Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Material microstructure”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An insight on the effect of sodium and silicon on microstructure and crystallography of high alumina cements

In the present study the influence of minor elements (Na{sub 2}O and SiO{sub 2}) on the mineralogy, chemistry and microstructure of High Alumina Cements (HACs) has been investigated. HACs have several advantages respected to Ordinary Portland Cement (OPC) but the shortage of Al-rich raw materials represents a limiting factor: re-use of Al-rich waste as raw material represents a solution but it will add minor elements to the raw meal that could change HACs properties. For the first time, four commercial HACs, doped with sodium and silicon, and one synthetic HAC, only highly doped in sodium, were studied through a multidisciplinary approach by combining conventional and unconventional analytical techniques. Results highlighted that (i) sodium and silicon were mainly incorporated in a sodium-rich phase (Na-phase, NCA{sub 2}, Na{sub 1.9}CaAl{sub 3.9}Si{sub 0.1}O{sub 8}), (ii) no minor phases such as gehlenite and/or mayenite occurred, and (iii) CA (CaAl{sub 2}O{sub 4}) and CA{sub 2} (CaAl{sub 4}O{sub 7}) revealed a limited ionic substitution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A cycle-jump acceleration method for the crystal plasticity simulation of high cycle fatigue of the metallic microstructure

For metallic materials, high-cycle fatigue life is sensitive to underlying microstructure features including secondary phases, textures, grains morphology, etc. The traditional, data-based safe-life approaches for modeling fatigue don’t explicitly consider the microstructure and can’t guide study in microstructure modification for improved fatigue property. Crystal plasticity-based simulation provides increased model fidelity at the expense of immense computation time, making it inapplicable for high cycle fatigue. In this work, an acceleration method based on cycle-jump approach (Lesne and Savalle, 1989) was developed for microstructure-based high-cycle FE simulation using crystal plasticity constitutive-model. We report this method demonstrated high efficiency in benchmark tests of various conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
NASA NTRS1999

Third and Final Shuttle Mission of the Isothermal Dendritic Growth Experiment Conducted: Highest Supercooling Ever Recorded Achieved

Dendrites describe the treelike crystal morphology commonly assumed in metals and alloys that freeze from supercooled or supersaturated melts. There remains a high level of engineering interest in dendritic solidification because the size, shape, and orientation of the dendrites determine the final microstructure of a material. It is the microstructure that then determines the physical properties of cast or welded products. Although it is well known that dendritic growth is controlled by the transport of latent heat from the moving solid-liquid interface, an accurate and predictive model has not yet been developed. The effects of gravity-induced convection on the transfer of heat from the interface have prevented adequate testing, under terrestrial conditions, of solidification models. The Isothermal Dendritic Growth Experiment (IDGE) constituted a series of three microgravity experiments flown aboard the Space Shuttle Columbia. The apparatus was used to grow and record dendrite solidification in the absence of gravity-induced convective heat transfer, thereby producing a wealth of benchmark-quality data for testing solidification models and theories.

Glicksman, Martin E.↗
NASA NTRS2011

Microstructure of Matrix in UHTC Composites

Approaches to controlling the microstructure of Ultra High Temperature Ceramics (UHTCs) are described.. One matrix material has been infiltrated into carbon weaves to make composite materials. The microstructure of these composites is described.

Johnson, Sylvia↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Chemical and Microstructural Changes in Metallic and Ceramic Materials Exposed to Venusian Surface Conditions

