Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sub scale”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Tropospheric Emissions: Monitoring of Pollution Overview

TEMPO is now well into its implementation phase, having passed both its Key Decision Point C and the Critical Design Review (CDR) for the instrument. The CDR for the ground systems will occur in March 2016 and the CDR for the Mission component at a later date, after the host spacecraft has been selected. TEMPO is on schedule to measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions by 50 percent. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Chance, Kelly↗
NASA NTRS2016

Airborne Spectral BRDF of Various Surface Types (Ocean, Vegetation, Snow, Desert, Wetlands, Cloud Decks, Smoke Layers) for Remote Sensing Applications

In this paper we describe measurements of the bidirectional reflectance-distribution function (BRDF) acquired over a 30-year period (1984-2014) by the National Aeronautics and Space Administration's (NASA's) Cloud Absorption Radiometer (CAR). Our BRDF database encompasses various natural surfaces that are representative of many land cover or ecosystem types found throughout the world. CAR's unique measurement geometry allows a comparison of measurements acquired from different satellite instruments with various geometrical configurations, none of which are capable of obtaining such a complete and nearly instantaneous BRDF. This database is therefore of great value in validating many satellite sensors and assessing corrections of reflectances for angular effects. These data can also be used to evaluate the ability of analytical models to reproduce the observed directional signatures, to develop BRDF models that are suitable for sub-kilometer-scale satellite observations over both homogeneous and heterogeneous landscape types, and to test future spaceborne sensors. All of these BRDF data are publicly available and accessible in hierarchical data format (http:car.gsfc.nasa.gov/).

albedo↗
NASA NTRS2016

Hollow-Core Fiber Lamp

Hollow-core capillary discharge lamps on the millimeter or sub-millimeter scale are provided. The hollow-core capillary discharge lamps achieve an increased light intensity ratio between 194 millimeters (useful) and 254 millimeters (useless) light than conventional lamps. The capillary discharge lamps may include a cone to increase light output. Hollow-core photonic crystal fiber (HCPCF) may also be used.

Yi, Lin↗
NASA NTRS2016

Full Field X-Ray Fluorescence Imaging Using Micro Pore Optics for Planetary Surface Exploration

Many planetary surface processes leave evidence as small features in the sub-millimetre scale. Current planetary X-ray fluorescence spectrometers lack the spatial resolution to analyse such small features as they only provide global analyses of areas greater than 100 mm(exp 2). A micro-XRF spectrometer will be deployed on the NASA Mars 2020 rover to analyse spots as small as 120m. When using its line-scanning capacity combined to perpendicular scanning by the rover arm, elemental maps can be generated. We present a new instrument that provides full-field XRF imaging, alleviating the need for precise positioning and scanning mechanisms. The Mapping X-ray Fluorescence Spectrometer - "Map-X" - will allow elemental imaging with approximately 100μm spatial resolution and simultaneously provide elemental chemistry at the scale where many relict physical, chemical and biological features can be imaged in ancient rocks. The arm-mounted Map-X instrument is placed directly on the surface of an object and held in a fixed position during measurements. A 25x25 mm(exp 2) surface area is uniformly illuminated with X-rays or alpha-particles and gamma-rays. A novel Micro Pore Optic focusses a fraction of the emitted X-ray fluorescence onto a CCD operated at a few frames per second. On board processing allows measuring the energy and coordinates of each X-ray photon collected. Large sets of frames are reduced into 2d histograms used to compute higher level data products such as elemental maps and XRF spectra from selected regions of interest. XRF spectra are processed on the ground to further determine quantitative elemental compositions. The instrument development will be presented with an emphasis on the characterization and modelling of the X-ray focussing Micro Pore Optic. An outlook on possible alternative XRF imaging applications will be discussed.

micro-XRF spectrometer↗
NASA NTRS2017

Model Validation for Propulsion - On the TFNS and LES Subgrid Models for a Bluff Body Stabilized Flame

This paper summarizes the reacting results of simulating a bluff body stabilized flame experiment of Volvo Validation Rig using a releasable edition of the National Combustion Code (NCC). The turbulence models selected to investigate the configuration are the sub-grid scaled kinetic energy coupled large eddy simulation (K-LES) and the time-filtered Navier-Stokes (TFNS) simulation. The turbulence chemistry interaction used is linear eddy mixing (LEM).

