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NASA NTRS2016

Analysis of Potential Glove-Induced Hand Injury Metrics During Typical Neutral Buoyancy Training Operations

Injuries to the hands are common among astronauts who train for extravehicular activity. When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. A brief review of NASA's Lifetime Surveillance of Astronaut Health's injury database reveals that 76 percent of astronaut hand and arm injuries from training between 1993 and 2010 occurred either to the fingernail, finger crotch, metacarpophalangeal joint, or fingertip. The purpose of this study was to assess the potential of using small sensors to measure forces acting on the fingers and hand within pressurized gloves and other variables such as skin temperature, humidity, and fingernail strain of a NASA crewmember during typical NBL (Neutral Buoyancy Laboratory) training activity. During the 5-hour exercise, the crewmember seemed to exhibit very large forces on some fingers, resulting in higher strain than seen in previous glove-box testing. In addition, vital information was collected on the glove cavity environment with respect to temperature and humidity. All of this information gathered during testing will be carried forward into future testing, potentially in glove-box- or 1G- (1 gravitational force) or NBL-suited environments, to better characterize and understand the possible causes of hand injury amongst NASA crew.

McFarland, Shane↗

NASA NTRS2014

Structural Analysis Using NX Nastran 9.0

NX Nastran is a powerful Finite Element Analysis (FEA) software package used to solve linear and non-linear models for structural and thermal systems. The software, which consists of both a solver and user interface, breaks down analysis into four files, each of which are important to the end results of the analysis. The software offers capabilities for a variety of types of analysis, and also contains a respectable modeling program. Over the course of ten weeks, I was trained to effectively implement NX Nastran into structural analysis and refinement for parts of two missions at NASA's Kennedy Space Center, the Restore mission and the Orion mission.

NX Nastran↗

NASA NTRS2004

EVA Performance Prediction

Astronaut physical performance capabilities in micro gravity EV A or on planetary surfaces when encumbered by a life support suit and debilitated by a long exposure to micro gravity will be less than unencumbered pre flight capabilities. The big question addressed by human factors engineers is: what can the astronaut be expected to do on EVA or when we arrive at a planetary surface? A second question is: what aids to performance will be needed to enhance the human physical capability? These questions are important for a number of reasons. First it is necessary to carry out accurate planning of human physical demands to ensure that time and energy critical tasks can be carried out with confidence. Second it is important that the crew members (and their ground or planetary base monitors) have a realistic picture of their own capabilities, as excessive fatigue can lead to catastrophic failure. Third it is important to design appropriate equipment to enhance human sensory capabilities, locomotion, materials handling and manipulation. The evidence from physiological research points to musculoskeletal, cardiovascular and neurovestibular degradation during long duration exposure to micro gravity . The evidence from the biomechanics laboratory (and the Neutral Buoyancy Laboratory) points to a reduction in range of motion, strength and stamina when encumbered by a pressurized suit. The evidence from a long history of EVAs is that crewmembers are indeed restricted in their physical capabilities. There is a wealth of evidence in the literature on the causes and effects of degraded human performance in the laboratory, in sports and athletics, in industry and in other physically demanding jobs. One approach to this challenge is through biomechanical and performance modeling. Such models must be based on thorough task analysis, reliable human performance data from controlled studies, and functional extrapolations validated in analog contexts. The task analyses currently carried out for EVA activities are based more on extensive domain experience than any formal analytic structure. Conversely, physical task analysis for industrial and structured evidence from training and EV A contexts. Again on earth there is considerable evidence of human performance degradation due to encumbrance and fatigue. These industrial models generally take the form of a discounting equation. The development of performance estimates for space operations, such as timeline predictions for EVA is generally based on specific input from training activity, for example in the NBL or KC135. uniformed services tasks on earth are much more formalized. Human performance data in the space context has two sources: first there is the micro analysis of performance in structured tasks by the space physiology community and second there is the less structured evidence from training and EV A contexts.

