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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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NASA NTRS2019

Deep Space Habitability Design Guidelines Based on the NASA NextSTEP Phase 2 Ground Test Program

This report summarizes habitation design guidelines for deep space habitats, which were derived from the NASA Next Space Technologies for Exploration Partnerships (NextSTEP) Phase 2 Habitat Ground Test Program. All data presented in this document have been contractor-deidentified and approved for public release. The report prioritizes capabilities and recommends allocating those capabilities to either the Habitation and Logistics Outpost (HALO) or the International Habitat (I-Hab). A review of the design guidelines is presented in the main body of the report, along with a list of the 170 specific design guidelines with references to the specific data sources from which they were derived.

Gernhardt, Michael↗
NASA NTRS2020

The Atmospheric Measurements of ICESat-2

ICESat-2 is an exciting new multi-beam, photon-counting, satellite lidar designed to acquire simultaneous high-resolution measurements of the earth's surface and atmospheric structure. Launched in September, 2018 the Advanced Topographic Lidar Altimeter System (ATLAS) on board ICESat-2 has been acquiring data continuously for nearly 1 year. The 3 laser beams comprising the atmospheric channel are providing excellent back scatter measurements that enable the retrieval of global cloud fraction, cloud and aerosol layer height, column optical depth, and blowing snow properties. Public release of the data products began with version 1in May, 2019 and continues with a significantly improved version 2 in October, 2019. This presentation will give an overview of the ICESat-2 atmospheric channel and show examples of cloud, aerosol and blowings now properties as well as comparisons of ATLAS and CALIOP measurements.

Palm, Stephen↗
NASA NTRSDate not supplied

An Interactive MATLAB Program for Fitting Transfer Functions to Frequency Responses

A computer program called FRFit (Frequency Response Fitting) is described for fitting single-input single-output transfer function models to empirical frequency response data. The program is interactive in that the user specifies ``elementary factors'' (gain, delay, pure differentiators and integrators, and first- and second-order zeros and poles) by entering numerical values or moving sliders in a graphical user interface. A nonlinear optimization can then be performed to obtain maximum likelihood estimates of transfer function parameters and uncertainties to provide feedback on the modeling and refine estimates. Several examples are discussed, including the identification of aircraft pitch dynamics from simulation data and data reported in the literature, approximating Theodorsen's function of unsteady aerodynamics, and obtaining a reduced-order model of a computational fluid dynamics code describing the unsteady aerodynamics around an aeroelastic wing. The program has some usefulness as a teaching aid, and can be applied to model structure determination, reduced-order modeling, preliminary analysis, and simple system identification problems. The program was written in MATLAB and is planned for public release through the NASA Software Catalog.

System identification↗
NASA NTRS2020

The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products

The NASA Aqua MODIS and Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) climate data record continuity cloud properties products (CLDPROP) were publicly released in April 2019 with an update later that year (Version 1.1). These cloud products, having heritage with the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) MOD06 cloud optical properties product and the NOAA GOES-R Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA), represent an effort to bridge the multispectral imager records of NASA’s Earth Observing System (EOS) and NOAA’s current generation of operational weather satellites to achieve a continuous, multi-decadal climate data record for clouds that can extend well into the 2030s. CLDPROP offers a “continuity of approach,” applying common algorithms and ancillary datasets to both MODIS and VIIRS, including utilizing only a subset of spectral channels available on both sensors to help mitigate instrument differences. The initial release of the CLDPROP_MODIS and CLDPROP_VIIRS data records spans the SNPP observational record (2012-present). Here, we present an overview of the algorithms and an evaluation of the intersensor continuity of the core CLDPROP_MODIS and CLDPROP_VIIRS cloud optical property datasets, i.e., cloud thermodynamic phase, optical thickness, effective particle size, and derived water path. The evaluation includes analyses of pixel-level MODIS/VIIRS co-locations as well as spatial and temporal aggregated statistics, with a focus on identifying and understanding the root causes of individual dataset discontinuities. The results of this evaluation will inform future updates to the CLDPROP products and help scientific users determine the appropriate use of the product datasets for their specific needs.

