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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↗

NASA NTRS2010

The Fate of Saharan Dust Across the Atlantic: An Integrated Modeling and Observational Study of the TC4 Field Campaign

During the NASA TC-4 field campaign in July 2007, several Saharan dust events were observed over the Caribbean basin. A-Train observations suggest that these Saharan dust events are confined the Caribbean and rarely transported across Central America to the Pacific Ocean. We investigate the nature of this barrier to dust transport using the NASA GEOS-5 atmospheric general circulation model. Our simulations with GEOS-5 are driven by the Modern Era Retrospective-Analysis for Research and Applications (MERRA) meteorological analyses, and include online simulation of aerosol distributions using a version of the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model. Simulated dust distributions are evaluated using A-Train observations from MODIS and CALIOP, as well as MISR and ground-based AERONET sun photometers, and show good agreement with the observations in terms of the timing and magnitude of dust events. A component analysis of the dust transport and removal pathways is used to evaluate the relative roles of these processes in establishing the observed dust transport barrier. From this analysis, we show that while both atmospheric dynamics and wet removal contribute towards the Caribbean dust barrier, northward dust transport is the more dominant term. Additional simulations are performed to ascertain the sensitivity of our results to uncertain loss processes (i.e., wet removal) in our model.

Nowottnick, E.↗

NASA NTRS1998

Secondary Fragmentation of Comet Shoemaker-Levy 9 and the Ramifications for the Progenitor's Breakup in July 1992

Comprehensive analysis of discrete events of secondary fragmentation leads to a conceptually new understanding of the process of disintegration of comet Shoemaker-Levy 9. We submit that the jovian tidal forces inflicted extensive cracks throughout the interior of the original nucleus but did not split it apart. The initial disruption was apparently accomplished by stresses exerted on the cracked object by its fast rotation during the early post-perijove period of time. We argue that this disruption was in fact a rapid sequence of episodes during July 1992 that gave birth to the 12 on-train, or primary, fragments: A, C, D, E, G, H, K, L, Q (later Q(sub 1)), R, S, and W. The discrete events of secondary fragmentation, which gave birth to the off-train fragments, are understood in this scenario as stochastic manifestations of the continuing process of progressive disintegration. Of the 13 off-train fragments considered, nine were secondary--B, F, G(sub 2), M, N, P (later P(sub 2) or P(sub 2a)), Q(sub 2), U, and V--and four tertiary (J, P(sub 1), P(sub 2b), and T). The separation parameters of 11 off-train fragments were determined. The vectorial distribution of separation velocities of these fragments shows a strong concentration toward a great circle, unquestionably an effect of the approximately conserved angular momentum of the progenitor comet since the time of its initial disruption. Also apparent is their clumping (except for P(sub 1)) to a segment along the great circle, implying that the fragments were consistently released from one side of their parents, thus explaining for the first time why the off-train fragments preferentially appeared on one side of the nuclear train. In order to obtain a consistent solution, our model requires that the points of separation be on the antisolar side of the parent fragments, where thermal stresses are likely to enhance the effect of rotation. The episodes of secondary fragmentation are found to have nine months after the close encounter with Jupiter in early July 1992, and the separation velocities ranged between 0.36 and 1.7 m/s. The spin-axis position is determined to have been nearly in the jovicentric orbit plane, which rules out the Asphaug-Benz-Solem strengthless aggregate model as a plausible breakup hypothesis. Since the separation velocities are rotational in nature, they cannot substantially exceed the critical limit for centrifugal breakup and offer an estimate for the original nuclear dimensions. The comet's nucleus is found to have been approximately 10 km in diameter and spinning rapidly. With the exception of P(sub 1) and apparently also P(sub 2) and F, no nongravitational deceleration was detected in the motions of the off-train fragments. Serious doubts are cast on continuing appreciable activity of any of these fragments. Indeed, when it was necessary to introduce a deceleration into the equations of motion, the effect appears to have been due to the action of solar radiation pressure on the centroid of centimeter-sized particulates in the disintegrating condensations.

Sekanina, Z.↗

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 NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗

NASA NTRS1992

Elastomeric load sharing device

An elastomeric load sharing device, interposed in combination between a driven gear and a central drive shaft to facilitate balanced torque distribution in split power transmission systems, includes a cylindrical elastomeric bearing and a plurality of elastomeric bearing pads. The elastomeric bearing and bearing pads comprise one or more layers, each layer including an elastomer having a metal backing strip secured thereto. The elastomeric bearing is configured to have a high radial stiffness and a low torsional stiffness and is operative to radially center the driven gear and to minimize torque transfer through the elastomeric bearing. The bearing pads are configured to have a low radial and torsional stiffness and a high axial stiffness and are operative to compressively transmit torque from the driven gear to the drive shaft. The elastomeric load sharing device has spring rates that compensate for mechanical deviations in the gear train assembly to provide balanced torque distribution between complementary load paths of split power transmission systems.