Peacock, Brian↗

NASA NTRS1984

Dynamics of early planetary gear trains

A method to analyze the static and dynamic loads in a planetary gear train was developed. A variable-variable mesh stiffness (VVMS) model was used to simulate the external and internal spur gear mesh behavior, and an equivalent conventional gear train concept was adapted for the dynamic studies. The analysis can be applied either involute or noninvolute spur gearing. By utilizing the equivalent gear train concept, the developed method may be extended for use for all types of epicyclic gearing. The method is incorporated into a computer program so that the static and dynamic behavior of individual components can be examined. Items considered in the analysis are: (1) static and dynamic load sharing among the planets; (2) floating or fixed Sun gear; (3) actual tooth geometry, including errors and modifications; (4) positioning errors of the planet gears; (5) torque variations due to noninvolute gear action. A mathematical model comprised of power source, load, and planetary transmission is used to determine the instantaneous loads to which the components are subjected. It considers fluctuating output torque, elastic behavior in the system, and loss of contact between gear teeth. The dynamic model has nine degrees of freedom resulting in a set of simultaneous second order differential equations with time varying coefficients, which are solved numerically. The computer program was used to determine the effect of manufacturing errors, damping and component stiffness, and transmitted load on dynamic behavior. It is indicated that this methodology offers the designer/analyst a comprehensive tool with which planetary drives may be quickly and effectively evaluated.

August, R.↗

NASA NTRS2013

Investigation of Control Inceptor Dynamics and Effect on Human Subject Performance

The control inceptor used in a vehicle simulation is an important part of adequately representing the dynamics of the vehicle. The inceptor characteristics are typically based on a second order spring mass damper system with damping, force gradient, breakout force, and natural frequency parameters. Changing these parameters can have a great effect on pilot control of the vehicle. A quasi transfer of training experiment was performed employing a high fidelity and a low fidelity control inceptor. A disturbance compensatory task was employed which involved a simple horizon line disturbed in roll by a sum of sinusoids presented in an out-the-window display. Vehicle dynamics were modeled as 1/s and 1/s2. The task was to maintain level flight. Twenty subjects were divided between the high and the low fidelity training groups. Each group was trained to a performance asymptote, and then transferred to the high fidelity simulation. RMS tracking error, a PSD analysis, and a workload analysis were performed to quantify the transfer of training effect. Quantitative results of the experiments show that there is no significant difference between the high and low fidelity training groups for 1/s plant dynamics. For 1/s2 plant dynamics there is a greater difference in tracking performance and PSD; and the subjects are less correlated with the input disturbance function

Stanco, Anthony A.↗

NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Bauer, M. E.↗

NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗

NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗

NASA NTRS2006

Imagine Moving Off the Planet

Moving off the planet will be a defining moment of this century as landing on the Moon was in the last. For that to happen for humans to go where humans cannot go-- simulation is the sole solution. NASA supports simulation for life-cycle activities: design, analysis, test, checkout, operations, review and training. We contemplate time spans of a century and more, teams dispersed to different planets and the need for systems that endure or adapt as missions, teams and technology change. Without imagination such goals are impossible. But with imagination we can go outside our present perception of reality to think about and take action on what has been, is and, especially, what might be. Consciously maturing an imagined, possibly workable, idea through framing it to optimization to design, and building the product provides us with a new approach to innovation and simulation fidelity. We address options, analyze, test and make improvements in how we think and work. Each step includes increasingly exact information about costs, schedule, who will be needed, where, when and how. NASA i integrating such thinking into its Exploration Product Realization Hierarchy for simulation and analysis, test and verification, and stimulus response goals. Technically NASA follows a timeline of studies, analysis, definition, design, development and operations with concurrent documentation. We have matched this Product Realization Hierarchy with a continuum from image to realization that incorporates commitment, current and needed research and communication to ensure superior and creative problem solving as well as advances in simulation. One result is a new approach to collaborative systems. Another is a distributed observer network prototyped using game engine technology bringing advanced 3-D simulation of a simulation to the desktop enabling people to develop shared consensus of its meaning. Much of the value of simulation comes from developing in people their ability to make good decisions and reflexes supporting impressive achievement. Synthesizing imagination systematically into our work - and thus our success - is a challenge. NASA engineers have inventive minds, and the task is determining how best to enable them to devise the simulation and other innovations that will make a story so clear and so intellectually sound that people can carry out the mission for 50-100 years. This demands skills and knowledge traditionally under-respected and under-represented in technology organizations. But we are beginning to see that the process encourages efficiency and enables us to attain more effective results. We have to elicit imaginative, intelligent and effective ways to make better use than ever of the minds we have and will have available. We have to accept the challenge to accomplish tasks among dispersed interdisciplinary teams who must overcome changing priorities and technology, time and distance in order to maximize interactivity and innovation as never before. Attention to the process of innovation is a practical means to increase the efficiency of our intelligence. We have an obligation to reexamine and improve the process by which we approach and exercise innovation as we accept the charge to move off the planet.

