and forward modeling codes with a magnetic field extrapolation to create a model active region and apply the time lag method to synthetic observations. Our aim is not to reproduce a particular set of observations in detail, but to recover some typical properties and patterns observed in active regions. Our key findings are the following. (1) Cooling dominates the time lag signature and the time lags between the channel pairs are generally consistent with observed values. (2) Shorter coronal loops in the core cool more quickly than longer loops at the periphery. (3) All channel pairs show zero time lag when the line of sight passes through coronal loop footpoints. (4) There is strong evidence that plasma must be re-energized on a timescale comparable to the cooling timescale to reproduce the observed coronal activity, but it is likely that a relatively broad spectrum of heating frequencies are operating across active regions. (5) Due to their highly dynamic nature, we find nanoflare trains produce zero time lags along entire flux tubes in our model active region that are seen between the same channel pairs in observed active regions.

Bradshaw, S. J.↗

NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle S.↗

NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Bosson, Christabelle↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