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NASA NTRS2015

Integrated Space Asset Management Database and Modeling

Effective Space Asset Management is one key to addressing the ever-growing issue of space congestion. It is imperative that agencies around the world have access to data regarding the numerous active assets and pieces of space junk currently tracked in orbit around the Earth. At the center of this issues is the effective management of data of many types related to orbiting objects. As the population of tracked objects grows, so too should the data management structure used to catalog technical specifications, orbital information, and metadata related to those populations. Marshall Space Flight Center's Space Asset Management Database (SAM-D) was implemented in order to effectively catalog a broad set of data related to known objects in space by ingesting information from a variety of database and processing that data into useful technical information. Using the universal NORAD number as a unique identifier, the SAM-D processes two-line element data into orbital characteristics and cross-references this technical data with metadata related to functional status, country of ownership, and application category. The SAM-D began as an Excel spreadsheet and was later upgraded to an Access database. While SAM-D performs its task very well, it is limited by its current platform and is not available outside of the local user base. Further, while modeling and simulation can be powerful tools to exploit the information contained in SAM-D, the current system does not allow proper integration options for combining the data with both legacy and new M&S tools. This paper provides a summary of SAM-D development efforts to date and outlines a proposed data management infrastructure that extends SAM-D to support the larger data sets to be generated. A service-oriented architecture model using an information sharing platform named SIMON will allow it to easily expand to incorporate new capabilities, including advanced analytics, M&S tools, fusion techniques and user interface for visualizations. In addition, tight control of information sharing policy will increase confidence in the system, which would encourage industry partners to provide commercial data. Combined with the integration of new and legacy M&S tools, a SIMON-based architecture will provide a robust environment that can be extended and expanded indefinitely.

MacLeod, Todd↗

NASA NTRS2017

System for Performing Single Query Searches of Heterogeneous and Dispersed Databases

The present invention is a distributed computer system of heterogeneous databases joined in an information grid and configured with an Application Programming Interface hardware which includes a search engine component for performing user-structured queries on multiple heterogeneous databases in real time. This invention reduces overhead associated with the impedance mismatch that commonly occurs in heterogeneous database queries.

Maluf, David A.↗

NASA NTRS2018

Interactive Multi-Instrument Database of Solar Flares

The fundamental motivation of the project is that the scientific output of solar research can be greatly enhanced by better exploitation of the existing solar/heliosphere space-data products jointly with ground-based observations. Our primary focus is on developing a specific innovative methodology based on recent advances in "big data" intelligent databases applied to the growing amount of high-spatial and multi-wavelength resolution, high-cadence data from NASA's missions and supporting ground-based observatories. Our flare database is not simply a manually searchable time-based catalog of events or list of web links pointing to data. It is a preprocessed metadata repository enabling fast search and automatic identification of all recorded flares sharing a specifiable set of characteristics, features, and parameters. The result is a new and unique database of solar flares and data search and classification tools for the Heliophysics community, enabling multi-instrument/multi-wavelength investigations of flare physics and supporting further development of flare-prediction methodologies.

Heliophysics↗

NASA NTRS2017

New Data-Driven Estimation of Terrestrial CO2 Fluxes in Asia Using a Standardized Database of Eddy Covariance Measurements, Remote Sensing Data, and Support Vector Regression

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial carbon dioxide fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem carbon dioxide exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated carbon dioxide fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r (exp 2) =0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r (exp 2)=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land carbon dioxide fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land carbon dioxide fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land carbon dioxide fluxes. These data-driven estimates can provide a new opportunity to assess carbon dioxide fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

chlorophyll fluorescence↗

NASA NTRS2018

Database of Episode-Integrated Solar Energetic Proton Fluences

A new database of proton episode-integrated fluences is described. This database contains data from two different instruments on multiple satellites. The data are from instruments on the Interplanetary Monitoring Platform-8 (IMP8) and the Geostationary Operational Environmental Satellites (GOES) series. A method to normalize one set of data to one another is presented to create a seamless database spanning 1973 to 2016. A discussion of some of the characteristics that episodes exhibit is presented, including episode duration and number of peaks. As an example of what can be understood about episodes, the July 4, 2012 episode is examined in detail. The coronal mass ejections and solar flares that caused many of the fluctuations of the proton flux seen at Earth are associated with peaks in the proton flux during this episode. The reasoning for each choice is laid out to provide a reference for how CME and solar flares associations are made.