The chemical and microstructural behavior of steels (304, 310, 316, and 1018), nickel-based alloys (beta-NiAl, G30, and 625), gold, coatings (4YSZ, SilcoNert(TradeMark) 1040 (SilcoTek Co.), Dursan(TradeMark) (SilcoTek Co.), and porcelain), and bulk ceramics (alpha-Al2O3, fused quartz, beta-SiC, and alpha-Si3N4) were probed after exposure to supercritical fluid with temperature, pressure, and composition mimicking the Venus lower atmosphere. Exposures were carried out in the Glenn Extreme Environments Rig (GEER) chamber with the Venusian gas mixture (96.5% CO2, 3.5% N2, 30 ppm H2O, 150 ppm SO2, 28 ppm CO, 15 ppm OCS, 3 ppm H2S, 0.5 ppm HCl, and 5 ppb HF) at 92 bar (1330 psi) and 467 C (873 F) for durations of 10 and 42 days. An additional 21-day exposure was done to stainless steel uncoated and coated with SilcoNert(TradeMark) and Dursan(TradeMark). Samples were characterized before and after the experiment by gravimetric analysis, X-ray diffraction, X-ray photoelectron and Auger electron spectroscopies, and cross section electron microscopy analysis. All steels exposed for 10 and 42 days formed double-layered scales consisting mainly of metal (Cr, Fe, Ni) oxides and sulfides showing different chemistry, microstructure, and crystalline phases. The alloys G30 and 625 formed double-layered scales consisting mainly of nickel sulfides. After 10 days, the beta-NiAl exhibited no detectable scale, suggesting only a very thin film was formed. The 304 and 316 stainless steels coated with 4YSZ that were exposed for 10 and 42 days exhibited no significant oxidation. Steel 1018 coated with 4YSZ exhibited a corrosion scale of iron and/or chromium oxide formed at the base of the alloy. The 304 steel coated with porcelain did not exhibit corrosion, although the coating exhibited recession. SilcoNert(TradeMark) exposed for 10 and 42 days exhibited recession, although no oxidation was found to occur at the base of the alloy. Stainless steel 316 coated with Dursan(TradeMark)  exhibited corrosion at the base of the alloy. All ceramics tested showed no clear evidence of reaction. The weight-gain-per-area performance of the materials exposed in the GEER for 10 and 42 days are reported from the lowest to the highest weight gain per area as follows: gold did not exhibit any weight change; nickel-based alloys: beta- NiAl < G30 < 625; steels: 304 < 310 < 316 < 1018; ceramics: considering the experimental uncertainties, no weight change was observed for all ceramics of this work (alpha-Al2O3, Si3N4, SiC, and amorphous SiO2).

Costa, Gustavo C. C.↗
NASA NTRS2004

Ceramic Foams from Pre-Ceramic Polymer Routes for Reusable Acreage Thermal Protection System Applications

Contents include the following: Motivation. Current light weight insulation. Advantages of preceramic-polymer-derived ceramic foams. Rigid insulation materials. Tailor foam microstructures. Experimental approach. Results: sacrificial materials, sacrificial fillers. Comparison of foam microstructures. Density of ceramic foams. Phase evolution and properties: oxidation behavior. mechanical properties, aerothermal performance. Impact damage of microcellular foams. Conclusions.

Stackpoole, Mairead↗
DOE OSTI2025

Grain size effects on slip band development

Crystallographic slip localizations, known as slip bands, concentrate stress in polycrystals, often leading to the nucleation of damage. Slip band development has been experimentally shown to be sensitive to grain size, tending to develop more frequently and with a greater intensity in large grains. In this work, we investigate the influence of grain size on the propensity for crystallographic slip band development. To this end, we employ the slip band-fast Fourier transform method (SB-FFT). SB-FFT is a 3D, full-field crystal plasticity model that permits the incremental development of discrete crystallographic slip bands according to microstructure and material properties. We present a model Inconel 718 tricrystal to isolate the effect of grain size. Our findings show that slip bands in large grains develop at lower applied strain levels and at a faster rate than slip bands in small grains. The grain size effect is due to a backstress produced by the interaction of the slip band and its neighboring grain. The backstress is most intense at small grain sizes, impeding slip activity within a developing slip band and immediately surrounding the slip band.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A constitutive model for glass–ceramic materials

