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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗
NASA NTRSDate not supplied

Investigation of the Effect of Etch Pits on the Material Properties of Carbon Fiber Structures

It is well known that etch pits are formed on carbon surfaces during their consumption in reactive gas environments such as during ablation of carbon-based thermal protection system (TPS) materials. These pits start out from atomic defects on the carbon surface, grow in size and then ultimately lead to the complete removal of the solid carbon material. However, the effect of such etch pits on the material properties of carbon ablators such as FiberForm are poorly understood, as the maximum size of pits are on the order of microns in size, which makes them hard to observe experimentally. Hence, we have developed a module within the Porous Microstructure Analysis code PuMA that can generate etch pits on an arbitrary material surface, with a prescribed distribution of pit size and density. We use this newly developed module to calculate material properties of FiberForm, which is the base material for one of the most commonly used TPS materials - Phenolic Impregnated Carbon Ablator (PICA). The detailed microstructure of FiberForm in PuMA will either be generated synthetically or obtained from X-ray microtomography. Our simulations show that material properties such as the thermal conductivity and tortuosity decrease with increasing degree of pitting. This will be helpful to more accurately predict the degradation of carbon-based TPS during ablation, and furthermore may allow us to identify previously unknown TPS failure modes due to pitting.

PuMA↗
NASA NTRS1992

Microstructural characterization of thin polymer films using Langley low energy positron flux generator

We have developed a highly efficient scheme for generating high fluxes of slow positrons. These positrons have been successfully used to measure lifetimes in thin test films. The lifetime data have been used to develop two structure-property models for the test films. The first model relates the free volume cell size to the molecular weight of the polymer repeat unit. The second model relates the free volume fraction to the dielectric constant of the polymer film.

Singh, Jag. J.↗
DOE OSTI2025

The influence of kinematics of deformation on polycrystalline halite dynamic recrystallization: Full-field simulation of simple shear versus pure shear

Rock salt, composed mainly of halite, flows viscoplastically over a wide range of geological conditions, strongly impacting the dynamic evolution of sedimentary basins and orogens. Understanding how dislocation creep, which involves dislocation glide, intracrystalline recovery and dynamic recrystallization, influences the microstructure and rheology of halite under various deformation kinematics and temperatures is crucial for enhancing knowledge of salt flow dynamics. Here, this study employs a full-field numerical simulation method to compare the viscoplastic deformation of polycrystalline halite under simple shear and pure shear conditions up to a natural strain of ε = 1.5 at temperatures ranging from 100 °C to 300 °C. The results are presented in terms of crystallographic preferred orientation (CPO), grain shape preferred orientation (SPO), subgrain boundary direction, grain size and strain rate distribution. The results indicate that the crystallographic anisotropy of individual halite crystals is transferred to the polycrystalline scale, resulting in strain localization, particularly in simple shear simulations. The kinematics of deformation affect the evolution and distribution of high strain-rate bands, determining the direction of intragranular substructures and the morphology of strain-induced grain boundaries, with minimal impact on grain size. The intensity of grain boundary migration increases with temperature, significantly influencing grain morphology and size, thereby obscuring strain localization, while having little effect on CPOs. At low strain (ε < 1.0), CPOs relative to both the maximum shortening direction and the grain SPO are similar regardless of the deformation kinematics. At high strain (ε > 1.0), simple shear CPOs exhibit three stronger {100} maxima with a monoclinic symmetry relative to the grain SPO compared to the six {100} maxima with an orthotropic symmetry relative to the grain SPO generated under pure shear. Therefore, microstructures and CPOs can serve as indicators of the strain path in polycrystalline halite under various conditions, aiding in determining the shear sense and elucidating the deformation kinematics of salt structures.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Investigation of the effect of etch pits on the material properties of carbon fiber structures

It is well known that etch pits are formed on carbon surfaces during their consumption in reactive gas environments such as during ablation of carbon-based thermal protection system (TPS) materials. These pits start out from atomic defects on the carbon surface, grow in size and then ultimately lead to the complete removal of the solid carbon material [1, 2]. However, the effect of such etch pits on the material properties of carbon ablators such as FiberForm are poorly understood, as the maximum size of pits are on the order of microns in size, which makes them hard to observe experimentally. Hence, we have developed a module within the Porous Microstructure Analysis code PuMA [3] that can generate etch pits on an arbitrary material surface, with a prescribed distribution of pit size and density. Figure 1 shows a FiberForm sample with artificially generated pits of varying radii and pit density. We use this newly developed module to calculate material properties of FiberForm, which is the base material for one of the most commonly used TPS materials - Phenolic Impregnated Carbon Ablator (PICA). The detailed microstructure of FiberForm in PuMA will either be generated synthetically or obtained from X-ray microtomography [4]. Our simulations show that material properties such as the thermal conductivity and tortuosity decrease with increasing degree of pitting. This will be helpful to more accurately predict the degradation of carbon-based TPS during ablation, and furthermore may allow us to identify previously unknown TPS failure modes due to pitting.