episode characteristcs↗

NASA NTRS2016

Storage of Physical Sample Metadata in the Astrobiology Habitable Environments Database (AHED)

The National Aeronautics and Space Administration has begun an effort to store, curate, and publish information about physical samples collected and analyzed in conjunction with NASA-funded astrobiology research. Astrobiology is a multidisciplinary area of scientific research being conducted by collaborating teams of biologists, chemists, geologists, atmospheric scientists, oceanographers, astrophysicists, astronomers, and other specialists. Astrobiology studies the origin, evolution, and distribution of life in the Universe. NASA uses the results of astrobiology research to focus its future missions on targets of opportunity for the discovery of life off Earth. Astrobiology researchers conduct both field-based and laboratory-based research, during which physical samples are collected, processed, and catalogued. The cataloguing practices employed by different teams of astrobiologists vary widely, and there are no specific standards available to guide the collection and recording of astrobiology sample data. The disparity in data collection approaches and the lack of a centralized sample repository makes it difficult for astrobiology teams to share data and benefit from resultant synergies.To facilitate data sharing within the astrobiology community, NASA is developing a prototype database the Astrobiology Habitable Environments Database (AHED) and an associated set of data collection templates. The database will store information about samples, along with associated measurements and analyses, including information about biological cultures enriched or isolated from samples, and the results of analyses performed on the samples (e.g., via spectrography, microscopy, etc.). In addition, the system will store contextual information about field sites where samples were collected, the instruments or equipment used for analysis, and people and institutions involved in their collection. AHED is being implemented on top of Open Data Repository's Data Publisher [1], an open source software platform for the publication of scientific datasets. The data collection templates under development represent an initial attempt to propose a set of metadata for capture and storage within AHED. The design of these templates is being conducted by a consolidated group of astrobiologists from active research teams at NASA Ames Research Center, assisted by data science and software engineering specialists. These initial templates must be vetted with the broader astrobiology community through a defined process to ensure that they meet community needs. Each template captures a different type of data collection record. For each template, we are developing a list of fields to be captured, including a set of required entry fields, a set of recommended but optional fields, and a set of discretionary fields. A datatype selected from a variety of text and numeric types is specified for each field. Included is a 'choice' type that restricts user input to an enumerated list of values. Many of the fields and field values capture information of particular interest to the astrobiology community, and are intended to facilitate search and retrieval of relevant data across multiple datasets.

Keller, Rich↗

NASA NTRS2019

Establishing Best Practices for X-57 Maxwell CFD Database Generation

The X-57 Maxwell is NASA’s latest electric airplane concept that has been simulated for aerodynamic performance using the structured overset and unstructured grid solvers within the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework as well as the unstructured polyhedral grid solver in Star-CCM+ for code-to-code comparison. In order to validate the predictions, comparisons were made between the CFD solutions and experimental data collected in the 12-foot Low-Speed Wind Tunnel at NASA Langley Research Center. The simulations are in preparation for the development of a comprehensive aerodynamic database which will assess aircraft performance at a variety of conditions. The findings from these simulations will establish the best practices for mesh resolution, numerical discretization, and turbulence modeling to be used for this database. Preliminary database results have shown that best-practices learned from the initial validation simulations will potentially reduce error in X-57 aerodynamic loads and moments relative to experiment by up to 14%.

