Porous Microstructure Analysis (PuMA)
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Conductivity↗
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Results for “TOMOGRAPHY”
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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Composite Bond Line Measurements Based on A Bayesian Analysis of Flash Thermography Data
For bonded composite materials, an accurate characterization of the adhesive bond line is needed to predict failure modes and fracture toughness. In this paper, bond line thickness was estimated from data obtained using through transmission flash thermography. The forward model that predicts back surface temperature is based on a three layer heat diffusion equation with varying diffusivity and flux boundary conditions. The corresponding inverse problem of estimating bond line thickness from measurement data was solved using a Bayesian approach that assumed Gaussian priors for the bond line thickness and thermal diffusivity of the adherends. Finally, the outputs of the thermography based method were compared to measurements that were collected using a micrometer and ultrasound testing.
Flash thermography↗
Composite Bond Line Measurements Based on a Bayesian Analysis of Flash Thermography Data
For bonded composite materials, an accurate characterization of the adhesive bond line is needed to predict failure modes and fracture toughness. In this paper, bond line thickness was estimated from data obtained using through transmission flash thermography. The forward model that predicts back surface temperature is based on a three layer heat diffusion equation with varying diffusivity and flux boundary conditions. The corresponding inverse problem of estimating bond line thickness from measurement data was solved using a Bayesian approach that assumed Gaussian priors for the bond line thickness and thermal diffusivity of the adherends. Finally, the outputs of the thermography based method were compared to measurements that were collected using a micrometer and ultrasound testing.
Flash thermography↗
Assessment of Segmentation-Induced Deviations of Porosity Metrics in Powder Bed Fusion Additively Manufactured Components
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Nondestructive evaluation↗
Additive Manufacturing Disk Specimen Nondestructive Evaluation Dataset
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Additive Manufacturing↗
Near Infrared In-situ Measurements for the Detection of Keyhole Porosity in Autogenous Ti-6AL-4V Welds
Certification of additive manufactured metal parts requires nondestructive evaluation (NDE) to ensure build quality. NDE can be performed during the build process or post build. For large parts with complex geometries, post build NDE can be challenging. In-situ NDE potentially provides a way to perform the inspection layer by layer. This work explores the use of a high speed near infrared (NIR) camera that is focused in-line with a laser to obtain high spatial and temporal resolution thermal imagery of the melt pool and associated cooling areas. The thermal data is obtained during a laser melting process using Ti-6AL-4V plate and of particular interest is the detection of keyhole porosity. Keyhole porosity can result from non-optimal build conditions, such as excessive laser power at a given laser scanning speed, that creates an entrapped bubble. The NIR measured melt pool and cooling areas are processed to detect keyhole porosity. The results are compared to X-ray CT results for validation.
additive manufacturing↗
Near Infrared In-situ Measurements for the Detection of Keyhole Porosity in Autogenous Ti-6AL-4V Welds
Certification of additive manufactured metal parts requires nondestructive evaluation (NDE) to ensure build quality. NDE can be performed during the build process or post build. For large parts with complex geometries, post build NDE can be challenging. In-situ NDE potentially provides a way to perform the inspection layer by layer. This work explores the use of a high speed near infrared (NIR) camera that is focused in-line with a laser to obtain high spatial and temporal resolution thermal imagery of the melt pool and associated cooling areas. The thermal data is obtained during a laser melting process using a Ti-6Al-4V plate and of particular interest is the detection of keyhole porosity. Keyhole porosity can result from non-optimal build conditions, such as excessive laser power at a given laser scanning speed, that creates an entrapped bubble. The NIR measured melt pool and cooling areas are processed to detect keyhole porosity. The results are compared to X-ray CT results for validation. Small keyhole pores buried deep were not detectable with this technique, however, some larger subsurface elongated pores along with some open surface pores did show some promise for detectability.
additive manufacturing↗
Material Inspectability Assessment Related to Lunar Construction Technology Development
For the building of lunar structures on the Moon using lunar regolith-based construction materials, ensuring their structural integrity and employing methods optimal for such tasks is crucial. In this technical memorandum (TM) the detectability of simulated void defects in a cementitious-based lunar construction material is compared between different nondestructive evaluation (NDE) methods, especially methods which have potential to be developed into space qualified instruments. Primarily, simulated voids, introduced during fabrication as foam spheres, were used for this evaluation. Furthermore, smaller, unintended void defects were used to assess the detectability. The results highlights trade-offs between the NDE methods in terms of detection limit, correct size indication, penetration depth, and potential for reduction in size, weight, and power (SWaP).
