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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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24 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Reconstituted Three-Dimensional Interactive Imaging

A method combines two-dimensional images, enhancing the images as well as rendering a 3D, enhanced, interactive computer image or visual model. Any advanced compiler can be used in conjunction with any graphics library package for this method, which is intended to take digitized images and virtually stack them so that they can be interactively viewed as a set of slices. This innovation can take multiple image sources (film or digital) and create a "transparent" image with higher densities in the image being less transparent. The images are then stacked such that an apparent 3D object is created in virtual space for interactive review of the set of images. This innovation can be used with any application where 3D images are taken as slices of a larger object. These could include machines, materials for inspection, geological objects, or human scanning. Illuminous values were stacked into planes with different transparency levels of tissues. These transparency levels can use multiple energy levels, such as density of CT scans or radioactive density. A desktop computer with enough video memory to produce the image is capable of this work. The memory changes with the size and resolution of the desired images to be stacked and viewed.

Hamilton, Joseph↗
NASA NTRS2015

Complete Tem-Tomography: 3D Structure of Gems Cluster

GEMS (glass with embedded metal and sulfide) grains in interplanetary dust particles (IDPs) are considered to be one of the ubiquitous and fundamental building blocks of solids in the Solar System. They have been considered to be interstellar silicate dust that survived various metamorphism or alteration processes in the protoplanetary disk but the elemental and isotopic composition measurements suggest that most of them have been formed in the protoplanetary disk as condensates from high temperature gas. This formation model is also supported by the formation of GEMS-like grains with respect to the size, mineral assemblage, texture and infrared spectrum by condensation experiments from mean GEMS composition materials. Previous GEMS studies were performed only with 2D observation by transmission electron microscopy (TEM) or scanning TEM (STEM). However, the 3D shape and structure of GEMS grains and the spatial distribution of Fe/FeS's has critical information about their formation and origin. Recently, the 3D structure of GEMS grains in ultrathin sections of cluster IDPs was revealed by electron tomography using a TEM/STEM (JEM-2100F, JEOL). However, CT images of thin sections mounted on Cu grids acquired by conventional TEM-tomography are limited to low tilt angles (e. g., less than absolute value of 75 deg. In fact, previous 3D TEM observations of GEMS were affected by some artifacts related to the limited tilt range in the TEM used. Complete tomographic images should be acquired by rotating the sample tilt angle over a range of more than absolute value of 80 deg otherwise the CT images lose their correct structures. In order to constrain the origin and formation process of GEMS grains more clearly, we performed complete electron tomography for GEMS grains. Here we report the sample preparation method we have developed for this study, and the preliminary results.

Matsuno, J.↗
NASA NTRS2017

Creep/Stress Rupture Behavior of 3D Woven SiC/SiC Composites with Sylramic-iBN, Super Sylramic-iBN and Hi-Nicalon-S Fibers at 2700F in Air

To determine the influence of fiber types on creep durability, 3D SiC/SiC CMCs were fabricated with Sylramic-iBN, super Sylramic-iBN and Hi-Nicalon-S fibers and the composite specimens were then tested under isothermal tensile creep at 14820C at 69, 103 and 138 MPa for up to 300hrs in air. The failed specimens were examined by scanning electron microscopy (SEM) and computed tomography (CT) for fracture mode analysis. The creep data of these composites are compared with those of other SiC/SiC composites in the literature. The results of this study will be presented.

failure modes↗
NASA NTRS2010

X-Ray Computed Tomography Inspection of the Stardust Heat Shield

The "Stardust" heat shield, composed of a PICA (Phenolic Impregnated Carbon Ablator) Thermal Protection System (TPS), bonded to a composite aeroshell, contains important features which chronicle its time in space as well as re-entry. To guide the further study of the Stardust heat shield, NASA reviewed a number of techniques for inspection of the article. The goals of the inspection were: 1) to establish the material characteristics of the shield and shield components, 2) record the dimensions of shield components and assembly as compared with the pre-flight condition, 3) provide flight infonnation for validation and verification of the FIAT ablation code and PICA material property model and 4) through the evaluation of the shield material provide input to future missions which employ similar materials. Industrial X-Ray Computed Tomography (CT) is a 3D inspection technology which can provide infonnation on material integrity, material properties (density) and dimensional measurements of the heat shield components. Computed tomographic volumetric inspections can generate a dimensionally correct, quantitatively accurate volume of the shield assembly. Because of the capabilities offered by X-ray CT, NASA chose to use this method to evaluate the Stardust heat shield. Personnel at NASA Johnson Space Center (JSC) and Lawrence Livermore National Labs (LLNL) recently performed a full scan of the Stardust heat shield using a newly installed X-ray CT system at JSC. This paper briefly discusses the technology used and then presents the following results: 1. CT scans derived dimensions and their comparisons with as-built dimensions anchored with data obtained from samples cut from the heat shield; 2. Measured density variation, char layer thickness, recession and bond line (the adhesive layer between the PICA and the aeroshell) integrity; 3. FIAT predicted recession, density and char layer profiles as well as bondline temperatures Finally suggestions are made as to future uses of this technology as a tool for non-destructively inspecting and verifying both pre and post flight heat shields.

McNamara, Karen M.↗
NASA NTRS2015

3D Printing in Zero-G Experiment, In Space Manufacturing (LPS, 4)

The 3D Printing in Zero‐G Experiment has been an ongoing effort for several years. In June 2014 the technology demonstration 3D printer was launched to the International Space Station. In November 2014 the first 21 parts were manufactured in orbit marking the beginning of a paradigm shift that will allow astronauts to be more self‐sufficient and pave the way to larger scale orbital manufacturing. Prior to launch the 21 parts were built on the ground with the flight unit with the same feedstock. These ground control samples are to be tested alongside the flight samples in order to determine if there is a measurable difference between parts built on the ground vs. parts built in space. As of this writing, testing has not yet commenced. Tests to be performed are structured light scanning for volume and geometric discrepancies, CT scanning for density measurement, destructive testing of mechanical samples, and SEM analysis for inter‐laminar adhesion discrepancies. Additionally, an ABS material characterization was performed on mechanical samples built from the same CAD files as the flight and ground samples on different machine / feedstock combinations. The purpose of this testing was twofold: first to obtain mechanical data in order to have a baseline comparison for the flight and ground samples and second to ascertain if there is a measurable difference between machines and feedstock.

Bean, Quincy↗
NASA NTRSDate not supplied

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