Flame↗
NASA NTRS2016

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO (Tropospheric Emissions: Monitoring of Pollution) was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (approximately 2.1 kilometers N/S by 4.4 kilometers E/W at 36.5 degrees N, 100 degrees W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

tropospheric measurement suite↗
NASA NTRS2017

From Tomography to Material Properties of Thermal Protection Systems

A NASA Ames Research Center (ARC) effort, under the Entry Systems Modeling (ESM) project, aims at developing micro-tomography (micro-CT) experiments and simulations for studying materials used in hypersonic entry systems. X-ray micro-tomography allows for non-destructive 3D imaging of a materials micro-structure at the sub-micron scale, providing fiber-scale representations of porous thermal protection systems (TPS) materials. The technique has also allowed for In-situ experiments that can resolve response phenomena under realistic environmental conditions such as high temperature, mechanical loads, and oxidizing atmospheres. Simulation tools have been developed at the NASA Ames Research Center to determine material properties and material response from the high-fidelity tomographic representations of the porous materials with the goal of informing macroscopic TPS response models and guiding future TPS design.

Tomography↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2011

Fabrication and Test of an Optical Magnetic Mirror

Traditional mirrors at optical wavelengths use thin metalized or dielectric layers of uniform thickness to approximate a perfect electric field boundary condition. The electron gas in such a mirror configuration oscillates in response to the incident photons and subsequently re-emits fields where the propagation and electric field vectors have been inverted and the phase of the incident magnetic field is preserved. We proposed fabrication of sub-wavelength-scale conductive structures that could be used to interact with light at a nano-scale and enable synthesis of the desired perfect magnetic-field boundary condition. In a magnetic mirror, the interaction of light with the nanowires, dielectric layer and ground plate, inverts the magnetic field vector resulting in a zero degree phase shift upon reflection. Geometries such as split ring resonators and sinusoidal conductive strips were shown to demonstrate magnetic mirror behavior in the microwave and then in the visible. Work to design, fabricate and test a magnetic mirror began in 2007 at the NASA Goddard Space Flight Center (GSFC) under an Internal Research and Development (IRAD) award Our initial nanowire geometry was sinusoidal but orthogonally asymmetric in spatial frequency, which allowed clear indications of its behavior by polarization. We report on the fabrication steps and testing of magnetic mirrors using a phase shifting interferometer and the first far-field imaging of an optical magnetic mirror.

Hagopian, John G.↗
NASA NTRS2018

Star & Planet Formation Studies and Opportunities with SOFIA

Star formation, the most fundamental process in the universe, is linked to planet formation and thus to the origin and evolution of life. We have a general outline of how planets and stars form, yet unraveling the details of the physics and chemistry continues to challenge us. The infrared and submillimeter part of the spectrum hold the most promise for studying the beginnings of star formation. The observational landscape recently shaped by Spitzer, Herschel and ALMA, continues to challenge our current theories. SOFIA, the Stratospheric Observatory for Infrared Astronomy, equipped with state-of-the-art infrared instrumentation to a vantage point at 45,000 feet (13.7 kilometers) flight altitude that is above 99.9 percent of the Earth's water vapor, enables observations in the infrared through terahertz frequencies not possible from the ground. SOFIA is a community observatory, about to start its sixth annual observing cycle. My talk will focus on recent results in advancing star and planet formation processes using SOFIAʼs imaging and polarimetric capabilities, and the upcoming science enabled by the 3rd generation instrument High-Resolution Mid-Infrared Spectrometer (HIRMES) to be commissioned in 2019. I will show how mid-infrared imaging is used to test massive star formation theories, how far-infrared polarimetry on sub-parsec scales is directly testing the role of magnetic fields in molecular clouds, and how velocity-resolved high-resolution spectroscopy will push forward our understanding of proto-planetary disk science. I will also summarize upcoming opportunities with the SOFIA observatory. For the latest news about your flying observatory, see https://sofia.usra.edu/.

Star formation↗
NASA NTRS2018

Field Exploration and Life Detection Sampling for Planetary Analogue Research (FELDSPAR): Variability and Correlation in Biomarker and Mineralogy Measurements from Icelandic Mars Analogues

In situ exploration of planetary environments allows biochemical analysis of sub-centimeter-scale samples; however, landing sites are selected a priori based on measurable meter- to kilometer-scale geological features. Optimizing life detection mission science return requires both understanding the expected biomarker distributions across sample sites at different scales and efficiently using first-stage in situ geochemical instruments to justify later-stage biological or chemical analysis. Icelandic volcanic regions have an extensive history as Mars analogue sites due to desiccation, low nutrient availability, and temperature extremes, in addition to the advantages of geological youth and isolation from anthropogenic contamination. Many Icelandic analogue sites are also rugged and remote enough to create the same type of instrumentation and sampling constraints typically faced by robotic exploration.

