SEARCH · Engineering Papers
Results for “SeN”
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.
Radiance and reflectance algorithms for the AVIRIS-NG India Campaign, 2015-16
UNKNOWN
Interplanetary Alfvénic Tubulence: The Need for Verification and Modeling
UNKNOWN
Optimization of sensor materials using physical vapor transport growth method
Explore the source record for details and available documents.
Design of ZnSe QPM for wide transparency sensing and laser applications
The main objective of this study is to develop template for quasi phase matched approach on silicon and gallium arsenide wafers.
Variations in color and reflectance on the surface of asteroid (101955) Bennu
Visible-wavelength color and reflectance provide information about the geologic history of planetary surfaces. Here we present multispectral images (0.44 to 0.89 micrometers) of near-Earth asteroid (101955) Bennu. The surface has variable colors overlain on a moderately blue global terrain. Two primary boulder types are distinguishable by their reflectance and texture. Space weathering of Bennu surface materials does not simply progress from red to blue (or vice versa). Instead, freshly exposed, redder surfaces initially brighten in the near-ultraviolet region (i.e., become bluer at shorter wavelengths), then brighten in the visible to near-infrared region, leading to Bennu’s moderately blue average color. Craters indicate that the time scale of these color changes is ~105 years. We attribute the reflectance and color variation to a combination of primordial heterogeneity and varying exposure ages.
Leveraging STARE for Co-aligned Data Locality with netCDF and Python MPI
We have leveraged STARE indexing to package partitioned data chunks from diverse datasets into netCDF files, distributed them on a cluster of 16 lightweight nodes with their placements spatiotemporally co-aligned, and demonstrated a few integrative analyses using netCDF parallel I/O and Python MPI, with single-user performance and scalability comparable to, or even better than, that of a parallel array database management system (ADBMS) such as SciDB. However, records of the node location and STARE index ranges for each data chunk, similar to the chunk maps of SciDB, must be maintained and consulted by the I/O and analysis code for coordinating the analytic operations in parallel, in order to achieve the good performance and scalability.
Design of ZnSe QPM for Wide Transparency Sensing and Laser Applications
ZnSe has been a great choice for the rare-earth and transition metal doping to develop lasers. It is an excellent materialfor variety of optical applications due to wide transparency range, good fabricability and very low optical absorptionsimilar to other selenides. NASA Marshall Space Flight Center has developed large crystals using physical vapordeposition (PVD) doped with transition metals for lasing. GaAs based quasi-phase matched structures have a lot oflimitations including difficulty of frequency conversion from available high power lasers. We are developing Si- andGaAs- based templates and using microfabrication process to deposit ZnSe using physical vapor transport (PVT)method. Experimental results of the fabrication of templates and growth of ZnSe on templates will be presented.
Towards a Mass-Consistent Methodology for Realistic Melting Hydrometeor Retrieval
To address the acute challenge posed by the melting layer to accurate surface precipitation retrievals from space, we en-sure the compositional consistency in ice, liquid, and total masses of synthetic melting hydrometeors with a method of stochastic compensation. The method is applied to simulated melting hydrometeors prior to calculating their scattering properties using the discrete dipole approximation (DDA). We investigate the impact of this stochastic compensation to calculated scattering properties by contrasting it with a naïve approach and report our findings.
Spectral Effects of Varying Texture and Composition in Two-component “Mudpie” Simulations: Insights for Asteroid (101955) Bennu
Data returned by the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) spacecraft have shown that asteroid (101955) Bennu has a globally low-albedo surface covered in boulders with diverse texture, color, and albedo properties, and an aqueously altered composition dominated by phyllosilicates. To test whether Bennu’s color and albedo diversity could be caused by texture and/or composition variations, we performed a laboratory-based study using simple two-component mixtures (called “mudpies”) of the phyllosilicate saponite and carbon-rich opaques. Each mudpie is prepared in four different textures: fine powder, coarse particles, sanded slab, and textured rock. We find that a sanded slab made from 90% saponite and 10% lampblack is a good analog for Bennu, and the color and albedo changes due to texture variations are substantial. At 550 nm, texture changes alone can create up to 36% brightness contrast, and in color measured as a 473 nm/847 nm ratio, texture changes can provide up to 18% color contrast. In comparison, Bennu shows approximately 25% albedo and <1% color contrasts from boulder type to boulder type. These findings suggest that if texture contributes to color on Bennu, the texture variations are typically more subtle than what we simulated in the laboratory. According to our study, the color and albedo properties of different boulder types on Bennu are consistent with different concentrations of carbon-rich opaques (and possibly consistent with variations in carbonate concentration). The variations within each boulder group are consistent with textural differences.