Duensing, Jared C.↗

NASA NTRSDate not supplied

Harmonized Sentinel-1 SAR Global River Geometry and Inundation Database

Satellite-based observations on river geometries are sporadic in time, space, or both. Most satellite-based surface water maps, river widths, water surface elevations (WSE), slopes, and bathymetry are asynchronized in time and space. The current configuration of satellites such as Sentinel-6 measured the WSE but is missing the river width, slopes, and depths. To advance hydrological sciences research, there is a need to produce a harmonized time series of river geometry data of non-SWOT satellites in partnership with the upcoming SWOT mission. The SWOT satellite will measure river width, height, and slope but missing river depth measurements in space and time. Further, none of these current satellites measure the WSE, river width, and slopes synchronously. In this work, we use the Sentinel-1 SAR satellite data archive from 2015 to the present to create a global river width and surface water database at the reach scale. A modified version of the Sentinel SAR surface water classification algorithm from ASF is used to quantify the surface water extent on the stream approximately every six days (at the equator) at 10m spatial resolution globally. This 10m water mask is fed into a workflow to quantify the river widths, surface water inundations, slopes, and synthetic bathymetry in SWORD (SWOT River Database) stream networks. A Satellite HAND is used to address the cloud obscured surface water observations using a trained machine learning algorithm. We use WSE derived from the Global Water Monitor from NASA GSFC, Hydroweb from LEGOS, and ICESat-2 to harmonize the WSE observation. And Landsat-8/9 and Sentinel-2 water observations to fill the gaps in the Sentinel-1 SAR database. We use Congo River Basin as a test case where we have more than 500 radar altimetry-based WSE, continuous series of Sentinel-1, ICESat-2, Landsat-8/9, and Sentinel-2 observations. A Congo River hydrologic model is used to generate the streamflow discharge. The satellite observed river reaches are assimilated with the stream flows computed by the routing models. And the downstream reaches in the river network without satellite observations get optimized for discharge/river geometry at each observation cycle. Our final product is a harmonized river geometry dataset (reach's water extent, WSE, slope, synthetic bathymetry) for Congo Basin's SWORD reaches.

Chandana Gangodagamage↗

NASA NTRSDate not supplied

Ramdb: The NASA Raman Spectral Database (version 1.00).

Given that, in most instances, minimal sample preparation is required and due to its contactless instrument design, Raman spectroscopy is one of the most versatile vibrational spectroscopic techniques for the chemical analysis of environmental and biological specimens. The diversity of applications of Raman spectroscopy ranges anywhere from art [1] to planetary science missions [2]. The advancement in the use of Raman spectroscopy in Solar System missions, notably in post-mission sample return analysis, requires a spectral library holding the broad range of specimens that could be found in Solar System sources. For this purpose, we have initiated the development of a Raman spectral database (Ramdb) at NASA Ames Research Center. Currently, the database includes experimental and theoretical Raman spectra of PAHs [3, 4], as well as laboratory Raman spectra of amino acids, carbon allotropes, minerals, and analogs relevance to Earth Sciences [5], Exobiology [6], Planetary [7], and Astrochemistry [8] to name just a few examples. Ramdb can be found on the web at www.astrochemistry.org/ramdb, where raw and processed Raman spectra can be downloaded in CSV format. The laboratory Raman spectra are measured using a laser Raman spectrometer (JASCO NRS-5500-532QRI). The Raman instrument is equipped with three excitation lasers, with wavelengths of 405, 532, and 785 nm. A clean silicon substrate is used as the internal standard for wavenumber calibration. Powdered samples were prepared (microscopic >10 um, grounded microscopic < 10 um) on glass slides. Some raw data exhibited a background signal arising as a combination of laser-induced fluorescence from the sample. To correct this background, we developed a Python pipeline that uses open-source Python libraries. Ramdb provides both raw and processed (using Python pipeline) data, which includes tabulated Raman shift transitions and other measurement details. The theoretical Raman band positions of PAHs (pyrene monomers and tetramer clusters) were computed using density functional theory (DFT) with the help of the Gaussian 16 suite of programs [9]. In the near future, Ramdb will serve as a repository of Raman spectral data from Laboratory Astrophysics and Planetary Science experiments involving the irradiation of organic compounds under simulated space and planetary conditions. In addition, online and offline tools will be developed for utilising the database for comparison to the user’s sample.

