Search NASA⌕ Search

SEARCH · Search NASA

Results for “SEM data”

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.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Homogeneity Study of ZrC, NbC, and TaC Binary Carbide Fuels for the Application of Nuclear Thermal Propulsion

In alignment with NASA and DARPA goals, efforts towards Nuclear Thermal Propulsion (NTP) have increased in recent years. As progress is made, a main challenge still exists; the fuel in an NTP system must survive the extreme environment experienced during operating conditions. Three fuel forms have been studied including a ceramic-metallic matrix, a ceramic-ceramic matrix, and solid solution carbides. Solid solution carbides were studied previously as a fuel for NTP and was shown to be a promising technology; however, the programs were canceled before demonstration in an NTP engine test. Of the refractory metal carbides of interest, zirconium carbide (ZrC), niobium carbide (NbC) and tantalum carbide (TaC) were studied. ZrC, NbC, and TaC powders were consolidated in monocarbide (ZrC, NbC, TaC) and bi-carbide (ZrC-NbC, ZrC-TaC, NbC-TaC) forms using spark plasma sintering (SPS), with 50-50 mol% compositions of the bi-carbides of interest. The time and temperature conditions to achieve a completely homogeneous solid solution for the mixed carbides is of interest for NTP fuel applications, since this homogeneity is considered to be essential for this fuel form. Homogeneity is typically not achieved during sintering, as surface diffusion (predominant for sintering to near ideal density) tends to occur at lower temperatures than bulk volumetric diffusion (required for solute interdiffusion to achieve a homogeneous solid solution). High temperature isochronal annealing was performed to identify the conditions to achieve a chemically homogeneous sample. The solute interpenetration vs annealing temperature was quantified using energy dispersion spectroscopy (EDS) in a scanning electron microscope (SEM). X-ray diffraction (XRD) was used to accurately measure the phase stability and lattice parameter as a function of annealing conditions. The diffusion data from each sample was then used to determine self-diffusion coefficients for interdiffusion of the Nb, Zr, and Ta solutes and compared to existing literature values. The minimum annealing conditions to achieve complete chemical homogeneity for each carbide was determined.

nuclear thermal propulsion↗
DOE OSTI2022

Is the Adoption of Electric Vehicles (EVs) and Solar Photovoltaics (PVs) Interdependent or Independent? An Integrated EVs-PVs Modeling Framework

Transportation and residential energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics or PVs) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market share for both EVs and PVs continue to grow, both these emerging technologies are deployed rather disjoint without considering the existence of potential similarities among users who own (or aspire to own) EVs and PVs. This might be due to lack of understanding of the behavioral interdependence in consumer preferences towards these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores EV and PV adoption behaviors. A structural equations model (SEM) is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive 'bundled' adoption of EVs and PVs could accelerate the adoption of both these sustainable technologies. The results highlights the need to consider transport and building energy efficient technology adoption behavior in a single integrated structure.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
DOE OSTI2022

SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions

Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, here we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Correlative Surface Imaging Reveals Chemical Signatures for Bacterial Hotspots on Plant Roots

The rhizosphere is arguably the most complex microbial habitat on earth, comprising an integrated network of plant roots, soil and a highly diverse microbial community (the rhizosphere microbiome)1. Understanding, predicting and controlling plant-microbe interactions in the rhizosphere will allow us to harness the plant microbiome as a means to increase or restore plant ecosystem productivity, improve plant responses to a wide range of environmental perturbations, and mitigate effects of climate change by designing ecosystems for long-term soil carbon storage1. To this end, it is imperative to develop new molecular approaches with high spatial resolution to capture interactions at the plant-microbe, microbe-microbe, and plant-plant interfaces. In this work, we designed an imaging sample holder that allows integrated surface imaging tools to map the same locations of a plant root-microbe interface with submicron lateral resolutions, providing novel in vivo analysis of root-microbe interactions. Specifically, confocal fluorescence microscopy, time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS), and scanning electron microscope (SEM) were used for the first time for correlative imaging of the Brachypodium distachyon root and its interaction with Pseudomonas SW25, a typical plant growth-promoting soil bacterium. Imaging data suggest that the root surface is inhomogeneous and that the interaction between Pseudomonas and Brachypodium roots was confined to only few spots along the sampled root segments and that the bacterial attachment spots were enriched in Na- and S-related and high-mass organic species. We conclude that attachment of the Pseudomonas cells to the root surface is outcompeted by strong root-soil mineral interactions but facilitated by formation of extracellular polymeric substances (EPS).

