Location-Aware Discrimination in Physics Informed Generative Adversarial Networks
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deep learning↗
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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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The Deep Space Network
This report presents DSN progress in flight project support, tracking and data acquisition (TDA) research and technology, network engineering, hardware and software implementation, and operations. Each issue presents material in some, but not all, of the following categories in the order indicated. - Description of the DSN - Mission Support Ongoing Planetary/Interplanetary Flight Projects Advanced Flight Projects - Radio Science - Special Projects - Supporting Research and Technology Tracking and Ground-Based Navigation Communications--Spacecraft/Ground Station Control and Operations Technology Network Control and Data Processing - Network and Facility Engineering and Implementation Network Network Operations Control Center Ground Communications Deep Space Stations - Operations Network Operations Network Operations Control Center Ground Communications Deep Space Stations - Program Planning TDA Planning Quality Assurance In each issue, the part entitled "Description of the DSN" describes the functions and facilities of the DSN and may report the current configuration of one of the five DSN systems (Tracking, Telemetry, Command, Monitor & Control, and Test & Training). The work described in this report series is either performed or managed by the Tracking and Data Acquisition organization of JPL for NASA.
Tracking and Data Acquisition organization↗
Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment
Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, development of new coating systems is hindered by the large design space and difficulty in predicting properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate accurate prediction of the anisotropy of the CTE in good agreement with diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.
rare earth silicates↗
A new technology for manufacturing scheduling derived from space system operations
A new technology for producing finite capacity schedules has been developed in response to complex requirements for operating space systems such as the Space Shuttle, the Space Station, and the Deep Space Network for telecommunications. This technology has proven its effectiveness in manufacturing environments where popular scheduling techniques associated with Materials Resources Planning (MRPII) and with factory simulation are not adequate for shop-floor work planning and control. The technology has three components. The first is a set of data structures that accommodate an extremely general description of a factory's resources, its manufacturing activities, and the constraints imposed by the environment. The second component is a language and set of software utilities that enable a rapid synthesis of functional capabilities. The third component is an algorithmic architecture called the Five Ruleset Model which accommodates the unique needs of each factory. Using the new technology, systems can model activities that generate, consume, and/or obligate resources. This allows work-in-process (WIP) to be generated and used; it permits constraints to be imposed or intermediate as well as finished goods inventories. It is also possible to match as closely as possible both the current factory state and future conditions such as promise dates. Schedule revisions can be accommodated without impacting the entire production schedule. Applications have been successful in both discrete and process manufacturing environments. The availability of a high-quality finite capacity production planning capability enhances the data management capabilities of MRP II systems. These schedules can be integrated with shop-floor data collection systems and accounting systems. Using the new technology, semi-custom systems can be developed at costs that are comparable to products that do not have equivalent functional capabilities and/or extensibility.
Hornstein, R. S.↗
JPL Fault Protection Experiences - Case Studies
This slide presentation shows several case studies for fault protection. The cases involve a discovery-class mission to excavate material from a comet, rendezvous with two asteroids and develop a prototype system for a next-generation Deep Space Network consisting of ndca large array of small antennas.
fault protection↗
Cryogenic filters for RFI protection
The increased bandwidth and sensitivity of the DSN maser-based receiver systems along with the increase in worldwide microwave spectrum usage dictated the need for employing additional measures to protect these systems from RFI (radio frequency inerference). Both in-band and out-of-band microwave signals at the input of the Deep Space Network (DSN) traveling wave masers (TWM) can adversely affect the maser performance in a variety of ways. Filters fabricated from superconducting materials operating below their superconducting transition temperature (Tc) possess the most potential for providing the necessary RFI protection without degrading the system performance.
Bautista, J. J.↗
The deep space network, Volume 11
Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.
