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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

The Deep Impact Network Experiment Operations Center

Delay/Disruption Tolerant Networking (DTN) promises solutions in solving space communications challenges arising from disconnections as orbiters lose line-of-sight with landers, long propagation delays over interplanetary links, and other phenomena. DTN has been identified as the basis for the future NASA space communications network backbone, and international standardization is progressing through both the Consultative Committee for Space Data Systems (CCSDS) and the Internet Engineering Task Force (IETF). JPL has developed an implementation of the DTN architecture, called the Interplanetary Overlay Network (ION). ION is specifically implemented for space use, including design for use in a real-time operating system environment and high processing efficiency. In order to raise the Technology Readiness Level of ION, the first deep space flight demonstration of DTN is underway, using the Deep Impact (DI) spacecraft. Called the Deep Impact Network (DINET), operations are planned for Fall 2008. An essential component of the DINET project is the Experiment Operations Center (EOC), which will generate and receive the test communications traffic as well as "out-of-DTN band" command and control of the DTN experiment, store DTN flight test information in a database, provide display systems for monitoring DTN operations status and statistics (e.g., bundle throughput), and support query and analyses of the data collected. This paper describes the DINET EOC and its value in the DTN flight experiment and potential for further DTN testing.

flight experiment↗
NASA NTRS2012

Target Search and Selection for the DI/EPOXI Spacecraft

Upon completion of the Hartley 2 flyby in November 2010, the Deep Impact (DI) spacecraft resided in a solar orbit without possibility for gravity assist with any large body. Conservative estimates of remaining fuel were enough to provide only an 18 m/s impulse on the spacecraft. We present our method and results of our systematic scan of potential small body encounters for DI, and our criteria to narrow the selection to the asteroid 2002 GT as the target flyby body. The mission profile has two deterministic maneuvers to achieve the encounter, the first of which executed on November 25, 2011.

Deep Impact (DI)↗
NASA NTRS2006

A Whale of a Tale: Creating Spacecraft Telemetry Data Analysis Products for the Deep Impact Mission

This paper describes some of the challenges and lessons learned from the Deep Impact (DI) Mission Ground Data System's (GDS) telemetry data processing and product generation tool, nicknamed 'Whale.' One of the challenges of any mission is to analyze testbed and operational telemetry data. Methods to retrieve this data to date have required spacecraft subsystem members to become experts in the use of a myriad of query and plot tools. As budgets shrink, and the GDS teams grow smaller, more of the burden to understand these tools falls on the users. The user base also varies from novice to expert, and requiring them to become GDS tool experts in addition to spacecraft domain experts is an undue burden. The "Whale" approach is to process all of the data for a given spacecraft test, and provide each subsystem with plots and data products 'automagically.'.

Deep Impact↗
NASA NTRS2010

Earth Glint Observations Conducted During the Deep Impact Spacecraft Flyby

We describe observations of Earth conducted using the High Resolution Instrument (HRI) - a 0.3 m f/35 telescope - on the Deep Impact (DI) spacecraft during its recent flybys. Earth was observed on five occasions: 2008-Mar-18 18:18 UT, 2008-May-28 20:05 UT, 2008-Jun-4 16:57 UT, 2009-Mar-27 16:19 and 2009-Oct-4 09:37 UT. Each set of observations was conducted over a full 24-hour rotation of Earth and a total of thirteen NIR spectra were taken on two-hour intervals during each observing period. Photometry in the 450, SSO, 650 and 8S0 nm filters was taken every fifteen minutes and every hour for the 350, 750 and 950 nm filters. The spacecraft was located over the equator for the three sets of observations in 2008, while the 2009- Mar and 2009-Oct were taken over the north and south Polar Regions, respectively. Observations of calibrator stars Canopus and Achernar were conducted on multiple occasions through all filters. The observations detected a strong specular glint not necessarily associated with a body of water. We describe spectroscopic characterization of the glint and evidence for the possibility of detection of reflection from high cirrus clouds. We describe implications for observations of extrasolar planets.