Planetary↗
NASA NTRS2018

Two-thirds of Global Cropland Area Impacted by Climate Oscillations

The El Nino Southern Oscillation (ENSO) peaked strongly during the boreal winter 2015-2016, leading to food insecurity in many parts of Africa, Asia and Latin America. Besides ENSO, the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) are known to impact crop yields worldwide. Here we assess for the first time in a unified framework the relationship between ENSO, IOD and NAO and simulated crop productivity at the sub-country scale. Our findings reveal that during 1961-2010, crop productivity is significantly influenced by at least one large-scale climate oscillation in two-thirds of global cropland area. Besides observing new possible links, especially for NAO in Africa and the Middle East, our analyses confirm several known relationships between crop productivity and these oscillations. Our results improve the understanding of climatological crop productivity drivers, which is essential for enhancing food security in many of the most vulnerable places on the planet.

IOD↗
NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2019

WRLES: Wave Resolving Large-Eddy Simulation Code, Theory and Usage

A computational fluid dynamics code has been developed for large-eddy simulations (LES) of turbulent flow. The code uses high-order of accuracy and high-resolution numerical methods to minimize solution error and maximize the resolution of the turbulent structures. Spatial discretization is performed using explicit central differencing. The central differencing schemes in the code include 2nd- to 12th-order standard central difference methods as well as 7-, 9-, 11- and 13-point dispersion relation preserving schemes. Solution filtering and high-order shock capturing are included for stability. Time discretization is performed using multistage Runge-Kutta methods that are up to 4th order accurate. Several options are available to model turbulence including: Baldwin-Lomax and Spalart-Allmaras Reynolds-averaged Navier-Stokes turbulence models, and Smagorinsky, Dynamic Smagorinsky and Vreman sub-grid scale models for LES. This report presents the theory behind the numerical and physical models used in the code and provides a user's manual to the operation of the code.

High-Order↗
NASA NTRS2019

The Space Debris Sensor Experiment

The Space Debris Sensor (SDS) is a NASA Class 1E technology demonstration external payload aboard the International Space Station (ISS). With approximately one square meter of detection area, the SDS is attached to the European Space Agency Columbus module facing the ISS velocity vector with minimal obstruction from ISS hardware. The SDS is the first flight demonstration of the Debris Resistive/Acoustic Grid Orbital NASA-Navy Sensor (DRAGONS) technology developed and matured over 10 years by the NASA Orbital Debris Program Office (ODPO), in concert with the DRAGONS consortium, to provide information on the sub-millimeter scale orbital debris environment. The SDS demonstrated the capacity to read 4 resistive grids at 1 Hz, 40 acoustic sensors at 500 kHz, and record and downlink impact data to the ground. Observable and derived data from the SDS could provide information to models that are critical to understanding risks the small debris environment poses to spacecraft in low Earth orbit. The technology demonstrated by the SDS is a major step forward in monitoring and characterizing the space debris environment. This paper will address the technical performance of the SDS during its operational lifetime and its realization of technical and scientific goals. The SDS was intended to operate for 3 years; however, the payload incurred multiple anomalies during its operational life. Subsequently termed “Anomaly #1,” the first was the symptomatic loss of low data rate 1553 channel command and telemetry. The second, Anomaly #2, was loss of all low- and medium-data rate (Ethernet) telemetry. Anomaly #2 proved to be unrecoverable, leading to loss of the payload after approximately 26 days on-board the ISS. Therefore, this paper also addresses the anomalies that occurred during operation of the SDS, their attribution, and their resolution. Lessons learned are described when relevant to anomaly identification, attribution, and resolution.

Anz-Meador, P.↗
NASA NTRS2016

Onboard Data Analysis for the Planetary Instrument for X-Ray Lithography

Imaging spectrometers are invaluable instruments for robotic science exploration, enabling quantitative maps of physical and chemical properties at high spatial resolution. This is particularly valuable in remote missions to other planetary bodies like Mars. The PIXL instrument on the Mars 2020 rover will deploy an arm-mounted X-Ray fluorescence spectrometer to map chemical composition at sub-millimeter scales. Its high resolution places dramatic new demands on instrument placement accuracy and measurement time. We address these challenges using novel on board data analysis strategies inspired by FRC science autonomy research.

Thompson, David R.↗