Source record↗
The S/X-Band Microwave Feed System for NASA's First Beamwaveguide Antenna
The configuration, detail design, and performance of the dual S/X-band microwave feed system for the first Deep Space Network (DSN) beamwaveguide (BWG) antenna are reported int his paper. By using existing spare components, simplifying the design of new components, and using new fabrication techniques and material, this feed system was implemented successfully with a small budget and a very tight schedule. The measured noise temperature of the feed systems is 17.5 Kelvin for S-band and 24.0 Kelvin for X-band, which agrees very closely with the predicted performance.
S/X-Band↗
New seismic data on the state of the deep lunar interior.
Direct shear-wave arrivals from seismic events originating on the far side of the moon are not observed at some of the stations of the Apollo seismic network. These data suggest that the material in the lunar interior at a depth of 1000 to 1100 kilometers is more dissipative for seismic shear waves than the lithosphere above, and possibly exists in a partially molten state akin to the earth's asthenosphere.
Nakamura, Y.↗
Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks
The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.
Hannah Alpert↗
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↗
Fast Electromechanical Switches Based on Carbon Nanotubes
Electrostatically actuated nanoelectromechanical switches based on carbon nanotubes have been fabricated and tested in a continuing effort to develop high-speed switches for a variety of stationary and portable electronic equipment. As explained below, these devices offer advantages over electrostatically actuated microelectromechanical switches, which, heretofore, have represented the state of the art of rapid, highly miniaturized electromechanical switches. Potential applications for these devices include computer memories, cellular telephones, communication networks, scientific instrumentation, and general radiation-hard electronic equipment. A representative device of the present type includes a single-wall carbon nanotube suspended over a trench about 130 nm wide and 20 nm deep in an electrically insulating material. The ends of the carbon nanotube are connected to metal electrodes, denoted the source and drain electrodes. At bottom of the trench is another metal electrode, denoted the pull electrode (see figure). In the off or open switch state, no voltage is applied, and the nanotube remains out of contact with the pull electrode. When a sufficiently large electric potential (switching potential) is applied between the pull electrode and either or both of the source and drain electrodes, the resulting electrostatic attraction bends and stretches the nanotube into contact with the pull electrode, thereby putting the switch into the "on" or "closed" state, in which substantial current (typically as much as hundreds of nanoamperes) is conducted. Devices of this type for use in initial experiments were fabricated on a thermally oxidized Si wafer, onto which Nb was sputter-deposited for use as the pull-electrode layer. Nb was chosen because its refractory nature would enable it to withstand the chemical and thermal conditions to be subsequently imposed for growing carbon nanotubes. A 200- nm-thick layer of SiO2 was formed on top of the Nb layer by plasma-enhanced chemical vapor deposition. In the device regions, the SiO2 layer was patterned to thin it to the 20-nm trench depth. The trenches were then patterned by electron- beam lithography and formed by reactive- ion etching of the pattern through the 20-nm-thick SiO2 to the Nb layer.
Kaul, Anupama↗
The Solar-Stellar Connection (NAG5-6124: SOHO Guest Investigator Program)
The following is a final report from the SOHO Guest Investigator program to use the SUMER far-UV spectrometer to obtain imaging spectroscopy in support of the goals of the so-called "solar-stellar connection." In particular, a major emphasis was utilization of the long-slit time-resolved maps of the solar surface in bright far-UV emission lines to deduce how particular aspects of the temporally and spatially averaged line profiles trace back to individual structural features of the magnetically disturbed outer atmosphere; to help interpret the unresolved line profiles from high quality stellar observations (say, with the Space Telescope Imaging Spectrograph). The researchers served two tours of duty in the SOHO Operations Center as SUMER planners, during which time we conducted an extensive series of observing programs. These can be divided into three general categories: surface mapping, translimb spectroscopy, and active region diagnostics. We have analyzed some of the large volumes of data to the point where we have presented them in poster