PuMA↗
DOE OSTI2025

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗
DOE OSTI2022

Multiscale characterization and comparison of historical and modern nuclear graphite grades

Beginning with Chicago Pile I, graphite has been used as a moderator material in nuclear power stations and is considered a potential material for use in future Generation IV advanced reactors. The microstructure of graphite is responsible for much of its mechanical and thermo-physical properties, and how it responds to irradiation. To understand graphite microstructure, it is necessary to understand its porosity at the macro- and micro-scales; and to understand its porosity, it is necessary to characterize the morphological connectivity of the void content and the two main phases of graphite: filler and binder. Here, using several microscopy and analytical techniques, a detailed examination of the heterogeneity, microstructure and pore structure of different graphite grades and their binder and filler phases is presented. Significant differences were found between coarser and finer nuclear grades. Coarse grades have a more diverse range of filler particles, pores and thermal cracks. Finer grades have a more well-defined pore size distribution, fewer variations of filler particles sizes and do not contain as many large thermal cracks. Fine grades tend to have a well-connected network of pores whereas coarser grades contain a larger content of closed porosity. The framework developed within this work can be applied and used to assess the various graphite grades that would down-select materials for specific use in graphite moderated reactor designs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Quantification of morphological change in materials based on image data utilizing machine learning techniques

Computed tomography (CT) resolution has become high enough to monitor morphological changes due to aging in materials in long-term applications. We explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images visually reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect. Both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Investigation of the Formation of Topologically Close Packed Phase Instabilities in Nickel-Based Superalloy Rene N6

Topologically close packed (TCP) phase instability in third generation Ni-base superalloys is understood to hinder component performance when used in high-temperature jet engine applications. The detrimental effects on high temperature performance from these brittle phases includes weakening of the Ni-rich matrix through the depletion of potent solid solution strengthening elements. Thirty-four compositional variations of polycrystalline Rene N6 were defined from a design-of-experiments approach and then cast, homogenized, and finally aged to promote TCP formation. Our prior work reported on the results of the multiple retression modeling of these alloys in order to predict the volume fraction of TCP. This paper will present further regression modeling results on these alloys in order to predict the occurrence of TCP in third generation Ni-base superalloy microstructures. Kinetic results are also discussed.

Ritzert, Frank↗
DOE OSTI2025

Mesoscale Modeling of Dislocation Cell Structure Evolution and Radiation-Induced Segregation in Additively Manufactured Austenitic Stainless Steel

Structural alloys under irradiation develop radiation-induced segregation (RIS) at point defect sinks, which lead to undesired changes in the alloy's properties. Additively manufactured austenitic stainless steels are expected to yield a distinct response to irradiation damage owing to their unique as-printed dislocation cell structure. In this talk, we present the development of a mesoscale model for dislocation cell structure evolution coupled with RIS using the MOOSE framework. The evolution of network dislocations within spatially distinct cells and cell walls are modeled using climb-mediated edge annihilation and generation processes. Starting with initial microstructures of as-printed non-equilibrium segregations at dislocation cell walls, our 2D and 3D simulations explore the effects of irradiation temperature, dose rate, dislocation sink bias, and specimen thickness on microstructure evolution. The results will be compared against experimental characterizations of in-situ and ex-situ ion-irradiated samples, and the implications of evolving cell structure on irradiation damage response will be discussed.

additive manufacturing↗
NASA NTRSDate not supplied

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechanical and Microstructural Study of Neutron-Irradiated Novel Alloy Systems

Generation IV nuclear reactors require structural materials capable of withstanding high temperatures, corrosion, and radiation damage. Refractory high entropy alloys (RHEAs) show potential due to their resistance to irradiation damage, reduced void swelling, and microstructural stability. However, most previous studies have focused on thin films and ion irradiation, leaving the neutron irradiation response of bulk RHEAs largely unexplored. Mechanical behavior results show brittle behavior before and after irradiation. The addition of Zr led to an increase in hardness due to Zr-rich precipitates in the bulk microstructure. Lastly, hardness results showed an increase in hardening after irradiation indicating irradiation induced hardening has occurred.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Stacking Faults Originating from Star-Defects in 4H-SiC

Intense efforts are currently in progress to study various sources of basal plane dislocations (BPDs) in SiC epitaxial layers. BPDs can generate Shockley-type stacking faults (SSFs) in SiC epitaxial layers, which have been shown to be associated with the degradation of power devices. This study shows that the star-shaped defect can be a source of several BPDs in the epitaxial layer. We investigate the complex microstructure of the star defect, the generation of BPDs, and expansion of SSFs using various complementary microscopy and optical techniques. We show direct evidence that star-defects can be a nucleation point of single-SSFs that can expand at the core of the defect. Newly found secondary dislocation arrays extending over a few centimeters away are found to be emanating from the primary arms of the star defect. The presence of such dislocation walls and the expansion of single-SSFs will affect the yield of numerous die on a wafer. Further understanding of the formation mechanism of stacking faults generated from star-defects as provided in this study helps understand their effect on SiC-based devices, which is crucial to assess device reliability.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Prebiotic Synthesis of Autocatalytic Products From Formaldehyde-Derived Sugars as the Carbon and Energy Source

