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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Discoveries about Tropical Ozone from Satellite and SHADOZ (Southern Hemisphere ADditional Ozonesondes)

We have been producing near-real tropical tropospheric ozone ("TTO") data from TOMS since 1997 with Prof. Hudson and students at the University of Maryland. Maps for 1996-2000 for the operational Earth-Probe instrument reside at: . The tropics is a region strongly influenced by natural variability and anthropogenic activity and the satellite data have been used to track biomass burning pollution and to detect interannual variability and climate signals in ozone. The satellite view of chemical-dynamical interactions in tropospheric ozone is not adequate to capture vertical variability. Thus, in 1998, NASA's Goddard Space Flight Center, NOAA's Climate Monitoring and Diagnostics Laboratory (CMDL) and a team of international sponsors established the SHADOZ (Southern Hemisphere ADditional OZonesondes) project to address the gap in tropical ozone soundings. SHADOZ provides a public archive of ozonesonde data from twelve tropical and subtropical stations at http://croc.gsfc.nasa.gov/shadoz. Highlights from the first 4-5 years of SHADOZ data (presently > 2000 sondes) will be shown.

Thompson, Anne M.↗
NASA NTRS2006

Behavioral Health Program Element

The project goal is to develop behavioral health prevention and maintenance system for continued crew health, safety, and performance for exploration missions. The basic scope includes a) Operationally-relevant research related to clinical cognitive and behavioral health of crewmembers; b) Ground-based studies using analog environments (Antarctic, NEEMO, simulations, and other testbeds; c) ISS studies (ISSMP) focusing on operational issues related to behavioral health outcomes and standards; d) Technology development activities for monitoring and diagnostic tools; and e) Cross-disciplinary research (e.g., human factors and habitability research, skeletal muscle, radiation).

Leveton, Lauren B.↗
NASA NTRS2007

Advances in Ground Transmitters for the NASA Deep Space Network

The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, is equipped with multiple microwave transmitters ranging in average radiated power from 200 W to 400 kW. The transmitters are used for routine or emergency communication with spacecraft, for navigation, and for radio science tasks. The latest advances in transmitter engineering were implemented in a new generation of 20-kW dual-band transmitters developed for the DSN 34-m beam waveguide antennas. Innovations include additional X-band communication capability for near Earth missions, new control algorithms, automated calibration, improved and expanded computerized monitoring and diagnostics, reduced cabling, and improved maintainability. The innovations were very beneficial for the DSN 'overload' during the Mars 2003/2004 missions and will benefit other missions throughout the next decade. This paper describes the current design of the new transmitters and possible future developments.

microwaves↗
NASA NTRS2010

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 6 - Development and Demonstration of a Self-Organizing Diagnostic System for Structural Health Monitoring

This report describes a significant advance in the capability of the CSIRO/NASA structural health monitoring Concept Demonstrator (CD). The main thrust of the work has been the development of a mobile robotic agent, and the hardware and software modifications and developments required to enable the demonstrator to operate as a single, self-organizing, multi-agent system. This single-robot system is seen as the forerunner of a system in which larger numbers of small robots perform inspection and repair tasks cooperatively, by self-organization. While the goal of demonstrating self-organized damage diagnosis was not fully achieved in the time available, much of the work required for the final element that enables the robot to point the video camera and transmit an image has been completed. A demonstration video of the CD and robotic systems operating will be made and forwarded to NASA.

Batten, Adam↗
NASA NTRS2012

Dust-Tolerant Intelligent Electrical Connection System

Faults in wiring systems are a serious concern for the aerospace and aeronautic (commercial, military, and civilian) industries. Circuit failures and vehicle accidents have occurred and have been attributed to faulty wiring created by open and/or short circuits. Often, such circuit failures occur due to vibration during vehicle launch or operation. Therefore, developing non-intrusive fault-tolerant techniques is necessary to detect circuit faults and automatically route signals through alternate recovery paths while the vehicle or lunar surface systems equipment is in operation. Electrical connector concepts combining dust mitigation strategies and cable diagnostic technologies have significant application for lunar and Martian surface systems, as well as for dusty terrestrial applications. The dust-tolerant intelligent electrical connection system has several novel concepts and unique features. It combines intelligent cable diagnostics (health monitoring) and automatic circuit routing capabilities into a dust-tolerant electrical umbilical. It retrofits a clamshell protective dust cover to an existing connector for reduced gravity operation, and features a universal connector housing with three styles of dust protection: inverted cap, rotating cap, and clamshell. It uses a self-healing membrane as a dust barrier for electrical connectors where required, while also combining lotus leaf technology for applications where a dust-resistant coating providing low surface tension is needed to mitigate Van der Waals forces, thereby disallowing dust particle adhesion to connector surfaces. It also permits using a ruggedized iris mechanism with an embedded electrodynamic dust shield as a dust barrier for electrical connectors where required.