Elfrey, Priscilla R.↗

NASA NTRS1934

A flight investigation of the spinning of the NY-1 airplane with varied mass distribution and other modifications, and an analysis based on wind-tunnel tests

This report presents the results of an investigation of the spinning characteristics of NY-1 naval training biplane. The results of flight tests and an analysis based on wind-tunnel test data are given and compared. The primary purpose of the investigation was the determination in flight of the effect of changes in mass distribution along the longitudinal axis, without change of mass quantity or centroid. Other effects were also investigated, such as those due to wing loading, center-of-gravity position, dihedral of wings, control setting, and the removal of a large portion of the fabric from the fin and rudder. The wind tunnel test results used in the numerical analysis were obtained in the 7 by 10 foot wind tunnel through an angle-of-attack.

Scudder, Nathan F↗

NASA NTRS1990

Establishment of a strain analysis capability using photoelastic coatings

In accordance with the Research Plan prepared at the beginning of the Fellowship Program, the summer activities consisted of: training the personnel of the Structural Test Division of the Structures and Dynamics Laboratory in the theory and practice of strain analysis using photoelastic coatings; and performing strain analysis using photoelastic coatings on appropriate test articles. In support of these activities, the following actions were taken: (1) equipment and supplies necessary for strain analysis using photoelastic coatings were specified, purchased, and checked out; (2) four engineers were trained in the theory and practice of strain analysis using photoelastic coatings; (3) four technicians were trained in the practice of preparing and applying photoelastic coatings to both curved and flat surfaces; (4) in addition to the final program seminar, three seminars on the fundamentals and use of photoelastic coatings were presented to a total of 43 members of the various laboratories at MSFC; (5) a photoelastic coating was applied to and used in a test of a thrust vector control corner section; (6) to further assist the engineers with the use and understanding of photoelastic coatings, fifteen journal articles were located and copied, and camera settings for photographic fringe patterns were determined and recorded; and (7) two proposals for providing technical assistance in strain analysis at MSFC and testing of selected components/assemblies at the University of Alabama in Tuscaloosa were written for submission to NASA.

Gambrell, Samuel C., Jr.↗

NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗

NASA NTRS1981

The evaluation of alternate methodologies for land cover classification in an urbanizing area

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Smekofski, R. M.↗

NASA NTRS2006

A-Train Data Depot: Integrating and Visualizing Atmospheric Measurements Along the A-Train Tracks

The succession of US and international satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000) The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot, to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The A-Train Data Depot (ATDD) will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth s Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archwing for fast data access, and Cooperative Institute for Research in the Atmosphere (CRA) data co-registration tools. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be followed by associated measurements from MLS, OMI, HIRDLS, and TES. Given the independent nature of instrument/platform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be discussed. The project s resulting visualizations and analysis illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗

NASA NTRS2006

A-Train Data Depot: Integrating, Visualizing, and Extracting Cloudsat, CALIPSO, MODIS, and AIRS Atmospheric Measurements Along the A-Train Tracks

The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗

NASA NTRS2003

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Hopkins, Dale A.↗

NASA NTRS1991

Pilot personality and crew coordination - Implications for training and selection

It is contended that past failures to find linkages between performance and personality were due to a combination of premature performance evaluation, inadequate statistical modeling, and/or the reliance on data gathered in contrived as opposed to realistic situations. The goal of the research presented is to isolate subgroups of pilots along performance-related personality dimensions and to document limits on the impact of crew coordination training between the groups. Three different profiles were identified through cluster analysis of personality scales that replicated across samples and predicted attitude change following training in crew coordination.

Chidester, Thomas R.↗

NASA NTRS1941

Air Transport by Gliders : Some Technical Observations

This short analysis may be useful in determining the real tactical possibilities of "glider trains" and in adopting the course to be followed in possible studies of these questions. In this analysis most prominent are: (a) the power required for the train in level flight; (b) its speed; (c) climb; and (d) the type of airplane best suited for towing as well as design requirements for transport gliders.

Stepniewski, Wieslaw↗