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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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We investigate techniques for estimating the contributions to TID hardness variability for families of linear bipolar technologies, determining how part-to-part and lot-to-lot variability change for different part types in the process.
Software error data of major recent Digital Flight Control Systems Development Programs. The report summarizes the data, compare these data with similar data from previous surveys and identifies trends and disciplines to improve software reliability.
The first production DRIRU 2 (NASA standard high performance inertial reference unit) system was launched as a subsystem of the Modular Attitude Control System for the Solar Maximum Mission (SMM) spacecraft in February 1980. This hardware was retrieved during the repair of the SMM during Shuttle Flight 41-C in April 1984 and returned to Teledyne Systems Company (manufacturer) for investigation and performance measurements as directed by Goddard Space Flight Center. A failure of one of the three gyro channels occurred approximately 6.5 months after launch. The built in redundancy functioned properly, the DRIRU 2 continued to provide the required attitude control function without performance degradation. Subsequent failure of other attitude control subsystems made the SMM a candidate for the first demonstration of the shuttle in-orbit repair capabilty. The in-orbit DRIRU 2 II failure scenario and the results of the analyses/tests conducted after retrieval are discussed. Comparison of this data with similar data prior to launch demonstates the excellent stability of performance parameters achieveable with DRIRU 2.
The research objective is to improve our understanding of the surface mineralogy of asteroids and to link the vast existing body of meteorite geochemical data with specific astronomical objects which may be the targets of future NASA missions. The methodology employed is: (1) use advanced astronomical instrumentation to obtain reflection spectra in the 0.3 to 5.2 micrometer wavelength range of selected astroids; (2) compare the asteroid data with similar data on simulated asteroid regoliths of various compositions to determine the surface mineralogy and meteoritic affinities of asteroid spectral classes and specific asteroids; (3) integrate the mineralogical information with other astronomical data, orbital dynamics studies, and meteorite geochemistry data to reconstruct the condensational, thermal, and collisional history of the present asteroids and their parent planetesimals; and (4) use the information obtained to assist planning of future NASA asteroid missions.
The research objective was to improve the understanding of the surface mineralogy of asteroids and to link the vast existing body of meteorite geochemical data with specific astronomical objects which may be the targets of future NASA missions. The methodology used is as follow: (1) to use advanced astronomical instrumentation to obtain reflection spectra in the 0.3 to 5.2 micron wavelength range of selected asteroids; (2) to compare the asteroid data with similar data on simulated asteroid regoliths of various compositions to determine the surface mineralogy and meteoritic affinities of asteroid spectral classes and specific asteroids; (3) to integrate the mineralogical information with other astronomical data, orbital dynamics studies, and meteoritic geochemistry data to reconstruct the condensational, thermal, and collisional history of the present asteroids and their parent planetesimals; and (4) to use the information obtained to assist planning of future NASA asteroid missions such as Galileo and CRAF.
We present X-ray data, both detections and upper limits, from the ROSAT all-sky survey for most known M dwarfs later than type M5, as well as from selected ROSAT pointed observations of some of these stars. We compare these data with similar data for early M dwarfs in an attempt to probe the nature of the magnetic dynamo and coronal heating mechanism for the very late M dwarfs, which are presumably totally convective. Our results indicate that late M dwarfs can have coronae which are just as active as those for the early M dwarfs and that coronal heating efficiency for 'saturated' stars does not drop at spectral type M6.
SGR 1806-20 turned active late 1996, and in response to this reactivation we triggered our target-of-opportunity observations. The results were extremely rewarding. We found a periodic modulation of the persistent X-ray signal, at a period of 7.47 seconds. We combined our data with similar data obtained by Dr T. Strohmayer, which confirmed the presence of the pulsations, and gave evidence for a spin down on a time scale of some 1000 years. We retrieved the pulsations in archival ASCA data, obtained in 1993 and 1995, and confirmed the long-term spin down of the neutron star in this system. The properties of SGR 1806-20 lead to the conclusion that it is a mgnetar, i.e., a neutron star with a magnetic field some 2 to 3 orders of magnitude higher than those of young radio pulsars and binary X-ray pulsars.
Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.
Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.
We recently obtained photoclinometric profiles across all simple grabens and erosional landforms (e.g., troughs, pits, wall-valley heads, and scarps that are bounded above and below by flat surfaces) that occur within Tempe Terra. These data, together with similar data that we obtained for Syria, Sinai, and Lunae Plana and the Alba Patera region, allow regional examination of shallow crust Al discontinuities between latitude 30 deg. S and 50 deg. N and longitude 50 deg. W and 112 deg. W. The profile for each simple graben was used with an appropriate structural model to estimate the depth to the base of the faulted layer. The depths of erosional wall scarps may also indicate the depths of mechanical discontinuities such as a local lithologic or cryospheric boundary. Examination of these data indicates a surprisingly consistent set of shallow crust Al discontinuities for the Tharsis region at depths of 0.4-0.6 km, 1.0-1.4 km, and 2 km; the maximum depth of the features in most study areas appears to be about 4 km. The concentration of values between 0.4 and 0.6 km in most scarp and some faulted-layer depth data is similar to the range in estimated thicknesses of individual exposed Noachian and Hesperian plains units in the Tharsis region. The regional depth data also show two modes near 1 and 2 km in some study areas and a maximum depth near 4 km in most study areas; the faulted-layer depths in excess of 4 km at Alba Patera occur near the summit of the caldera and could be attributed to volcanic loading. Our detailed examination of these depth data includes the following observations: (1) The mode at 1.0-1.4 km depth transcends age and geologic setting in this broad study area; (2) The 2-km mode is most obvious at Alba Patera and moderately well developed at Syria and Sinai Plana, but it is muted at Tempe Terra, which is in the same latitude range as Alba Patera but older; and (3) The 2-km-depth mode is not present in all areas that have features of Amazonian age. We suggest two possible explanations for our observations in the Tharsis region.
Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.
Radiation Hardness Assurance (RHA) methodologies against Total Ionizing Dose (TID) degradation impose rigorous statistical treatments for data from a part's Radiation Lot Acceptance Test (RLAT) and/or its historical performance. However, no similar methods exist for using "similarity" data - that is, data for similar parts fabricated in the same process as the part under qualification. This is despite the greater difficulty and potential risk in interpreting of similarity data. In this work, we develop methods to disentangle part-to-part, lot-to-lot and part-type-to-part-type variation. The methods we develop apply not just for qualification decisions, but also for quality control and detection of process changes and other "out-of-family" behavior. We begin by discussing the data used in ·the study and the challenges of developing a statistic providing a meaningful measure of degradation across multiple part types, each with its own performance specifications. We then develop analysis techniques and apply them to the different data sets.
Similarities between experimentally measured power spectral density curves of landing radar signal returned from rough surface and computer simulated curves for sinusoidal surfaces
Confusion over the use of non-video 19 mm data recorders is becoming more pronounced as we enter the world of high performance computing. This paper addresses the following: the differences between ID-1, ID-2, MIL-STD-2179 and DD-2; what the proper machine is for various applications; how the machine can be integrated into an environment; and any misconceptions there might be about 19 mm tape recorders. DD-2 and 19 mm instrumentation recorders have missions for which each is well designed. While the differences may appear subtle, understanding the difference between the two is the key to picking the right recorder for a particular application.