Plant root, Plant growth-promoting bacteria, soil,↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of Orientation Mapping in SEM and TEM

In this work, multiple experimental configurations for performing nanoscale orientation mapping are compared to determine their fidelity to the true 3D microstructure of a sample. Transmission Kikuchi Diffraction (TKD) experiments in an SEM and nanobeam diffraction (NBD) experiments in a TEM were performed on thin electrodeposited hard Au films with two different microstructures. The Au samples either have a grain size that is >50 nm or <20 nm. The same regions of the samples were measured with TKD apparatuses at 30 kV in an SEM with detectors in the horizontal and vertical configurations and in the TEM at 300 kV. Under the proper conditions, we demonstrate all three configurations can produce data of equivalent quality.Each method has strengths and challenges associated with its application and representation of the true microstructure. The conditions needed to obtain high-quality data for each acquisition method and the challenges associated with each will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multiscale Modeling and Experimental Insights into High-Temperature Soil Biodegradation Dynamics of Semi-Crystalline Poly(Lactic Acid) Nonwoven Fabrics

This study investigates the biodegradation of semi-crystalline poly(lactic acid) (PLA) nonwovens (NWs) in soil at 58 °C using both experimental and mathematical modeling approaches. The model utilizes a system of parabolic diffusion-reaction partial differential equations (PDEs) to elucidate chemical transformations over time and in space. It accounts for phenomena such as the diffusion of water and lactic acid monomers through the polymer matrix and into the surrounding soil, along with their microbial breakdown. It also accounts for the initial PLA crystallinity and predicts its evolution in time. The model is solved numerically for a single filament, and the results were used to shed light on PLA NW transformations observed in soil over a 180-day incubation period. Various characterization techniques, including scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and Raman spectroscopy, were employed to assess morphological changes, crystallinity, and molecular changes in the NWs throughout the experiment. By comparing the experimental data with the model predictions, the hydrolysis rate coefficient was found to be 3.37 × 10 -7 s -1 , while the rate of microbial degradation of lactic acid monomers was faster, of the order of 9.63 × 10 -7 s -1 . The findings highlight the significant role of crystallinity in the biodegradation process. The PLA degradation ceases when no amorphous material remains, and the crystallinity reaches 0.8, as observed in the experiments by day 120. Furthermore, this research contributes to a deeper understanding of PLA biodegradation dynamics and offers insights for effectively managing biodegradable materials in environmental settings.

Diffusion−reaction modeling↗
NASA NTRS1992

LDEF SP-HVDE (Space Plasma-High Voltage Drainage Experiment) post-flight results: Leakage current and discharge

TRW designed and fabricated two identical SP-HVDE trays which were flown in the NASA LDEF (Long Duration Exposure Facility) for 5.75 years in the LEO (low earth orbit) environment. One tray was placed near the leading edge and one near the trailing edge, and investigations were performed to compare the environmental interactions on the dielectric samples of the two trays. Each tray consisted of six assemblies with Kapton dielectric samples of varying thicknesses (i.e., 2 mils, 3 mil, and 5 mils) biased under +/- 300 V, +/- 500 V, and +/- 1000 V. The original objective was successfully achieved by measuring the first in-flight average leakage current through the samples. Less than 5 percent of the post-flight coulometers behaved anomalously. The data should be valuable to the design and evaluation of spacecraft with high voltage systems. M/D (micrometeoroid and debris) impacts over the dielectric samples were examined using a SEM (Scanning Electron Microscope) and an EDS (Energy Dispersive x-ray Spectrometer). These impact sites were sprayed by silver which were most likely caused either by the impact or a 'local ESD' (electrostatic discharge).

Yaung, J. Y.↗
NASA NTRS1993

Characteristics of hypervelocity impact craters on LDEF experiment S1003 and implications of small particle impacts on reflective surfaces