papers, and in invited papers at national and international meetings. Listed below are the titles of the preliminary publications we have written, including brief abstracts to indicate the main results. (1) Chromospheric structure and Dynamics-- Observations -- The chromosphere is a highly structured dynamic 'layer' of the solar outer atmosphere. Here, not only are the effects of mechanical heating first evident (moving upward in altitude from the deep photosphere), but also the amount of nonradiative energy deposited is far greater than in the albeit much hotter overlying transition region and corona. Further, the chromosphere is by far the thickest zone of the solar atmosphere with respect to the pressure scale height. A major goal of stellar astrophysics is to understand how the chromosphere is heated and why it adopts its peculiar structure. A cursory examination of solar filtergrams and high-resolution movies demonstrates that much of the chromospheric "action" must be occurring on fine spatial scales and short times; particularly in the cell interior transient brightenings, but also in the longer-lived network fragments. That regime of investigation is far removed from what one usually associates with "synoptic" measurements. Nevertheless, synoptic observations of chromospheric indices, filtergrams, and globally-averaged profile parameters not only can provide important insight concerning the crucial role of the cycle-variable part of the solar magnetic field; but they also can forge a key link with analogous measurements of the stars, where often the phenomena can be significantly exaggerated from the solar case, but high spatial resolution reconnaissance is not even a remote possibility. In addition to discussing the synoptic aspects of chromospheric structure and dynamics, I summarized new insights into the general problem of the solar chromosphere that have been obtained recently with the SUMER far-ultraviolet spectrometer on SOHO. (2) CO and the Temperature Structure of the Solar Atmosphere -- The surface layers of the Sun provide a crucial boundary condition for many of the processes that occur in the deep interior. The stratification of the outer solar atmosphere once was thought to be well understood. However, studies of thermally sensitive molecular absorptions in the infrared revealed puzzling anomalies. Strong lines of the CO fundamental vibration-rotation bands near 5 microns showed very cool temperatures at the extreme limb, and remarkable off-limb emissions extending well into the supposedly hot chromosphere. The conflicting pictures of the photosphere/chromosphere interface, from the widely separated wavelength regimes, has raised suspicions that those "layers" of the atmosphere are much more inhomogeneous than previously suspected. One proposal is that the low chromosphere is dominated by cool gas, the "COmosphere," which is threaded by a network of persistent small-scale hot magnetic filaments and occasionally disrupted by localized acoustic disturbances. The COmosphere is capped by the merged fields of the network elements in the chromospheric "canopy." (3) Translimb Spectroscopy with SOHO/SUMER -- We have used SUMER to obtain deep exposures of the 1300-1400 A spectrum, at the extreme limb and off-limb. Previous "translimb" studies in the thermal infrared had revealed remarkable extensions of cold material (T is approximately equal to 3000 K)-traced by carbon monoxide emission lines-into the heart of the hot chromosphere. A main objective of our program was to search for corresponding far-UV signatures of the "thermally-bifurcated" low chromosphere; for example, radiatively fluoresced emissions of the CO A-X 4th-positive system (collisional excitation would be negligible in cold gas). We conducted two separate observing programs with SUMER. Both made use of the 1 inch-diameter circular aperture, translated across the limb in the minimum motor step increments of 0."375, along the central meridian in the Northern polar coronal hole. The first program executed for nine hours beginning 19UT 25 Oct 1996. The full wavelength range was 1340-1400 A. It was recorded in two overlapping segments, placing key regions of the spectrum alternately on the KBr and bare parts of the detector, to help isolate 2nd-order features. Each segment was integrated for 500 s, and 32 pairs were obtained to span a 12" swath centered on the optical limb. The second program was conducted 00-09UT 01 Dec 1996. It consisted of a single wavelength setting (1300-1340 A) with exposure time 500 s, but twice the spatial coverage of the earlier series: 64 steps, for a total displacement of 24". The strong chromospheric resonance lines of atomic oxygen (1302-1305 A) and ionized carbon (1334-1335 A) were observed on the bare part of the MCP camera.