Barry, R. K.↗
NASA NTRS2011

Views from EPOXI: Colors in Our Solar System as an Analog for Extrasolar Planets

The first visible-light studies of Earth-sized extrasolar planets will employ photometry or low-resolution spectroscopy. This work uses EPOCh medium-hand filter photometry between 150 and 950 nm obtained with the Deep Impact (DI) High Resolution Instrument (HRI) of Earth, the Moon, and Mars in addition to previous full-disk observations of the other six solar system planets and Titan to analyze the limitations of using photometric colors to characterize extrasolar planets. We determined that the HRI 350, 550, and 850 nm filters are optimal for distinguishing Earth from the other planets and separating planets to first order based on their atmospheric and surface properties. Detailed conclusions that can be drawn about exoplanet atmospheres simply from a color-color plot are limited due to potentially competing physical processes in the atmosphere. The presence of a Rayleigh scattering atmosphere can be detected by an increase in the 350-550 nm brightness ratio, but the absence of Rayleigh scattering cannot be confirmed due to the existence of atmospheric and surface absorbing species in the UV. Methane and ammonia are the only species responsible for strong absorption in the 850 nm filter in our solar system. The combination of physical processes present on extrasolar planets may differ from those we see locally. Nevertheless, a generation of telescopes capable of collecting such photometric observations can serve a critical role in first-order characterization and constraining the population of Earth-like extrasolar planets.

Crow, Carolyn A.↗
NASA NTRS2013

Terminal Guidance Navigation for an Asteroid Impactor Spacecraft

Near Earth Asteroids could potentially cause a great deal of devastation if one were to impact the Earth. If such an asteroid were found, the next step would be to mitigate the threat by one of several options, the most viable of which is to deflect the asteroids trajectory such that it misses the Earth by hitting it at a very high velocity with a spacecraft. The technology to perform such a deflection has been demonstrated by the Deep Impact (DI) mission, which successfully collided with comet Tempel 1 in July 2005 using an onboard autonomous navigation system, called AutoNav, for the terminal phase of the mission. In this paper, we evaluate the ability of AutoNav to impact a wide range of scenarios that an deflection mission could encounter, varying parameters such as the approach velocity, phase angle, size of the asteroid, and the determination of spacecraft attitude. Using realistic Monte Carlo simulations, we tabulated the probability of success of the deflection as a function of these parameters, and find the highest sensitivity to be due the spacecraft attitude determination mode. We conclude with some recommendations for future work.

autonomous navigation↗
NASA NTRS2013

Closed Loop Terminal Guidance Navigation for a Kinetic Impactor Spacecraft

A kinetic impactor spacecraft is a viable method to deflect an asteroid which poses a threat to the Earth. The technology to perform such a deflection has been demonstrated by the Deep Impact (DI) mission, which successfully collided with comet Tempel 1 in July 2005 using an onboard autonomous navigation system, called AutoNav, for the terminal phase of the mission. In this paper, we evaluate the ability of AutoNav to impact a wide range of scenarios that an deflection mission could encounter, varying parameters such as the approach velocity, phase angle, size of the asteroid, and the determination of spacecraft attitude. Using realistic Monte Carlo simulations, we tabulated the probability of success of the deflection as a function of these parameters, and the highest sensitivity to be due the spacecraft attitude determination mode. In addition, we also specifically analyzed the impact probability for a proposed mission which would send an impactor to the asteroid 1999RQ36. We conclude with some recommendations for future work.

closed loop guidance↗
NASA NTRS2012

Target Search & Selection for the DI/EPOXI Spacecraft

Upon completion of the Hartley 2 flyby in November 2010, the Deep Impact (DI) spacecraft resided in a solar orbit without possibility for gravity assist with any large body. Conservative estimates of remaining fuel were enough to provide only an 18 m/s impulse on the spacecraft. We present our method and results of our systematic scan of potential small body encounters for DI, and our criteria to narrow the selection to the asteroid 2002 GT as the target flyby body. The mission profile has two deterministic maneuvers to achieve the encounter, the first of which executed on November 25, 2011.