Nondestructive Evaluation↗
Quantitative Characterization of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans
NASA's development of advanced 3D woven carbon composites, such as HEEET and 3MDCP, represents a significant leap in materials designed to endure extreme atmospheric entry conditions. However, the internal complexity of these materials introduces a variety of structures and novel defect types. Traditional characterization methods rely on labor-intensive, manual examination of X-ray CT scans, demanding considerable time and expertise. This process, while thorough, is not sustainable for large-scale analysis and defect tracking. Addressing this critical challenge, this presentation introduces an innovative suite of automated tools and analytical techniques, pioneered by the Mars Sample Return (MSR) Earth Entry System (EES) Thermal Protection System (TPS) project. These advancements offer a more efficient and accurate approach for quantifying CT data and characterizing the defects of 3D woven composites.
3D Woven↗
Assessing Potential Geophysical and Environmental Impacts from Frequent Rocket Launch Missions at Kennedy Space Center
Kennedy Space Center (KSC) in Florida has been utilized for space missions over many years with a gradually increasing number of rocket launches. There have been multiple studies where air-coupled acoustic waves and infrasound originating from launched rockets were used for operational purposes, such as locating booster trajectories as a function of changing atmospheric conditions. However, no study has utilized the acoustoelastic waves as a signal source for subsurface seismic investigations. We conducted a dispersion analysis using the seismic energy recorded from the Artemis I rocket launch at KSC in November 2022, and from these results we generated depth-sensitivity kernels at different wave frequencies. The kernels were compared with sedimentary core data to verify boundaries of carbonate layers above the Floridan Aquifer System. Accumulative information of bedrock-sediment boundary across the sedimentary platform could especially provide geo-structural evidence that manifests the configuration of coastal features. Continuous dispersion analysis of seismic recordings from consecutive rocket launches also has potential to identify non-stationary environmental effects, such as reorientation of sedimentary structures and fluctuation of the saltwater/groundwater lens from gravitational tides, which may affect erosional susceptibility of coastal features.
Rocket Launch Seismicity↗
3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning
Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.
clouds↗
Strong Thermoset Regolith UV-Curable Composite Technology (STRUCT) Overview
Future lunar surface missions require construction materials that can be manufactured in situ using lo-cal resources while operating under extreme environmental conditions. Many Lunar material demands can be solved solely with regolith by compacting or sintering. And yet past Lunar missions rely on polymeric materials, and sustained Lunar missions must reduce Earth-supplied polymers dependence. Dual-cure (Ultraviolet (UV) and thermal) polymer-regolith composites offer a promising pathway by leveraging solar UV radiation, moderate thermal in-put, and regolith. Mission mass limits, power availability and energy constraints on the lunar surface further motivate low-energy processing and curing strategies for surface construction materials. The Strong Thermoset Regolith UV-Curable Composite Technology (STRUCT) project has successfully synthesized and demonstrated dual-cure photopolymer resins derivable from in-situ resources [3]. Morphological, thermal, and mechanical characterization show that the newly formulated UV curable resin systems integrates well with lunar regolith simulants. Processing and chemistry changes, and computational analysis advanced the composite design. X-ray CT scanned and computational analysis demonstrate that resin, regolith and additives are well incorporated. The large fraction of regolith, 95% by mass, large char yield (82% mass), low thermal conductivity (0.26 W/m/K), confirm this material as a promising high-performance thermal and structural material.
thermal conductivity↗