The Ion Beam textured and coated surfaces EXperiment (IBEX), designated S1003, was flown on LDEF at a location 98 deg in a north facing direction relative to the ram direction. Thirty-six diverse materials were exposed to the micrometeoroid (and some debris) environment for 5.8 years. Optical property measurements indicated no changes for almost all of the materials except S-13G, Kapton, and Kapton-coated surfaces, and these changes can be explained by other environmental effects. From the predicted micrometeoroid flux of NASA SP-8013, no significant changes in optical properties of the surfaces due to micrometeoroids were expected. There were hypervelocity impacts on the various diverse materials flown on IBEX, and the characteristics of these craters were documented using scanning electron microscopy (SEM). The S1003 alumigold-coated aluminum cover tray was sectioned into 2 cm x 2 cm pieces for crater documentation. The flux curve generated from this crater data fits well between the 1969 micrometeoroid model and the Kessler debris model for particles less than 10(exp -9) gm which were corrected for the S1003 positions (98 deg to ram). As the particle mass increases, the S1003 impact data is greater than that predicted by even the debris model. This, however, is consistent with data taken on intercostal F07 by the Micrometeoroid/Debris Special Investigating Group (M/D SIG). The mirrored surface micrometeoroid detector flown on IBEX showed no change in solar reflectance and corroborated the S1003 flux curve, as well as results of this surface flown on SERT 2 and OSO 3 for as long as 21 years.

Mirtich, Michael J.↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Study of the thermal degradation mechanism of a composite propellant

The current experimental program was designed to systematically investigate the role of the oxidizer in the thermal degradation process of composite propellants. The scanning electron microscope (SEM) was used to examine the failure sites in thermally degraded propellant samples. The formulation variables tested were oxidizer purity, oxidizer particle size, and oxidizer to binder bonding agent. The binder, a saturated hydrocarbon, was kept constant throughout the experiments. The oxidizers were: AP, chlorate-doped AP, arsenate-doped AP, and phosphate-doped AP. The oxidizer particle size distribution was 60% of the large fraction and 40% of the small fraction. The bonding agent, when present, was used at the 0.15% level. The data showed that both the oxidizer purity and particle size had an important affect on the thermal degradation process. The affect of the oxidizer particle size was more noticeable at the higher temperature and stress levels. An examination of the failure site, by SEM, of propellants subject to these latter conditions indicated that the fracturing of the large oxidizer particles led to the propellant cracking.

Schmidt, W. G.↗
NASA NTRS2017

Surveys of ISS Returned Hardware for MMOD Impacts

Since February 2001, the Hypervelocity Impact Technology (HVIT) group at the Johnson Space Center in Houston has performed 26 post-flight inspections on space exposed hardware that have been returned from the International Space Station. Data on 1,024 observations of MMOD damage have been collected from these inspections. Survey documentation typically includes impact feature location and size measurements as well as microscopic photography (25-200x). Sampling of impacts sites for projectile residue was performed for the largest features. Results of Scanning Electron Microscopy (SEM) analysis to discern impactor source is included in the database. This paper will summarize the post-flight MMOD inspections, and focus on two inspections in particular: (1) Pressurized Mating Adapter-2 (PMA-2) cover returned in 2015 after 1.6 years exposure with 26 observed damages, and (2) Airlock shield panels returned in 2010 after 8.7 years exposure with 58 MMOD damages. Feature sizes from the observed data are compared to predictions using the Bumper risk assessment code.

Hyde, James↗
DOE OSTI2021

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Wu, Chao-Yi↗
NASA NTRS2003

Nanophase Iron Globules in Lunar Soil

Micrometeoritic impacts on lunar soils produce melt and vapor. A patina of condensed vapor is deposited on lunar grains, the melt forms agglutinitic glass. In lunar soils, agglutinitic glass and rinds of grains host submicron-sized globules of pure Fe0 (Fe-rich globules larger than 1 micron usually contain other elements such as Ni, P, and S). Observation and measurement of such small size requires either back scattered electron (BSE) imaging with a high-resolution SEM or transmitted electron imaging with a TEM. The two techniques impose different limitations on the size-range of measurements. Resolution of BSE imaging of polished thin sections or grain mounts of lunar soils is at best around 4-5nm (JEOL 6340F field-emission (FE)-SEM at JSC). Therefore, Fe0 globules below 10nm in cross-sectional diameter are not truly measured. The upper limit of a millimeter or so is not a hindrance. In fact, it is an advantage because whole grains can be observed and mapped at varying magnifications. Angstrom-scale resolution of TEM images is more than sufficient to observe and measure the smallest of Fe0 globules that are about 1nm in cross-section. Microtoming edges of lunar grains; however, puts an upper size limitation of 50nm, at best, on the wafer, which more or less limits measuring Fe0 globules up to 30nm or so. Clearly, SEM and TEM techniques complement each other in obtaining the complete range of size distribution of Fe0 globules in lunar soils. Below we describe, in brief, our method of determining the size distribution of Fe0 globules in agglutinitic glass using BSE-SEM imaging and size measurement. Although our work is incomplete, we also include a table of results obtained so far, which understandably would be refined as we collect more data.

C. L. James↗
DOE OSTI2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