Grebow, Daniel J.↗
NASA NTRS2011

Navigation of the EPOXI Spacecraft to Comet Hartley 2

On November 4, 2010, the EPOXI spacecraft flew by the comet Hartley 2, marking the fourth time that a NASA spacecraft successfully captured high resolution images of a cometary nucleus. EPOXI is the extended mission of the Deep Impact mission, which delivered an impactor on comet Tempel-1 on July 4, 2005. EPOXI officially started in September 2007 and eventually took over 3 years of flight time and had 3 Earth gravity assists to achieve the proper encounter conditions. In the process, the mission was redesigned to accommodate a new comet as the target and changes in the trajectory to achieve better imaging conditions at encounter. Challenges in navigation of the spacecraft included precision targeting of several Earth flybys and the comet encounter, uncertainties in determining the ephemeris of the comet relative to the spacecraft, and the high accuracy trajectory knowledge needed to image the comet during the encounter. This paper presents an overview of the navigation process used for the mission.

Deep Impact (DI)↗
NASA NTRS2011

EPOXI Trajectory and Maneuver Analyses

The EPOXI mission is a NASA Discovery Mission of Opportunity combining two separate investigations: Extrasolar Planet Observation and Characterization (EPOCh) and Deep Impact eXtended Investigation (DIXI). Both investigations reused the DI instruments and spacecraft that successfully flew by the comet Tempel-1 (4 July 2005). For EPOCh, the goal was to find exoplanets with the high resolution imager, while for DIXI it was to fly by the comet Hartley 2 (4 Nov 2010). This paper documents the navigation experience of the earlier ma-neuver analyses critical for the EPOXI mission including statistical ?V analyses and other useful analyses in designing maneuvers. It also recounts the trajectory design leading up to the final reference trajectory to Hartley 2.

trajectory design↗
DOE OSTI2020

Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenides with Sub-Picometer Precision

Two-dimensional (2D) materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so far limited electron microscopy methods to probe these atomic-scale strain fields. In this work, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe 2–2x Te 2x . We utilize deep learning to mine large data sets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class averages which allow us to measure 2D atomic spacings with up to 0.2 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe 2–2x Te 2x lattice that correspond to alternating rings of lattice expansion and contraction. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials.

2D materials↗
DOE OSTI2021

Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin

The subsurface is among Earth’s largest biomes, but the extent to which microbial communities vary across tectonic plate boundaries or interact with subduction-scale geological processes remains unknown. Here we compare bacterial community composition with deep-subsurface geochemistry from 21 hot springs across the Costa Rican convergent margin. We find that cation and anion compositions of the springs reflect the dip angle and position of the underlying tectonic structure and also correlate with the bacterial community. Co-occurring microbial cliques related to cultured chemolithoautotrophs that use the reverse tricarboxylic acid cycle (rTCA) as well as abundances of metagenomic rTCA genes correlate with concentrations of slab-volatilized carbon. This, combined with carbon isotope evidence, suggests that fixation of slab-derived CO2 into biomass may support a chemolithoautotrophy-based subsurface ecosystem. We calculate that this forearc subsurface biosphere could sequester 1.4 × 10 9 to 1.4 × 10 10 mol of carbon per year, which would decrease estimates of the total carbon delivered to the mantle by 2 to 22%. Furthermore, based on the observed correlations, we suggest that distribution and composition of the subsurface bacterial community are probably affected by deep tectonic processes across the Costa Rican convergent margin and that, by sequestering carbon volatilized during subduction, these chemolithoautotrophic communities could in turn impact the geosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2024

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Assimilation of MODIS Dark Target and Deep Blue Observations in the Dust Aerosol Component of NMMB-MONARCH version 1.0

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter - LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.

atmospheric dust↗