N Punnakayathil↗

NASA NTRSDate not supplied

A Holistic DSMC Transport Database for Re-Entry and Ablation Modeling

Hybrid simulation frameworks combining Computational Fluid Dynamics (CFD) and Direct Simulation Monte Carlo (DSMC) are frequently employed to efficiently perform high-fidelity solutions of environments containing combined continuum/rarified flow. The use of DSMC, a stochastic, particle-based method, is necessary for high-Knudsen flow where continuum-based assumptions governing CFD break down. However, the DSMC methodology is generally very computationally inefficient to model the continuum regime. In a CFD/DSMC hybrid approach, obtaining an accurate, high-fidelity solution hinges on the consistent treatment of transport properties and the used thermo-chemical models employed within the two solvers. In principle, in regions where CFD and DSMC are both employed, the same gas mixture under the same conditions should have the same properties, regardless of simulation type. Observed differences should be due to non-equilibrium processes, rather than differences in physical models. While the transport models governing CFD and DSMC simulations are starkly different, they can effectively be linked via their use of reduced Chapman-Enskog collision integrals. In CFD, these integrals are typically stored as fitted polynomial expressions and used to directly compute gas transport properties via mixing rules or the full Chapman-Enskog formulation. In DSMC, they can be used to derive the collision parameters needed for the phenomenological collision cross-section models that govern particle interactions, via a Nelder-Mead optimization scheme. The goal of this work is to provide a unified DSMC transport database encompassing the vast majority of known gas species encountered during atmospheric entry, on Earth or any other Solar body. This goal is largely possible due to recently performed ab-initio quantum chemistry calculations. Combined with other high-fidelity literature sources, the planned database will consist of collision integral data for over 200 neutral and ionized species and over 17000 binary collisions. From these collision integrals, Nelder-Mead optimization is used to compute Variable Soft Sphere (VSS) collision model parameters for DSMC, fitted from 300 K to 20000 K. Initial comparisons of transport properties of relevant equilibrium gas mixtures show great agreement between CFD and DSMC-derived results. The completed database will be able to be readily applied to model binary collisions of any gas mixture containing the included species over the specified temperature range, making it a valuable tool for future planetary probe modeling efforts. An example is shown below. Equilibrium mixture transport properties for a 19-species Titan atmospheric model [4] are computed using both fitted VSS parameters and the original CFD collision integral values. Deviations in computed properties between the two approaches is less than 5% for the entire temperature range.

M R Gosma↗

NASA NTRSDate not supplied

Metal Hydroxide and Oxyhydroxide Gas Database

In a high temperature combustion situation such as a gas turbine or rocket engine, combustion products include water vapor. This water vapor reacts with oxides in the hot-gas-path to form volatile hydroxides and oxyhydroxides. The oxides may be structural oxide components or protective oxides on alloys or non-oxide ceramics. In either case, this enhanced volatilization leads to degradation of components. Reliable thermochemical data for many metal hydroxides and oxyhydroxides has not been available. We review a database put together at NASA over many years based on experimental and ab initio computations for thermochemical data of Al, Si, Ti, Cr, Mn, Y, Zr, La, Ta, Gd, and Yb hydroxides and oxyhydroxides. Experimental methods for measuring thermodynamic quantities of metal hydroxides and oxyhydroxides are limited since the required high temperatures and high pressures preclude many conventional methods. Nonetheless, transpiration has proven a valuable tool in our laboratories for the Si-O-H, Cr-O-H, and Ti-O-H systems. Data are analyzed with second and third law methods. These data can be leveraged with quantum chemistry to provide reliable free energy functions for the third law calculation and comparison to the derived enthalpies of formation. Both wave function and DFT methods can be utilized for deriving the thermodynamic functions for the various metal hydroxides and oxyhydroxides. We have found that, in general, reasonable molecular geometries and spectroscopic data can be derived with a relatively low level of theory, i.e. DFT with the B3LYP functional. Accurate calculations of the enthalpy of formation require higher levels of theory, such as the Coupled Cluster, Singles, Doubles, and perturbative Triples CCSD(T) approach with complete basis sets (CBS). Also, corrections for hindered rotations and anharmonicity are necessary. For some symmetries, the anharmonic correction has proven to be a challenge and we have found that this is only treated properly by certain codes. The database lists the fHo(298), So(298), and Cp in the form Cp = A + BT + CT2 + D/T + E/T2, where A, B, C, D, and E are the fitting constants. Thus, it can be easily used in any of the common computational thermochemistry codes. To date, the database contains 51 gaseous species of the formula MOx(OH)y.