Evaluation of Higher-order Quadrature Schemes in Improving Computational Efficiency for Orientation-averaged Single-Scattering Properties of Nonspherical Ice Particles
We evaluate several high-order quadrature schemes for accuracy and efficacy in obtaining orientation-averaged single-scattering properties (SSPs). We use the highly efficient MIDAS to perform electromagnetic scattering calculations to evaluate the gain in efficiency from these schemes. MIDAS is shown to be superior to DDSCAT, a popular discrete dipole approximation (DDA) method. This study is motivated by the fact that quality physical precipitation retrievals rely on using accurate orientation-averaged SSPs derived from realistic hydrometeors as input to radiative transfer Models (RTMs). The DDA has been a popular choice for single-scattering calculations, due to its versatility with respect to target geometry. However, being iterative-solver-based (ISB), the most used DDA codes, e.g. DDSCAT and ADDA, must solve the scattering problem for each orientation of the target separately. As the size parameter and geometric anisotropy of the hydrometeor increase, the number of orientations needed to obtain accurate orientation-averages can increase drastically and so does the computation cost incurred by the ISB-DDA methods. MIDAS is a Direct-Solver-Based (DSB) code, its decomposition of the original large matrix with a high rank into multiple more manageable smaller matrices of lower ranks makes it much more computationally efficient while maintaining excellent accuracy. In addition, direct solvers consider all requested orientations at once, giving MIDAS further advantage over popular ISB-DDA methods. MIDAS, when combined with high-order quadrature for orientation averaging, can be greater than three orders of magnitude more efficient in obtaining RTM-ready SSPs of complex-shaped hydrometeors than existing ISB-DDA methods, with the native quadrature schemes they offer.
Sensitivity of Single-Scattering Properties to Precipitation Particle Meltwater Geometry for GPM Passive Microwave and Radar Remote Sensing Applications
The objective of the current work is to describe the microwave single-scattering properties of partially melted ice-phase precipitation particles using physically-based computational methods, taking into full account the varied geometries of the initially “dry” ice particles and the distributions of liquid water that develop during the melting process. Ultimately, the bulk properties of ensembles of these partially-melted particles will be included in “scattering tables” to support radar and combined radar-radiometer remote sensing of precipitation. A meshless Lagrangian melting procedure (snowMELT) based on smoothed-particle hydrodynamics is applied to both spherical and finely-structured ice particle models to describe the full evolution of the particles from dry ice particles to liquid drops. The melting of spherical ice particles using snowMELT is compared to an alternative continuum physics model to validate melt times and internal thermodynamics. The discrete dipole approximation is then utilized to calculate the single-scattering properties of different mixed-phase particles throughout the melting process. The sensitivities of particle single-scattering properties to thermodynamic and hydrodynamic assumptions in snowMELT are explored, and the implications for combined radar-radiometer precipitation remote sensing from GPM are discussed.
Micro/Nanomachines And Soft Active Matter in Zero-Gravity
Explore the source record for details and available documents.
The Earth Surface Mineral Dust Source Investigation: An Earth Science Imaging Spectroscopy Mission
The Earth Surface Mineral Dust Source Investigation, EMIT, is planned to operate from the International Space Station starting no earlier than the fall of 2021. EMIT will use visible to short wavelength infrared imaging spectroscopy to determine the mineral composition of the arid land dust source regions of the Earth to advance our knowledge of the radiative forcing effect of these aerosols. Mineral dust emitted into the atmosphere under high wind conditions is an element of the Earth system with many impacts to the Earth’s energy balance, atmosphere, surface, and oceans. The Earth’s mineral dust cycle with source, transport, and deposition phases are studied with advanced Earth System Models. Because the chemical composition, optical and surface properties of soil particles vary strongly with the mineral composition of the source, these models require knowledge of surface soil mineral dust source composition knowledge to accurately understand dust impacts on the Earth system now and in the future. At present, compositional knowledge of the Earth’s mineral dust source regions from existing data sets is uncertain as a result of limited measurements. EMIT will use spectroscopically derived surface mineral composition to update the prescribe boundary conditions for state-of-the-art Earth System Models. The EMIT-initialized models will be used to investigate the impact of direct radiative forcing in the Earth system that depends strongly on the composition of the mineral dust aerosols emitted into the atmosphere. These new measurements and related products will be used to address the EMIT science objectives and made available to the science community for additional investigations. An overview of the EMIT science, development, and mission is presented in this paper.
Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers
Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.
All optical methods to evaluate defects and performance of sensor materials
To develop fast and low cost all optical methods to estimate defects in sensor materials.
Perovskites for chemical and biological sensing
Explore the source record for details and available documents.