Isabelle, Charles J.↗

NASA NTRS2025

An Efficient GPU-Accelerated Multi-Source Global Fit Pipeline for LISA Data Analysis

The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog ∼12000 Galactic Binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

LISA global fit↗

NASA NTRS2015

High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort

Human hands play a significant role during Extravehicular Activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. Because of this high frequency usage, hand and arm related injuries are known to occur during EVA and EVA training in the NBL. The primary objectives of this investigation were to: 1) document all known EVA glove related injuries and circumstances of these incidents, 2) determine likely risk factors, and 3) recommend interventions where possible that could be implemented in the current and future glove designs. METHODS: The investigation focused on the discomforts and injuries of U.S. crewmembers who had worn the pressurized Extravehicular Mobility Unit (EMU) spacesuit and experienced 4000 Series or Phase VI glove related incidents during 1981 to 2010 for either EVA ground training or in-orbit flight. We conducted an observational retrospective case-control investigation using 1) a literature review of known injuries, 2) data mining of crew injury, glove sizing, and hand anthropometry databases, 3) descriptive statistical analyses, and finally 4) statistical risk correlation and predictor analyses to better understand injury prevalence and potential causation. Specific predictor statistical analyses included use of principal component analyses (PCA), multiple logistic regression, and survival analyses (Cox proportional hazards regression). Results of these analyses were computed risk variables in the forms of odds ratios (likelihood of an injury occurring given the magnitude of a risk variable) and hazard ratios (likelihood of time to injury occurrence). Due to the exploratory nature of this investigation, we selected predictor variables significant at p≤0.15. RESULTS: Through 2010, there have been a total of 330 NASA crewmembers, from which 96 crewmembers performed 322 EVAs during 1981-2010, resulting in 50 crewmembers being injured inflight and 44 injured during 11,704 ground EVA training events. Of the 196 glove related injury incidents, 106 related to EVA and 90 to EVA training. Over these 196 incidents, 277 total injuries (126 flight; 151 training) were reported and were then grouped into 23 types of injuries. Of EVA flight injuries, 65% were commonly reported to the hand (in general), metacarpophalangeal (MCP) joint, and finger (not including thumb) with fatigue, abrasion, and paresthesia being the most common injury types (44% of total flight injuries). Training injuries totaled to more than 70% being distributed to the fingernail, MCP joint, and finger crotch with 88% of the specific injuries listed as pain, erythema, and onycholysis. Of these training injuries, when reporting pain or erythema, the most common location was the index finger, but when reporting onycholysis, it was the middle finger. Predictor variables specific to increased risk of onycholysis included: female sex (OR=2.622), older age (OR=1.065), increased duration in hours of the flight or training event (OR=1.570), middle finger length differences in inches between the finger and the EVA glove (OR=7.709), and use of the Phase VI glove (OR=8.535). Differentiation between training and flight and injury reporting during 2002-2004 were significant control variables. For likelihood of time to first onycholysis injury, there was a 24% reduction in rate of reporting for each year increase in age. Also, more experienced crewmembers, based on number of EVA flight or training events completed, were less likely to report an onycholysis injury (3% less for every event). Longer duration events also found reporting rates to occur 2.37 times faster for every hour of length. Crewmembers with larger hand size reported onycholysis 23% faster than those with smaller hand size. Finally, for every 1/10th of an inch increase in difference between the middle finger length and the glove, the rate of reporting increased by 60%. DISCUSSION: One key finding was that the Series 4000 glove had a lower injury risk than the Phase VI, which provides a platform for further evaluation. General interventions that reduce hand overexertion and repetitive use exposure through tool development, procedural changes and shorter exposures may be one mitigation path, but due to the way the training event times were reported, we cannot provide a guideline for a specific event duration change. When the finger length was different from the glove length, the risk of injury increased indicating that the use of larger finger take-ups could be contributing to injury and therefore may not be recommended. Prior to this investigation, there was one previous investigation indicating hand anthropometry may be related to onycholysis. We found different hand anthropometry variables indicated by this investigation as compared to the prior, specifically differences in middle finger length compared to glove finger length, which point more towards a sizing issue than a specific anthropometry issue. Additionally, although this investigation has identified sizing as an issue, the force and environmental-related variables of the EVA glove that could also cause injury were not accounted for.

Reid, C. R.↗

NASA NTRS1988

RICIS Symposium 1988

Integrated Environments for Large, Complex Systems is the theme for the RICIS symposium of 1988. Distinguished professionals from industry, government, and academia have been invited to participate and present their views and experiences regarding research, education, and future directions related to this topic. Within RICIS, more than half of the research being conducted is in the area of Computer Systems and Software Engineering. The focus of this research is on the software development life-cycle for large, complex, distributed systems. Within the education and training component of RICIS, the primary emphasis has been to provide education and training for software professionals.