Metal Hydroxides↗

NASA NTRSDate not supplied

An Overview of the Aerothermodynamic Database for the Mars Sample Return Earth Entry Vehicle

The Mars Sample Return Earth Entry System (MSR-EES) is a capsule that is part of the Mars Sample Return mission that will return Martian soil samples to Earth in 2033. Due to the steep flight path angle and relatively large vehicle size, the MSR-EES capsule will experience the highest peak heating rate of any previous Earth entry vehicle. The aerothermal database for MSR-EES is primarily characterized by numerical CFD, DSMC, and radiation simulations. Margins are applied to the convective and radiative heating rates. Surface roughness effects and margins are also included in the database formulation. The aerothermal database can be used to extract information at any body point location, perform trade studies in trajectory space, and provide inputs for material response simulations.

Mars↗

NASA NTRS2024

Uncertainty Quantification of Artemis I Space Launch System Integrated Aerodynamics Databases

Accurate prediction of integrated aerodynamic forces and moments is a necessary part of aerospace vehicle development. This accuracy can be quantified in the form of an uncertainty model, which makes the prediction more useful within an integrated vehicle design effort. Aerodynamic force and moment databases were constructed for the Artemis~I mission of the Space Launch System vehicle. These databases reconcile data from multiple sources to yield unified predictions of how NASA's most advanced launch vehicle interacts with Earth's atmosphere as it ascends into orbit. This paper outlines how the uncertainty quantification was performed for these databases to ensure comprehensive and tractable uncertainty source coverage.

Michael W Lee↗

NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗

NASA NTRS2024

Xstar Atomic Database: The PyXstar Package

We present a progress report on the development of PyXstar, a Python package to manage the data (input, output, intermediate, atomic database, and model-grids) associated with the XSTAR code for treating photoionized and collisionally ionized plasmas. The PyXstar modular structure and database retrieval scheme are described, and its functionality is illustrated with Python functions and classes for performing database searches. We briefly compare PyXstar with two other Python spectrum modeling tools: PyNeb and PyAtomDB.

Claudio Mendoza↗

NASA NTRS2002

Windows on the brain: the emerging role of atlases and databases in neuroscience

Brain atlases and associated databases have great potential as gateways for navigating, accessing, and visualizing a wide range of neuroscientific data. Recent progress towards realizing this potential includes the establishment of probabilistic atlases, surface-based atlases and associated databases, combined with improvements in visualization capabilities and internet access.

Review↗

NASA NTRS2000

Database of non-canonical base pairs found in known RNA structures

Atomic resolution RNA structures are being published at an increasing rate. It is common to find a modest number of non-canonical base pairs in these structures in addition to the usual Watson-Crick pairs. This database summarizes the occurrence of these rare base pairs in accordance with standard nomenclature. The database, http://prion.bchs.uh.edu/, contains information such as sequence context, sugar pucker conformation, anti / syn base conformations, chemical shift, p K (a)values, melting temperature and free energy. Of the 29 anticipated pairs with two or more hydrogen bonds, 20 have been encountered to date. In addition, four unexpected pairs with two hydrogen bonds have been reported bringing the total to 24. Single hydrogen bond versions of five of the expected geometries have been encountered among the single hydrogen bond interactions. In addition, 18 different types of base triplets have been encountered, each of which involves three to six hydrogen bonds. The vast majority of the rare base pairs are antiparallel with the bases in the anti configuration relative to the ribose. The most common are the GU wobble, the Sheared GA pair, the Reverse Hoogsteen pair and the GA imino pair.

NASA Discipline Exobiology↗

NASA NTRS1998

A 5.8S nuclear ribosomal RNA gene sequence database: applications to ecology and evolution

We complied a 5.8S nuclear ribosomal gene sequence database for animals, plants, and fungi using both newly generated and GenBank sequences. We demonstrate the utility of this database as an internal check to determine whether the target organism and not a contaminant has been sequenced, as a diagnostic tool for ecologists and evolutionary biologists to determine the placement of asexual fungi within larger taxonomic groups, and as a tool to help identify fungi that form ectomycorrhizae.

Databases, Factual↗