Lewis, Mark↗
NASA NTRS2011

Accelerated Aging Experiments for Prognostics of Damage Growth in Composite Materials

Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.

composites↗
NASA NTRS2014

Chemistry Characterization of Jet Aircraft Engine Particulate by XPS: Results from APEX III

This paper reports XPS analysis of jet exhaust particulate from a B737, Lear, ERJ, and A300 aircraft during the APEX III NASA led field campaign. Carbon hybridization and bonding chemistry are identified by high-resolution scans about the C1s core-shell region. Significant organic content as gauged by the sp3/sp2 ratio is found across engines and platforms. Polar oxygen functional groups include carboxylic, carbonyl and phenol with combined content of 20 percent or more. By lower resolution survey scans various elements including transition metals are identified along with lighter elements such as S, N, and O in the form of oxides. Burning additives within lubricants are probable sources of Na, Ba, Ca, Zn, P and possibly Sn. Elements present and their percentages varied significantly across all engines, not revealing any trend or identifiable cause for the differences, though the origin is likely the same for the same element when observed. This finding suggests that their presence can be used as a tracer for identifying soots from aircraft engines as well as diagnostic for monitoring engine performance and wear.

soot↗
NASA NTRS2015

Nanoscale Test Strips for Multiplexed Blood Analysis

A critical component of the DNA Medicine Institute's Reusable Handheld Electrolyte and Lab Technology for Humans (rHEALTH) sensor are nanoscale test strips, or nanostrips, that enable multiplexed blood analysis. Nanostrips are conceptually similar to the standard urinalysis test strip, but the strips are shrunk down a billionfold to the microscale. Each nanostrip can have several sensor pads that fluoresce in response to different targets in a sample. The strips carry identification tags that permit differentiation of a specific panel from hundreds of other nanostrip panels during a single measurement session. In Phase I of the project, the company fabricated, tested, and demonstrated functional parathyroid hormone and vitamin D nanostrips for bone metabolism, and thrombin aptamer and immunoglobulin G antibody nanostrips. In Phase II, numerous nanostrips were developed to address key space flight-based medical needs: assessment of bone metabolism, immune response, cardiac status, liver metabolism, and lipid profiles. This unique approach holds genuine promise for space-based portable biodiagnostics and for point-of-care (POC) health monitoring and diagnostics here on Earth.

Chan, Eugene↗
NASA NTRS2019

Oculometric Assessment of Mild Neural Impairment

For over 100 years, neurologists have used eye movements to identify neural impairment, disease, or injury. Prior to the age of modern imaging, qualitative assessment of eye movements was a critical, routine component of diagnosis and remains today a routine law-enforcement tool for detecting impaired driving due to drugs or alcohol. We will describe the application of a simple 5-minute oculomotor tracking task coupled with a broad range of quantitative analyses of high-resolution oculomotor measurements for the sensitive detection of sub-clinical neural impairment and for the potential differentiation of various causes. Specifically, we will show that there are distinct patterns of impairment across our set of oculometric parameters observed with brain trauma, sleep and circadian disruption, and alcohol consumption. Such differences could form the basis of a self-administered medical monitoring or diagnostic support tool.

Stone, Lee↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33601-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1? gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation.

Moguo Sun↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33753-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1 gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation. The paper will also discuss CERES FlxbyCldTyp simulator and its application.

Moguo Sun↗
NASA NTRSDate not supplied

TPSAS-NF1676L-30044-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 17 years accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. The CERES FluxByCldTyp dataset, which contains cloud properties and radiative fluxes for 42 cloud types sorted by cloud top pressure and cloud optical depth, is used to investigate the clouds and their associated TOA (top-of-the-atmosphere) fluxes changes over the tropical area during ENSO events during the observed period. Unlike past studies, this study shows the impact of ENSO on cloud properties like optical depth, cloud top effective pressure and temperature and TOA LW and SW fluxes for each sub cloud type. The study reveals the detailed contributions from different cloud types for radiative characteristics during different regimes of ENSOs. This is especially important for very small net TOA radiative balance due to the cancellation of the fluxes from different cloud types. The dataset serves as a more stringent validation of climate models for cloud properties and radiative fluxes.

Sun, Moguo↗
NASA NTRSDate not supplied

CERES FluxByCldTyp NB2BB Fluxes Improvement based on Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

ENSO Tropical Cloud and TOA radiative signatures from the CERES observation

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 15 years accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. The CERES flux-by-cloud-type dataset, which contains cloud properties and radiative fluxes for 42 cloud types sorted by cloud top pressure and cloud optical depth, is used to investigate the clouds and their associated TOA (top-of-the-atmosphere) fluxes changes over the tropical area during ENSO events during the observed period. Unlike past studies, this study shows the impact of ENSO on cloud properties like optical depth, cloud top effective pressure and temperature and TOA LW and SW fluxes for each sub cloud type. The study reveals the detailed contributions from different cloud types for radiative characteristics during different phases of ENSO. This is especially important for very small net TOA radiative balance due to the cancellation of the fluxes from different cloud types. To further demonstrate the usefulness of this dataset, NCAR Community Atmosphere Model (CAM) is used to simulate the cloud and radiative changes during the period. The model cloud properties are converted to MODIS like cloud properties using modified MODIS simulator. The dataset serves as a more stringent validation of the model for cloud properties and radiative fluxes.

Moguo Sun↗
NASA NTRSDate not supplied

Improving Radiative Fluxes for the CERES FluxByCldTyp Data Product Using Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