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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Peridynamic Theory as a New Paradigm for Multiscale Modeling of Sintering

Sintering is a component fabrication process in which powder is compacted by pressing or some other means and then held at elevated temperature for a period of hours. The powder grains bond with each other, leading to the formation of a solid component with much lower porosity, and therefore higher density and higher strength, than the original powder compact. In this project, we investigated a new way of computationally modeling sintering at the length scale of grains. The model uses a high-fidelity, three-dimensional representation with a few hundred nodes per grain. The numerical model solves the peridynamic equations, in which nonlocal forces allow representation of the attraction, adhesion, and mass diffusion between grains. The deformation of the grains is represented through a viscoelastic material model. The project successfully demonstrated the use of this method to reproduce experimentally observed features of material behavior in sintering, including densification, the evolution of microstructure, and the occurrence of random defects in the sintered solid.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Simulated impedance of diffusion in porous media

This paper describes the use of a frequency domain, finite-difference scheme to simulate the impedance spectra of diffusion in porous microstructures. We investigate both open and closed systems for a range of ideal geometries, as well as some randomly generated synthetic volumes and tomographically derived microstructural data. In many cases, the spectra deviate significantly from the conventional Warburg-type elements typically used to represent diffusion in equivalent circuit analysis. Furthermore, a key finding is that certain microstructures show multiple peaks in the complex plane, which may be misinterpreted as separate electrochemical processes in real impedance data. This is relevant to battery electrode design as the techniques for nano-scale fabrication become more widespread. This simulation tool is provided as an open-source MatLab application and is freely available online as part of the TauFactor platform.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Random Laser Physical Unclonable Function

We report on the fabrication and characterization of nanocrystalline ZnO films for use as a random laser physical unclonable function (PUF). Correlation between processing conditions and film microstructure will be made to optimize the lasing properties and random response. We will specifically examine the repeatability and security of PUFs demonstrated in this novel system. This demonstration has promise to impact many of Sandia's core missions including counterfeit detection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Slate – A new record for crystal preferred orientation

This report describes microstructures and preferred orientation of a Devonian slate from the Belgian Ardennes (La Fortelle quarry). The sample, composed largely of muscovite, chlorite, and quartz, displays extraordinary texture strength of muscovite, exceeding 100 multiples of random distribution (m.r.d.) for (001) pole figures and thus setting a new record for preferred orientation in rocks. Quantitative texture analysis was performed with the Rietveld method on synchrotron X-ray diffraction images. Interestingly, quartz also displays strong shape preferred orientation (SPO) as revealed by X-ray microtomography and SEM imaging but largely random crystallographic preferred orientation (CPO). In conclusion, microstructural evidence suggests that this fine-grained rock formed by recrystallization of shale under stress, with growth of phyllosilicates and dissolution and growth of quartz.

58 GEOSCIENCES↗
DOE OSTI2012

Influence of recrystallization on phase separation kinetics of oxide dispersion strengthened Fe Cr Al alloy

The effect of different starting microstructures on the kinetics of Fe-rich ({alpha}) and Cr-rich ({alpha}') phase separation during aging of Fe-Cr-Al oxide dispersion strengthened (ODS) alloys has been analyzed with a combination of atom probe tomography and thermoelectric power measurements. The results revealed that the high recrystallization temperature necessary to produce a coarse grained microstructure in Fe-base ODS alloys affects the randomness of Cr-atom distributions and defect density, which consequently affect the phase separation kinetics at low annealing temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Corrosion-resistant high-entropy alloys: A review

Corrosion destroys more than three percent of the world’s gross domestic product. Therefore, the design of highly corrosion-resistant materials is urgently needed. By breaking the classical alloy-design philosophy, high-entropy alloys (HEAs) possess unique microstructures, which are solid solutions with random arrangements of multiple elements. The particular locally-disordered chemical environment is expected to lead to unique corrosion-resistant properties. In this review, the studies of the corrosion-resistant HEAs during the last decade are summarized. The corrosion-resistant properties of HEAs in various aqueous environments and the corrosion behavior of HEA coatings are presented. The effects of environments, alloying elements, and processing methods on the corrosion resistance are analyzed in detail. Finally, the possible directions of future work regarding the corrosion behavior of HEAs are suggested.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Burner Rig Hot Corrosion of a Single Crystal Ni-48Al-Ti-Hf-Ga Alloy

The hot corrosion resistance of a single crystal Ni-48Al-1Ti-0.5Hf-0.2Ga alloy was examined in a Mach 0.3 burner rig at 900 C for 300 hours. The combustion chamber was doped with 2 ppmw synthetic sea salt. The hot corrosion attack produced a random mound morphology on the surface. Microstructurally, the hot corrosion attack appeared to initiate with oxide-filled pits which were often broad and shallow. At an intermediate stage, the pits increased in size to incorporate unoxidized Ni islands in the corrosion product. The rampant attack stage, which was observed only at sharp sample corners, was characterized by rapid inward growth of alumina in finger-like protrusions incorporating significant amounts of Al-depleted Ni islands. Aluminum consumption in the oxide fingers resulted in the growth of a gamma' layer ahead of the advancing oxide fingers.