Ayres, Thomas R.↗
Jet Propulsion Laboratory: Annual Report 2007
Many milestones are celebrated in the business of space exploration, but one of them that arrived this year has particular meaning for us. Half a century ago, on January 31, 1958, the Jet Propulsion Laboratory was responsible for creating America's first satellite, Explorer 1, and joined with the Army to launch it into orbit. That makes 2007 the 50th year we have been sending robotic craft from Earth to explore space. No other event before or since has had such a profound effect on JPL's basic identity, setting it on the path to become the world's leader in robotic solar system exploration. It is not lost on historians that Explorer 1, besides being America's first satellite, was also the first spacecraft from any country to deliver scientific results in its case, the discovery of the Van Allen Radiation Belts that surround Earth. Science, of course, has been the prime motivator for all the dozens of missions that we have lofted into space in the half-century since then. JPL has sent spacecraft to every planet in the solar system from Mercury to Neptune, some of them very sophisticated machines. But in one way or another, they all owe their heritage to the 31-pound bullet-shaped probe JPL shot into space in 1958. Although we have ranged far and wide across the solar system, we have a very strong contingent of satellites and instruments dedicated, like Explorer, to the environment of our home planet. JPL missions have been providing much of the data to establish the facts of global warming - most especially, the melting of ice sheets in Greenland and Antarctica. During the past year, JPL and our parent organization, the California Institute of Technology, have created a task force to focus the special capabilities of the Laboratory and campus on ways to better understand the physics of global change. While Earth is a chaotic and dynamic system capable of large natural variations, evidence is mounting that human activities are playing an increasingly important role. A central piece of this effort is a search for novel energy sources to replace fossil fuels, the combustion of which adds carbon dioxide to our atmosphere. All of this is supported by our worldwide Deep Space Network, which provides the communication link between spacecraft and the ground. In addition, missions are infused with technologies developed by researchers working on projects for non-NASA sponsors as well as on pure research.
Jet Propulsion Laboratory (JPL)↗
Spatial Extent of a Deep Moonquake Nest: A Preliminary Report of Reexamination
Deep moonquakes, occurring at depths about halfway to the center of the Moon, were discovered during the Apollo lunar landing missions, 1969-1972. Their near-monthly occurrence with nearly identical waveforms at any given seismic station suggests that they are strongly influenced by tides, caused by the Earth and the Sun, acting on certain limited regions of the deep lunar interior. However, much about them is unknown, why they are restricted to certain depths and to limited source regions (nests), and what they tell us about the nature of the material and dynamics of the interior of the Moon. A piece of information helpful to decipher their true nature is the spatial extent and distribution of their hypocenters. The occurrence of nearly identical waveforms suggests groups of hypocenters appear in close proximity to one another, but can we tell how closely they are located and how they are distributed? The Apollo PSE (Passive Seismic Experiment) data from all stations of the seismic network were received in real time at a common receiving station on Earth in digital form. This provided extremely high inter-station timing accuracy not achievable for most earthquake data on Earth at that time. We took advantage of this to compute relative locations of waveform-matched events, and concluded that deep moonquake foci in the A1 moonquake nest were concentrated on a nearly horizontal plane of less than 1 km in diameter. This earlier study was based on deep moonquake events visually identified on seismograms. A recent reanalysis of earlier unidentified seismic events fully utilizing the high capability of present-day computers expanded the list of positively identified deep moonquakes by more than a factor of five. The new list contains many events that are not visually matched in waveforms, yet correlated at a significant level when cross-correlated with a computer. Thus it became imperative to reexamine the spatial distribution of deep moonquake hypocenters including the newly identified events. Additional information is included in the original extended abstract.