Glass–ceramics have received recent attention for use in glass–ceramic to metal hermetic seals. Due to their heterogeneous microstructure, these materials exhibit a number of advantageous responses over conventional glass based seals. Key amongst them is the possibility of a controllable thermal strain response and apparent coefficient of thermal expansion which may be used to minimize thermally induced residual stresses for aforementioned seals. Furthermore, these behaviors result from an inorganic glass matrix and variety of crystalline ceramic phases including silica polymorph(s) that may undergo reversible solid-to-solid transformations with associated inelastic strain. Correspondingly, these materials exhibit complex thermomechanical responses associated with multiple inelastic mechanisms (viscoelasticity and phase transformation). While modeling these behaviors is essential for developing and analyzing the corresponding applications, no such model exists. Therefore, in this work a three-dimensional continuum constitutive model for glass–ceramic materials combining these various inelastic mechanisms is developed via an internal state variable approach. A corresponding fully implicit three dimensional numerical formulation is also proposed and implemented. The model is used to simulate existing experiments and validate the proposed formalism. As an example, the simple seal problem of a glass–ceramic seal inside a concentric metal shell is explored. Finally, the impact of the cooling rates, viscoelastic shift factors, and inelastic strain on final residual stress state are all investigated and the differing contributions highlighted.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2021

Considering computational speed vs. accuracy: Choosing appropriate mesoscale RVE boundary conditions

Modeling a material’s microstructure using continuum theories allows for inspection of the relationship between coarse scale and fine scale behaviors. Computational limits generally require selection of a sub-volume from a bulk sample in order to directly model the microstructure. Boundary conditions are applied to the sub-volume to mimic the excluded bulk material. Appropriate selection of boundary conditions helps effectively determine the appropriate spatial scale required of the sub-volume. Applicable boundary conditions include direct displacement, periodic, and uniform traction. While direct displacement and periodic boundary conditions are commonly used, uniform traction boundary conditions have seen limited use due to rigid body stability issues in simulations of compression or shear deformation. A new application of uniform traction boundary conditions was developed through linear constraint equations, similar to approaches employed by direct displacement and periodic boundary conditions, to quench rigid body motions with minimal interference of the relative deformation of the model. These boundary conditions were tested by compressing several synthetically generated periodic microstructures using the finite element method. Evaluating the effective stiffness along the compression axis, the direct displacement boundary condition produced the stiffest response, whereas the uniform traction boundary condition produced the most compliant. Periodic boundary conditions produced the same response for all volumes analyzed and both the direct displacement and uniform traction boundary conditions trended toward the periodic response as the domain volume increased. Computational performance was also evaluated for each boundary condition using implicit and explicit solvers. Direct displacement boundary conditions presented the lowest computational cost of all of the boundary conditions followed by periodic then uniform traction. The computational expense of periodic and uniform traction boundary conditions limited the viable spatial scale and mesh resolutions able to be simulated. Selection of appropriate boundary conditions for specific uses need to be a balance between allowable computational expense and accuracy of the method. Techniques for evaluating which boundary conditions to use are discussed.

42 ENGINEERING↗
NASA NTRS2008

Simultaneous Noncontact Precision Imaging of Microstructural and Thickness Variation in Dielectric Materials Using Terahertz Energy

This article describes a noncontact single-sided terahertz electromagnetic measurement and imaging method that simultaneously characterizes microstructural (egs. spatially-lateral density) and thickness variation in dielectric (insulating) materials. The method was demonstrated for two materials-Space Shuttle External Tank sprayed-on foam insulation and a silicon nitride ceramic. It is believed that this method can be used as an inspection method for current and future NASA thermal protection system and other dielectric material inspection applications, where microstructural and thickness variation require precision mapping. Scale-up to more complex shapes such as cylindrical structures and structures with beveled regions would appear to be feasible.

Roth, Don J.↗
NASA NTRS2016

Information Management Workflow and Tools Enabling Multiscale Modeling Within ICME Paradigm

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to establish workflow for and demonstrate a unique set of web application tools for linking NASA GRC's Integrated Computational Materials Engineering (ICME) Granta MI database schema and NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset are presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.

Materials Engineering↗