BACKGROUND: Astronauts participating in long duration space missions are at an increased risk of physiological disruptions. The development of visual impairment and intracranial pressure (VIIP) syndrome is one of the leading health concerns for crew members on long-duration space missions; microgravity-induced fluid shifts and chronic elevated cabin CO2 may be contributing factors. By studying physiological and molecular changes in one identical twin during his 1-year ISS mission and his ground-based co-twin, this work extends a current NASA-funded investigation to assess space flight induced "Fluid Shifts" in association with the development of VIIP. This twin study uniquely integrates physiological and -omic signatures to further our understanding of the molecular mechanisms underlying space flight-induced VIIP. We are: (i) conducting longitudinal proteomic assessments of plasma to identify fluid regulation-related molecular pathways altered by long-term space flight; and (ii) integrating physiological and proteomic data with genomic data to understand the genomic mechanism by which these proteomic signatures are regulated. PURPOSE: We are exploring proteomic signatures and genomic mechanisms underlying space flight-induced VIIP symptoms with the future goal of developing early biomarkers to detect and monitor the progression of VIIP. This study is first to employ a male monozygous twin pair to systematically determine the impact of fluid distribution in microgravity, integrating a comprehensive set of structural and functional measures with proteomic, metabolomic and genomic data. This project has a broader impact on Earth-based clinical areas, such as traumatic brain injury-induced elevations of intracranial pressure, hydrocephalus, and glaucoma. HYPOTHESIS: We predict that the space-flown twin will experience a space flight-induced alteration in proteins and peptides related to fluid balance, fluid control and brain injury as compared to his pre-flight protein/peptide signatures. Conversely, the trajectory of these protein signatures will remain relatively constant in his ground based co-twin. METHODS: We are using proteomic and standard immunoelectrophoresis techniques to delineate the change in protein signatures throughout the course of a long duration space flight in relation to the development of VIIP. We are also applying a novel cell-based metaboloic organ system assay ("Organs on a Plate") to address how these circulating biomarkers affect physiological processes at the cellular and organ level which could result in VIIP symptoms. These molecular data will be correlated with physiological measures (eg. extra and intracellular fluid volume, vascular filling/flow patterns, MRI, and Optic Coherence Tomography. DISCUSSION: Pre- and in-flight data collection is in progress for the space-flown twin, and similar data have been obtained from the ground-based twin. Biosamples will be batch processed when received from ISS after the conclusion of the 1-year mission. Omic and Physiological measures from the twin astronauts will be compared to similar data being collected on twin subjects who participated in simulated microgravity study. bed rest study.
CMS is transitioning to use ROOT’s new RNTuple data storage format for the files CMS will write in the HL-LHC era. Based on initial tests, CMS expects faster I/O and smaller files compared to the present TTree storage format. This contribution will show a comprehensive performance comparison between RNTuple and TTree I/O using CMS AOD and MiniAOD data formats as test cases for both simulation and collision data corresponding to similar data taking conditions of LHC Run 3. Quantities such as the resulting file size, the memory usage of the I/O components, and the rate of events being read from a file or written to a file will be measured. CMS’ data processing relies heavily on reading files over the local or wide area networks. The file read patterns are important because the latencies have been seen to influence the total production job times. Therefore a study on the file read patterns will be conducted by recording traces of the offset, size, and timestamp of each read request for both RNTuple and TTree. The behavior of network reads will be mimicked by reading local files where artificial latency will be added to the read requests. The effect of different latency values on the job times will be studied.
The amount of programming required to develop a PATRAN-NASTRAN translator was surprisingly small. The approach taken produced a highly flexible translator comparable with the PATNAS translator and superior to the PATCOS translator. The coding required varied from around ten lines for a shell element to around thirty for a bar element, and the time required to add a feature to the program is typically less than an hour. The use of a lookup table for element names makes the translator also applicable to other versions of NASTRAN. The saving in time as a result of using PDA's Gateway utilities was considerable. During the writing of the program it became apparent that, with a somewhat more complex structure, it would be possible to extend the element data file to contain all data required to define the translation from PATRAN to NASTRAN by mapping of data between formats. Similar data files on property, material and grid formats would produce a completely universal translator from PATRAN to any FEA program, or indeed any CAE system.
The paper discusses research in the Intelligent Data Management project at the NASA/Goddard Space Flight Center, with emphasis on recent improvements in low-level feature detection algorithms for performing real-time characterization of images. Images, including MSS and TM data, are characterized using neural networks and the interpretation of the neural network output by an expert system for subsequent archiving in an object-oriented data base. The data show the applicability of this approach to different arrangements of low-level remote sensing channels. The technique works well when the neural network is trained on data similar to the data used for testing.