Nakamura, Yosio↗
NASA Tech Briefs, March 2012
The topics include: 1) Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy; 2) Portable Fourier Transform Spectroscopy for Analysis of Surface Contamination and Quality Control; 3) In Situ Geochemical Analysis and Age Dating of Rocks Using Laser Ablation-Miniature Mass Spectrometer; 4) Physics Mining of Multi-Source Data Sets; 5) Photogrammetry Tool for Forensic Analysis; 6) Connect Global Positioning System RF Module; 7) Simple Cell Balance Circuit; 8) Miniature EVA Software Defined Radio; 9) Remotely Accessible Testbed for Software Defined Radio Development; 10) System-of-Systems Technology-Portfolio-Analysis Tool; 11) VESGEN Software for Mapping and Quantification of Vascular Regulators; 12) Constructing a Database From Multiple 2D Images for Camera Pose Estimation and Robot Localization; 13) Adaption of G-TAG Software for Validating Touch and Go Asteroid Sample Return Design Methodology; 14) 3D Visualization for Phoenix Mars Lander Science Operations; 15) RxGen General Optical Model Prescription Generator; 16) Carbon Nanotube Bonding Strength Enhancement Using Metal Wicking Process; 17) Multi-Layer Far-Infrared Component Technology; 18) Germanium Lift-Off Masks for Thin Metal Film Patterning; 19) Sealing Materials for Use in Vacuum at High Temperatures; 20) Radiation Shielding System Using a Composite of Carbon Nanotubes Loaded With Electropolymers; 21) Nano Sponges for Drug Delivery and Medicinal Applications; 22) Molecular Technique to Understand Deep Microbial Diversity; 23) Methods and Compositions Based on Culturing Microorganisms in Low Sedimental Fluid Shear Conditions; 24) Secure Peer-to-Peer Networks for Scientific Information Sharing; 25) Multiplexer/Demultiplexer Loading Tool (MDMLT); 26) High-Rate Data-Capture for an Airborne Lidar System; 27) Wavefront Sensing Analysis of Grazing Incidence Optical Systems; 28) Foam-on-Tile Damage Model; 29) Instrument Package Manipulation Through the Generation and Use of an Attenuated-Fluent Gas Fold; 30) Multicolor Detectors for Ultrasensitive Long-Wave Imaging Cameras; 31) Lunar Reconnaissance Orbiter (LRO) Command and Data Handling Flight Electronics Subsystem; and 32) Electro-Optic Segment-Segment Sensors for Radio and Optical Telescopes.
Source record↗
Environmental projects. Volume 12: Friable asbestos abatement, GDSCC
The Goldstone Deep Space Communications Complex (GDSCC) is part of the NASA Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. These activities may give rise to a variety of environmental hazards, particularly the danger of exposure of GDSCC personnel to asbestos fibers that have been shown to be responsible for such serious ailments as asbestosis, lung cancer, and mesothelioma. Asbestos-containing materials (ACM's) were used in the construction of many of the approximately 100 buildings and structures that were built at the GDSCC during a 30-year period from the 1950s through 1980s. The friable asbestos-abatement program at the GDSCC is presented which consists of text, illustrations, and tables that describe the friable asbestos abatement carried out at the GDSCC from December 21, 1988 through May 11, 1989.
Source record↗
Viking bistatic radar experiment - Summary of first-order results emphasizing north polar data
Initial results of bistatic radar observations of Mars made by the Viking Orbiter spacecraft are presented with particular emphasis on the previously unstudied polar regions. Bistatic radar scattering experiments were performed in near-equatorial regions by the Viking Orbiter 1, while other regions of the planet were observed by the polar-orbiting Orbiter 2, with scattered signals received by stations of the NASA Deep Space Network. In the equatorial region, the bistatic radar estimates of rms surface slope are found to be in qualitative agreement with results obtained using earth-based transmitter-receivers, showing a nearly 2:1 decrease in rms surface roughness between 20 and 22 deg N with no appreciable change in dielectric constant. Data in the north polar region reveal variations in surface roughness from 1 to 6 deg, with areas of smooth material generally located within rougher environs, surface roughness decreasing along Vastitas Borealis in the direction of the north pole, and north polar cap rms roughnesses on the order of 2.5-3.0 deg. Trends in radar reflectivity suggest a decreasing surface density with increasing latitude, consistent with a layer of seasonal CO2 or H2O snow increasing in depth as the pole is approached.
Simpson, R. A.↗