Source record↗

NASA NTRS1995

Environmental awareness program development

Work this summer in the Office of Safety, Environment, and Mission Assurance began with a review of current initiatives and environmental projects at the Langley Research Center (LaRC). This involved researching many of the documents on file which detail problems which have occurred as well as various approaches which have been used to address these problems. A large portion of the time was spent interviewing and working with each of the engineers, industrial hygienists and other professionals connected with the Office of Environmental Engineering. A few of the projects I worked on include: Researching environmental compliance, and pollution prevention efforts; touring many of the facilities at LaRC to observe the environmental efforts in the work place; researching equipment needs for the recycling/reclamation center; writing scripts for in-house training videos; working with the video production department to produce a training video; developing e-mail distribution list; developing environmental coordinator's database; and working with others to research logistics of recycling and waste minimization efforts.

Steinhauer, David A.↗

NASA NTRS2019

A Global Model for Estimating Atmospheric Phase Scintillation Statistics

Since 2007, the National Aeronautics and Space Administration (NASA) has been collecting atmospheric phase turbulence data from various NASA ground stations throughout the world. The goal of these measurement campaigns has been to generate statistics to characterize the local site turbulence conditions and their impact on widely distributed ground based antenna arrays. This is of critical importance for the situation of uplink arraying, in which a priori knowledge of the fast varying turbulent conditions of water vapor in the troposphere may not be known, and will impact the power combining efficiency of ground based transmitting arrays. Therefore, the design of these type of systems will be dependent on the local climatology of the particular ground station site. Based on the 30+ station years of data collected characterizing atmospheric phase scintillation statistics at various sites, a global model is presented which attempts to predict the average phase statistics of a generic site based on local surface weather data, such as surface pressure, temperature, relative humidity, wind speed, and median wind direction. A model is proposed and based on a standard log power distribution similar to amplitude scintillation models trained on the existing data sets and shows reasonable accuracy against existing data sets.

propagation↗

NASA NTRS1982

Predicting the required number of training samples

A criterion which measures the quality of the estimate of the covariance matrix of a multivariate normal distribution is developed. Based on this criterion, the necessary number of training samples is predicted. Experimental results which are used as a guide for determining the number of training samples are included.

Kalayeh, H. M.↗

NASA NTRS1983

Predicting the required number of training samples

Kalayeh, H. M.↗

NASA NTRS1998

The Application of Dedicated Controllers to Parallel/Serial Exhauster Trains

Twenty new dedicated controllers were interfaced to the Distributed Control System (DCS) at NASA Lewis Research Center. Better surge avoidance, improved energy savings, interface intricacies, and plant complexities are the subjects of this paper. Problems with the existing Programmable Logic Controller (PLC) control logic are described as well as the solutions implemented with the new controllers. The complexity of the new communication network between the dedicated controllers, Multipurpose Micro-Processor (MMPs), and the operator workstations are illustrated. The complicated nature of the plant and methods of controlling the associated processes are also discussed.

Sadhukhan, Debashis↗

NASA NTRS1988

Comparative study of career development and training programs

Improvement of human capital has been identified as the key to any further increase of productivity for any country engaged in the global economic competition. The same can be said of any organization seeking to enhance its overall performance. This study is aimed at: (1) surveying the current practices of career development and training programs at major corporations and government research organizations; (2) presenting the distributions of various program features among survey respondents; (3) identifying the profile of the training program of a typical research organization, against which each organization can check and identify its relative strengths as well as areas needing further strengthening; (4) conducting an economic analysis of the effectiveness of the training programs at Langley Research Center; and (5) making recommendations as to how to enhance existing training programs.

Tsao, Alan↗

NASA NTRS2007

Middleware Trade Study for NASA Domain

This presentation presents preliminary results of a trade study designed to assess three distributed simulation middleware technologies for support of the NASA Constellation Distributed Space Exploration Simulation (DSES) project and Test and Verification Distributed System Integration Laboratory (DSIL). The technologies are: the High Level Architecture (HLA), the Test and Training Enabling Architecture (TENA), and an XML-based variant of Distributed Interactive Simulation (DIS-XML) coupled with the Extensible Messaging and Presence Protocol (XMPP). According to the criteria and weights determined in this study, HLA scores better than the other two for DSES as well as the DSIL

Bowman, Dan↗

NASA NTRS2008

NASA Constellation Distributed Simulation Middleware Trade Study

This paper presents the results of a trade study designed to assess three distributed simulation middleware technologies for support of the NASA Constellation Distributed Space Exploration Simulation (DSES) project and Test and Verification Distributed System Integration Laboratory (DSIL). The technologies are the High Level Architecture (HLA), the Test and Training Enabling Architecture (TENA), and an XML-based variant of Distributed Interactive Simulation (DIS-XML) coupled with the Extensible Messaging and Presence Protocol (XMPP). According to the criteria and weights determined in this study, HLA scores better than the other two for DSES as well as the DSIL.

Hasan, David↗