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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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8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Cellular solidification in a monotectic system

Succinonitrile-glycerol, SN-G, transparent organic monotectic alloy is studied with particular attention to cellular growth. The phase diagram is determined, near the monotectic composition, with greater accuracy than previous studies. A solidification interface stability diagram is determined for planar growth. The planar-to-cellular transition is compared to predictions from the Burton, Primm, Schlichter theory. A new technique to determine the solute segregation by Fourier transform infrared spectroscopy is developed. Proposed models that involve the cellular interface for alignment of monotectic second-phase spheres or rods are compared with observations.

Kaukler, W. F.↗
NASA NTRS1998

EXPRESS Service to the International Space Station: EXPRESS Pallet

The International Space Station (ISS) will be the ultimate scientific accomplishment in the history of NASA, with its primary objective of providing unique scientific investigation opportunities. This objective is the basis for the creation of the EXPRESS Pallet System (ExPs). The EXPRESS Pallet will provide extremal/unpressurized accommodations for a wide variety of external users. The payload developers represent many science disciplines, including earth observation, communications, solar and deep space viewing, long-term exposure, and many others. The EXPRESS Pallet will provide a mechanism to maximum utilization of the limited ISS unpressurized payload volume, standard physical payload interfaces for users, a standard integration template for users and the capability to changeout payloads on-orbit. The EXPRESS Pallet provides access to Ram, Wake, Starboard, Port, Nadir, Zenith and Earth Limb for exposure and viewing. 'Me ExPs consists of the Pallet structure, payload Adapters, and a subsystem assembly which includes data controller, power distribution and conversion, and Extra Vehicular Robotics/Extra-Vehicular Activity systems.

Primm, Lowell↗
NASA NTRS2001

The Microgravity Science Glovebox

The Microgravity Science Glovebox (MSG) provides scientific investigators the opportunity to implement interactive experiments on the International Space Station. The facility has been designed around the concept of an enclosed scientific workbench that allows the crew to assemble and operate an experimental apparatus with participation from ground-based scientists through real-time data and video links. Workbench utilities provided to operate the experiments include power, data acquisition, computer communications, vacuum, nitrogen. and specialized tools. Because the facility work area is enclosed and held at a negative pressure with respect to the crew living area, the requirements on the experiments for containment of small parts, particulates, fluids, and gasses are substantially reduced. This environment allows experiments to be constructed in close parallel with bench type investigations performed in groundbased laboratories. Such an approach enables experimental scientists to develop hardware that more closely parallel their traditional laboratory experience and transfer these experiments into meaningful space-based research. When delivered to the ISS the MSG will represent a significant scientific capability that will be continuously available for a decade of evolutionary research.

Baugher, Charles R.↗
NASA NTRS2024

Comprehensive Observations and Geostatistics of Slope Streaks within the Olympus Mons Aureole

Slope streaks (SS) are enigmatic linear features characterized by relatively low-albedo features that appear and fade on high-albedo slopes on Mars. Despite numerous hypotheses proposed to explain their formation, the primary mechanism behind SS remains elusive. Here, we examine 702 SS features using 32 multitemporal Context Imager (CTX) images and mesoscale modeling data obtained from a site (centered at 31.230°N, 216.281°E) in the Olympus Mons Aureole region. Our investigation revealed several key findings that shed light on the dynamics of SS formation and fading. We discovered a significant preference for SS formation on south-facing (equator-facing) slopes compared to north-facing slopes, with SS being over seven times more likely to occur on the former. Furthermore, SS formation was found to be seasonal with significantly enhanced by a factor of ∼6 near the equinoxes (from solar longitude Ls 337°-42° and 136°-227°) compared to other times of the year. Our analysis also revealed a correlation between the rates of SS formation and fading, with scree slopes exhibited the fastest-fading SS also experiencing the highest rates of newly-formed SS. Additionally, we measured the median starting and stopping slopes of SS to be 23.4° and 14.9°, respectively, significantly below the angle of repose of sand. These low slopes suggest the necessity for an energetic trigger mechanism to initiate SS formation. Infrared spectroscopy revealed that the principal distinction between the material inside and outside of a SS lies in the reduced abundance of dust within the streak. Notably, this site demonstrates the highest rates of SS formation (a 29.6% increase in new SS per Mars year) and fading (a 12% fading of SS per Mars year) ever quantified. These elevated rates may be attributed to the site's topography, which facilitates calm surficial nighttime winds throughout the year, leading to widespread dust deposition. Daytime downhill winds near the ridgelines of S-facing slopes may then trigger movement of newly deposited dust aggregates or alternatively, a Knudsen pump phenomenon could serve as a potential trigger for SS. We propose that SS is inhibited during the northern summer solstice season due to the relatively clear aphelion atmosphere, which limits dust deposition. Similarly, the lack of triggering activity during the winter solstice is likely due to more moderate daytime winds. Overall, our findings are consistent with the wind-triggered dry avalanche hypothesis as a plausible explanation for SS formation.

D E Stillman↗