Nesbitt, James A.↗
DOE OSTI2022

Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite

Nuclear graphite is a candidate material for Generation IV nuclear power plants. Porous materials such as graphite can contain complex networks of pores that influence the material's mechanical and irradiation response. A methodology known as the random finite element method (RFEM) was adapted to create synthetic microstructures and predict the influence of porosity on the elastic properties of graphite during oxidation. RFEM combines random field theory and the finite element method in a Monte Carlo framework to estimate the mechanical response of a given grade of graphite. In this research, the random fields were verified through experimental characterization to predict the elastic response of three nuclear graphite grades, ETU-10, IG-110, and 2114. Finite element models (FEM) were generated using segmentations of x-ray computed tomography (XCT) data known as image-based models (IBMs) to validate and compare with the RFEM results and better understand the effects of uniform oxidation in these graphite grades. The RFEM predictions appear to correlate well with the experimental values of the measured Young’s modulus of the three graphite grades and display the same trends as IBMs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Effect of heat input on the microstructure, residual stresses and corrosion resistance of 304L austenitic stainless steel weldments

Austenitic stainless steels are widely used in high performance pressure vessels, nuclear, chemical, process and medical industry due to their very good corrosion resistance and superior mechanical properties. However, austenitic stainless steels are prone to sensitization when subjected to higher temperatures (673 K to 1173 K) during the manufacturing process (e.g. welding) and/or certain applications (e.g. pressure vessels). During sensitization, chromium in the matrix precipitates out as carbides and intermetallic compounds (sigma, chi and Laves phases) decreasing the corrosion resistance and mechanical properties. In the present investigation, 304L austenitic stainless steel was subjected to different heat inputs by shielded metal arc welding process using a standard 308L electrode. The microstructural developments were characterized by using optical microscopy and electron backscattered diffraction, while the residual stresses were measured by X-ray diffraction using the sin{sup 2}ψ method. It was observed that even at the highest heat input, shielded metal arc welding process does not result in significant precipitation of carbides or intermetallic phases. The ferrite content and grain size increased with increase in heat input. The grain size variation in the fusion zone/heat affected zone was not effectively captured by optical microscopy. This study shows that electron backscattered diffraction is necessary to bring out changes in the grain size quantitatively in the fusion zone/heat affected zone as it can consider twin boundaries as a part of grain in the calculation of grain size. The residual stresses were compressive in nature for the lowest heat input, while they were tensile at the highest heat input near the weld bead. The significant feature of the welded region and the base metal was the presence of a very strong texture. The texture in the heat affected zone was almost random. - Highlights: • Effect of heat input on microstructure, residual stresses and corrosion is studied. • HAZ and width of dendrite in the welded region increase with heat input. • Residual stresses are tensile near the welded region after the highest heat input. • Welded region has the highest pit density after highest heat input. • Dendrites and δ-ferrite were highly oriented in the welded region.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Synthesis and characterization of visible-active molybdenum disulfide (2H-MoS{sub 2}) nanospheres

In current study, a novel 2H-MoS{sub 2} nanospheres were successfully synthesized and underwent structural- as well as optical-property characterizations. The MoS{sub 2} were prepared by one pot hydrothermal approach through adopting L-cysteine as environmentally-benignchalcogenide precursor. TEM image shows that the as-synthesized MoS{sub 2} appear to be spherical in shape with size distribution in the range of 120 nm – 180 nm. HRTEM lattice-fringes imaging further elucidate that the interlayer spacing at the edges is equal to be 0.62 nm that correspond to (002) plane stacking. Also, the HRTEM image clearly-illustrate that the internal microstructure of MoS{sub 2} composed of randomly-arrayed alternating layers, which render the postulation that the formation of nanosphere is driven by self-assembly of individual layers into globular morphology. XRD diffractogram that appear to be broad and unresolved reveal the partially crystalline nature of the sample. Optical-absorption spectra depicts the sample is visible active with featureless absorption, which can attribute to indirect transition of the excitions generated. By using Tauc plot, the bandgap energy is determined to be 1.75 eV, which reflect the nanospheres are indeed visible-active nanostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

Shape- and phase-controlled ZnS nanostructures and their optical properties

Graphical abstract: (a) TEM images of the nanorods, the HRTEM images for the lower (b) and the upper (c) part of the rod in (a). - Highlights: • Stacking faults were observed for ZnS nanocrystals with the size of ∼5 nm. • Nanotwinning structures and stacking faults were observed in ZnS nanorod. • Microstructure defects were found to be formed randomly for nanocrystals and nanorods. • The 1LO phonon mode exhibits a red-shift of 6 cm{sup −1} as the particle size increases from 5 to 15 nm. - Abstract: Single-crystalline ZnS nanoparticles with a zinc-blende crystal structure have some microdefects such as stacking faults and nanotwins. In contrast, ZnS nanorods have a wurtzite crystal structure, which grows along the [0 0 0 1] direction, although some nanorods display the intergrowth of a minor zinc-blende phase and the major wurtzite phase, which forms stacking faults or zinc-blende/wurtzite ZnS nanotwins. Raman spectroscopy measurements reveal surface phonons and longitudinal optical phonons in the nanoparticles, nanorods and doublet phonons that are associated with the transversal optical phonons of the A1 and E1 modes in only the nanorods. The first-order longitudinal optical phonon mode exhibits a blueshift of 6 cm{sup −1} when the particle size increases from 5 to 15 nm, but there is no shift in the range of 15–30 nm because of quantum confinement and microdefects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding interfacial segregation in polymer blend films with random and mixed side chain bottlebrush copolymer additives