satellite remote sensing↗
NASA NTRS2020

The Atacama Cosmology Telescope: DR4 maps and cosmological parameters

We present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013{2016 at 98 and 150 GHz. The maps cover more than 17,000 deg(^2), the deepest 600 deg(^2) with noise levels below 10µK-arcmin. We use the power spectrum derived from almost 6,000 deg(^2) of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H0. By combining ACT data with large-scale information from WMAP we measure H0 = 67:6±1:1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently measured Planck satellite estimate (from ACT alone we find H0 = 67:9± 1:5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5{2.2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.

Simone Aiola↗
NASA NTRS2021

Data and Reasoning Fabric (DRF) Video 2

The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed a video, suitable for public release.

Data Reasoning↗
NASA NTRSDate not supplied

Human Factors Considerations for Pressurized Crew Transfer Between Ascent or Lander Spacecraft and Surface Assets

An option for pressurized crew transfer between surface assets and lander or ascent spacecraft offers multiple advantages. The most important are the loss of life risks due to crew member injuries or surface spacesuit failures. However, even in nominal scenarios pressurized crew transfer is advantageous, protecting lander and ascent spacecraft as well as launch and entry spacesuits from dust exposure and aiding in adaptation to gravity following prolonged periods of microgravity. Several examples of lunar and Mars lander or ascent spacecraft have been released publicly and can serve as examples of the range of potential vehicles that may benefit from pressurized crew transfer. There are also examples of surface assets that can be used to provide crew transfer, including both current surface mobility or crew transfer concepts and terrestrial examples that could be adapted to use on the Moon or Mars. In all cases, the resulting passageway must be acceptable for use and protected from itself experiencing dust contamination. Key design considerations will be discussed.

Artemis↗
NASA NTRS2021

Summary of AHF 348 POCO Graphite Experiment

This document provides a summary of the data products from the POCO graphite Iso-Q run from AHF 348. POCO graphite is an UNCLASSIFIED material, and therefore this experimental dataset is being considered for public release as thermal response code validation exercise.

Thermal Response↗
NASA NTRSDate not supplied

An Overview of NASA-STD-1008: Classifications and Requirements for Testing Systems and Hardware to be Exposed to Dust in Planetary Environments

As we return to the Moon with Artemis, hardware will inevitably be exposed to lunar dust. Encountering the surface of a dusty planetary body brings significant hazards to the hardware. A team of subject matter experts across NASA were brought together to create a standard for addressing the best means of performing ground testing of hardware and systems to guard against the expected negative effects that dust may bring. This effort resulted in NASA-STD-1008: Classifications and Requirements for Testing Systems and Hardware to be Exposed to Dust in Planetary Environments. The NASA Technical Standard went through an Agency wide review and was approved for public release in September 2021.