Our research objective is to understand and model the chemical processes on the primitive Earth that generated the first autocatalytic molecules and microstructures involved in the origin of life. Our approach involves: (a) investigation of a model origin-of-life process named the Sugar Model that is based on the reaction of formaldehyde- derived sugars (trioses and tetroses) with ammonia, and (b) elucidation of the constraints imposed on the chemistry of the origin of life by the fixed energies and rates of C,H,O-organic reactions under mild aqueous conditions. Recently, we demonstrated that under mild aqueous conditions the Sugar Model process yields autocatalytic products, and generates organic micropherules (2-20 micron dia.) that exhibit budding, size uniformity, and chain formation. We also discovered that the sugar substrates of the Sugar Model are capable of reducing nitrite to ammonia under mild aqueous conditions. In addition studies done in collaboration with Sandra Pizzarrello (Arizona State University) revealed that chiral amino acids (including meteoritic isovaline) catalyze both the synthesis and specific handedness of chiral sugars. Our systematic survey of the energies and rates of reactions of C,H,O-organic substrates under mild aqueous conditions revealed several general principles (rules) that govern the direction and rate of organic reactions. These reactivity principles constrain the structure of chemical pathways used in the origin of life, and in modern and primitive metabolism.

Weber, Arthur L.↗
NASA NTRSDate not supplied

Computational Investigation of Oxidative Etch Pitting in FiberForm and Its Impact on Material Properties

Oxidation-driven carbon erosion does not occur uniformly but rather through the development of localized etch pits at active surface sites. These active sites form due to atomic defects on the carbon surface, making them significantly more reactive than the surrounding, non-defective areas. As a result, these sites are the first to react during ablation, leading to their removal. This process creates new defects in neighboring atoms, increasing their reactivity and causing localized carbon removal around these active sites. In this way, the highly reactive defective areas serve as nucleation points for the formation and growth of etch pits, which can have adverse effects on structural integrity of FiberForm. To better understand how these etch pits impact the material properties of carbon fiber microstructures, we have developed a new capability within the direct simulation Monte Carlo (DSMC) framework to capture the etch pit formation process. This capability, integrated into the DSMC code SPARTA (Stochastic Parallel Rarefied-gas Time-accurate Analyzer), models material removal in the presence of active sites, leading to the formation of etch pits. The current work focuses on studying the effects of these etch pits on the material properties of FiberForm, a widely used base material in thermal protection systems (TPS). The microstructure of virgin FiberForm, obtained via X-ray microtomography, is imported into SPARTA to generate the ablated geometries with etch pits. These modified microstructures are then analyzed using the Porous Microstructure Analysis (PuMA) software to compute various material properties, including elasticity, thermal conductivity, and permeability. We investigate the variation of these properties due to the complex surface topology changes caused by etch pit formation. Additionally, we compare the effects of pitting with the conventional model of shrinking fibers, traditionally used to simulate the ablation of carbon structures. Significant differences emerge between the two approaches. Consequently, this physically realistic model of material removal through etch pit formation offers improved accuracy in predicting the degradation of carbon-based TPS during oxidation. It also provides insights into other mechanisms, such as spallation, where chunks of material are removed into the flow due to etch pit growth. Ultimately, this model enhances our understanding of failure modes in these materials during ablation.

PuMA↗
DOE OSTI2022

Material processing, microstructure, and composite properties of low carbon Engineered Cementitious Composites (ECC)

Traditional PVA fiber-reinforced Engineered Cementitious Composites (ECC) show high tensile ductility and superior durability with tight crack width, but the high cost and embodied carbon can hinder its wider application in infrastructures. The objective of this study is to develop a better understanding of the fresh and hardened properties of an ECC that employs a lower embodied-carbon binder, Limestone Calcined Clay Cement (LC3), and lower-cost PP fiber that is widely available. Specifically, the interrelations between material processing, microstructure, and composite properties were studied experimentally. The results showed that ECC with high tensile ductility up to 9% tensile strain and tight crack width with 50 μm at 2% tensile strain can be achieved. It was found that a matrix paste with higher viscosity generally enhanced fiber dispersion uniformity and robustness in tensile strain-hardening. The paste viscosity is increased when OPC is replaced by LC3 and can be tuned with superplasticizer content. Larger maximum flaw size leads to lower first crack strength, beneficial for microcrack initiation and multiple cracking. This study generates fundamental knowledge linking processing-microstructure-performance of PP-LC3-ECC. This class of low embodied carbon ECC with tight crack width is expected to contribute to reducing the carbon footprint of the built environment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

Ahmadikia, Behnam↗