Bottlebrush polymers are complex macromolecules with tunable physical properties dependent on the chemistry and architecture of both the side chains and the backbone. Prior work has demonstrated that bottlebrush polymer additives can be used to control the interfacial properties of blends with linear polymers but has not specifically addressed the effects of bottlebrush side chain microstructures. Here, using a combination of experiments and self-consistent field theory (SCFT) simulations, we investigated the effects of side chain microstructures by comparing the segregation of bottlebrush additives having random copolymer side chains with bottlebrush additives having a mixture of two different homopolymer side chain chemistries. Specifically, we synthesized bottlebrush polymers with either poly(styrene-ran-methyl methacrylate) side chains or with a mixture of polystyrene (PS) and poly(methyl methacrylate) (PMMA) side chains. Furthermore, the bottlebrush additives were matched in terms of PS and PMMA compositions, and they were blended with linear PS or PMMA chains that ranged in length from shorter to longer than the bottlebrush side chains. Experiments revealed similar behaviors of the two types of bottlebrushes, with a slight preference for mixed side-chain bottlebrushes at the film surface. SCFT simulations were qualitatively consistent with experimental observations, predicting only slight differences in the segregation of bottlebrush additives driven by side chain microstructures. Specifically, these slight differences were driven by the chemistries of the bottlebrush polymer joints and side chain end-groups, which were entropically repelled and attracted to interfaces, respectively. Using SCFT, we also demonstrated that the interfacial behaviors were dominated by entropic effects with high molecular weight linear polymers, leading to enrichment of bottlebrush near interfaces. Surprisingly, the SCFT simulations showed that the chemistry of the joints connecting the bottlebrush backbones and side chains played a more significant role compared with the side chain end groups in affecting differences in surface excess of bottlebrushes with random and mixed side chains. This work provides new insights into the effects of side chain microstructure on segregation of bottlebrush polymer additives.

36 MATERIALS SCIENCE↗
NASA NTRS2020

An Algorithm for Characterization of Fiber Aggregation in Composite Microstructures

Composite structures are susceptible to localized flaws, or variability, that drive global failure. To capture this variance, a multiscale model needs to be introduced that not only accurately represents the statistical nature of the composite microstructure but is also efficient. At the microscale, fiber aggregation creates local stress concentrations, where failure is likely to occur sooner than expected. A method of rapid microstructure generation, in conjunction with cluster characterization, is examined to develop accurate reproductions of 2D composite cross-sections. Parameterization of shape and size of fiber clusters is used to characterize representative volume elements. The discrete element method is used to generate pseudo-microstructures. The clustering parameters from the pseudo- and actual microstructure arrangements (obtained from micrographs) can be compared to determine the validity of the representative volume element generated with the discrete element method. Results show a promising approach to evaluating randomness of fiber distributions and how to accurately recreate microstructures for strength analysis.

Carbon fiber↗
DOE OSTI2024

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Here, currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Structural, electrical, and thermoelectric properties of bismuth telluride: Silicon/carbon nanocomposites thin films

In this study, the effect of the presence of secondary phases on the structural, electrical, and thermoelectric properties of nanocomposite Bi{sub 2}Te{sub 3} films prepared by co-sputtering of silicon and carbon with Bi{sub 2}Te{sub 3} has been investigated. Growth temperature and the presence of Si and C phase are observed to have a strong effect on the topography and orientation of crystallites. X-ray diffraction study demonstrates that Bi{sub 2}Te{sub 3} and Bi{sub 2}Te{sub 3}:C samples have preferred (0 0 15) orientation in comparison to Bi{sub 2}Te{sub 3}:Si sample, which have randomly oriented crystallites. Atomic force, conducting atomic force, and scanning thermal microscopy analysis show significant differences in topographical, electrical, and thermal conductivity contrasts in Bi{sub 2}Te{sub 3}:Si and Bi{sub 2}Te{sub 3}:C samples. Due to the randomly oriented crystallites and the presence of Si along the crystallite boundaries, appreciable Seebeck coefficient, higher electrical conductivity, and lower thermal conductivity is achieved resulting in relatively higher value of power factor (3.71 mW K{sup −2} m{sup −1}) for Bi{sub 2}Te{sub 3}:Si sample. This study shows that by incorporating a secondary phase along crystallite boundaries, microstructural, electrical, and thermoelectric properties of the composite samples can be modified.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