lunar dust↗
NASA NTRS2022

Moon to Mars (M2M) Habitation Considerations: A Snap Shot As of January 2022

The following NASA Technical Memorandum (TM) is intended to provide a snapshot in time of NASA’s current considerations (ground rules and assumptions, functional allocations, logistics) for habitation systems for the lunar surface (non-roving) and Mars transits. As NASA continues to refine the reference designs to meet the needs of an evolving architecture, it is expected that this information will also be updated as a result. Where appropriate, relevant publicly released documents will be referenced to provide further detail. NASA’s human lunar exploration plan under the Artemis program calls for achieving the goal of sending the first woman and first person of color to the surface of the Moon in the mid- 2020s and working toward sustainable exploration by the end of the decade. Working with both commercial and international partners, NASA will establish a permanent human presence on the Moon to uncover new scientific discoveries and lay the foundation for private companies to build a lunar economy. Longer duration missions on the lunar surface and in lunar orbit will also serve as a test bed for technologies to support future Mars exploration campaigns. The agency will use what we learn on the Moon to prepare for humanity's next giant leap – sending astronauts to Mars. NASA intends to establish a sustained lunar presence with the development of the Artemis Base Camp to prove technologies and capabilities that will one day enable humans to live and work on Mars, beginning with core elements including the Lunar Terrain Vehicle (LTV), the Pressurized Rover (PR), the lunar Surface Habitat (SH), power systems, and in-situ resource utilization (ISRU) systems. For in space operations and eventual transport of humans to Mars, NASA will utilize a Mars Transit Habitat (TH). Following deployment, the TH will complete a series of longer duration missions and shakedown testing while docked at Gateway, leveraging Gateway’s habitation redundancy for safety measures. Proposed Gateway-TH missions will far exceed the longest duration cislunar human missions to date. They will be the first operational readiness tests of our long-duration deep space systems, and of the split crew (two crew on the surface, two crew in space) operations that are vital to the approach for the first human Mars mission. Both the SH and TH are major architectural elements of NASA’s Moon to Mars (M2M) approach, each with very different concepts of operation. The SH is intended for use on the lunar surface as a home for astronauts, surface operations base, science facility, hub for communications, extravehicular activity (EVA) equipment repair site, waste processing facility, and supply hub. It serves as an enabler for a sustained surface presence and preparation for partial gravity operations during Mars missions. The SH will be designed to be self-sufficient for operations on the lunar surface. The SH will independently provide several functions, including its own power generation, energy storage capability, sleep quarters, hygiene areas, work areas, and dining areas. It will be capable of communicating with surface assets, orbital assets, and directly with Earth ground stations. It is planned to operate with two crew in the habitat for ~28- day stays with crew swap-outs in which the PR crew of two trades places with the habitat crew. During the swap-out, the habitat will nominally support four crew for a short period of time. For contingency scenarios, the habitat must also be capable of supporting four crew for up to 7 days. The TH will be designed to be capable of up to ~1,200-day Mars missions with the ability to carry all food and supplies needed to support a crew of four for this duration. An assumed Mars mission profile for the TH is to carry crew and supplies for ~850-day roundtrips between Earth and Mars orbit that allows 30-day stays on the Martian surface. To test the systems for this long journey, the TH will be used to extend the duration of missions at Gateway, enabling the orbiting outpost to be used as a Mars analog. These analog missions will be accomplished by attaching the TH to Gateway and conducting lunar surface operations from the TH. The TH may also need to perform free-flying shakedown missions to test out all systems prior to leaving for Mars. The habitat provides many critical functions including: a contingency airlock, crew quarters, galley, hygiene areas, safe haven capability, and science equipment. It can receive docked items from either axial end or on a radial port.

Habitat↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry, Descent, and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRS2022

Supporting Responsible Machine Learning in Heliophysics

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.

Machine learning↗
NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗
NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗
NASA NTRS2023

The Trans-Heliospheric Survey - Radial Trends in Plasma Parameters Across the Heliosphere

Context. Though the solar wind is characterized by spatial and temporal variability across a wide range of scales, long-term averages of in situ measurements have revealed clear radial trends: changes in average values of basic plasma parameters (e.g., density, temperature, and speed) and a magnetic field with a distance from the Sun. Aims. To establish our current understanding of the solar wind's average expansion through the heliosphere, data from multiple spacecraft needed to be combined and standardized into a single dataset. Methods. In this study, data from twelve heliospheric and planetary spacecraft - Parker Solar Probe (PSP), Helios 1 and 2, Mariner 2 and 10, Ulysses, Cassini, Pioneer 10 and 11, New Horizons, and Voyager 1 and 2 - were compiled into a dataset spanning over three orders of magnitude in heliocentric distance. To avoid introducing artifacts into this composite dataset, special attention was given to the solar cycle, spacecraft heliocentric elevation, and instrument calibration. Results. The radial trend in each parameter was found to be generally well described by a power-law fit, though up to two break points were identified in each fit. Conclusions. These radial trends are publicly released here to benefit research groups in the validation of global heliospheric simulations and in the development of new deep-space missions such as Interstellar Probe.

Bennett A Maruca↗