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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

A Coordinated Microstructural and Isotopic Study ofa Wark-Lovering Rim on a Vigarano CAI

We carried out a coordinated mineralogical and isotopic study of a Wark-Lovering (WL) rim on a Ca,Al-rich inclusion (CAI) from the reduced CV3 chondrite Vigarano. The outermost edge of the CAI mantle is mineralogically and texturally distinct compared to the underlying mantle that is composed of coarse, zoned melilite (Åk~10-60) grains. The mantle edge contains fine-grained gehlenite with hibonite and rare grossite that likely formed by rapid crystallization from a melt enriched in Ca and Al. These gehlenite and hibonite layers are surrounded by successive layers of spinel, zoned melilite (Åk~0-10), zoned diopside that grades outwards from Al,Ti-rich to Al,Ti-poor, and forsteritic olivine intergrown with diopside. These layered textures are indicative of sequential condensation of spinel, melilite, diopside, and forsterite onto hibonite. Anorthite occurs as a discontinuous layer that corrodes adjacent melilite and Al2 diopside, and appears to have replaced them, probably even later than the forsterite layer formation. Based on these observations, we conclude that the WL rim formation was initiated by flash melting and extensive evaporation of the original inclusion edge, followed by subsequent gas-solid reactions under highly dynamic conditions. All the WL rim minerals are 16O-rich (Δ17O = ~−23‰), indicating their formation in an 16O-rich nebular reservoir. Our Al-Mg measurements of hibonite, spinel, and diopside from the WL rim, as well as spinel and Al,Ti-diopside in the core, define a single, well-correlated isochron with an inferred initial 26Al/27Al ratio of (4.94 ± 0.12) × 10−5. This indicates that the WL rim formed shortly after the host CAI. In contrast, the lack of 26Mg excesses in the WL rim anorthite suggest its later formation or later isotopic disturbance in the solar nebula, after 26Al had decayed.

Jangmi Han↗
NASA NTRS1994

Optical Scattering and Microstructure of Ion Beam Deposited Metal Coatings

Thin films of gold and platinum have been deposited onto super-polished fused silica substrates using thermal evaporation, ion assisted deposition (IAD), sputtering and ion assisted sputtering. The influence of ion beam flux, coating material and deposition rate on the films microroughness have been investigated. Coatings of gold and platinum have been bombarded with low energy (10-20 eV) Ar ions from an electron cyclotron resonance (ECR) ion source during deposition. Short range surface microroughness of coated surfaces has been examined using scanning tunneling microscopy (STM) and atomic force microscopy (AFM), while long range surface microroughness has been characterized using an angle resolved optical scatterometer. Results indicate that bombardment with low energy ions causes significant reduction in microroughness of metal coatings.

metal↗
NASA NTRSDate not supplied

Microstructural Analysis Across a Hybrid Inconel 718 Turbopump Inducer Following a Scheduled Heat Treatment Process

Additive Manufacturing (AM) provides a low-cost method to fabricate near net shape complex parts. In this study, a turbopump inducer was fabricated using blown powder direct energy deposition (DED) of Inconel 718 to form the fins on a wrought hub. By utilizing this combination process, more cost-effective methods are possible for complex parts. A series of heat treatments were applied to the assembly per ASTM F330.

Inconel 718↗
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