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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Nb-1Zr L-PBF In-situ Alloying and Elevated Temperature Mechanical Performance

Nb-1Zr is a readily-fabricable, dispersion-strengthened niobium alloy with improved elevated temperature properties compared to commercially pure niobium. This moderate strength niobium alloy is desirable in thermal management systems for fission power and propulsion applications; however, pre-alloyed spherical feedstock is generally unavailable. Laser powder bed fusion (L-PBF) in-situ alloying of a Nb-1Zr chemistry was performed using a pure niobium feedstock coated with 1 wt% of zirconium-based ceramic compound nano-powders. The additively manufactured material was mechanically tested at elevated temperatures in a controlled environment in the as-built condition. For comparison, traditionally manufactured wrought feedstock was also tested under identical conditions. Examination of the materials’ microstructure and elevated temperature mechanical response aimed to determine the Zr-compound nano-powder addition with the best performance, and the overall feasibility of the in-situ alloying approach.

Niobium-based alloys↗
DOE OSTI2021

Nonlinear ultrasonic technique for the characterization of microstructure in additive materials

This study employs nonlinear ultrasonic techniques to track microstructural changes in additively manufactured metals. The second harmonic generation technique based on the transmission of Rayleigh surface waves is used to measure the acoustic nonlinearity parameter, β. Stainless steel specimens are made through three procedures: traditional wrought manufacturing, laser-powder bed fusion, and laser engineered net shaping. The β parameter is measured through successive steps of an annealing heat treatment intended to decrease dislocation density. Dislocation density is known to be sensitive to manufacturing variables. In agreement with fundamental material models for the dislocation-acoustic nonlinearity relationship in the second harmonic generation, β drops in each specimen throughout the heat treatment before recrystallization. Geometrically necessary dislocations (GNDs) are measured from electron back-scatter diffraction as a quantitative indicator of dislocations; average GND density and β are found to have a statistical correlation coefficient of 0.852 showing the sensitivity of β to dislocations in additively manufactured metals. Moreover, β shows an excellent correlation with hardness, which is a measure of the macroscopic effect of dislocations.

42 ENGINEERING↗
DOE OSTI2021

Thermal Conductivity Measurement of Microstructure in Irradiated Materials

Argonne National Laboratory is establishing additional capabilities for advanced techniques for accelerated experiment-based fuel material screening. A key supporting element is expanded capabilities for the characterization of irradiated samples, including establishment of the capability to measure irradiated fuel thermal conductivity at the micro-scale level. Argonne is collaborating with the University of Illinois at Urbana-Champaign (UIUC) to adopt the suspended bridge method to measure thermal conductivity of radiation-induced microstructures in nuclear materials directly. The initial phase of work to support thermal conductivity measurements of microstructure in irradiated materials at Argonne has been executed, and is summarized here. This report is intended to serve as a status update for this activity, summarizing work completed in FY20.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Microstructure Clones

Background: A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. Objective: This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. Methods: This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Results: Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Conclusions: Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Crystal Plasticity↗
DOE OSTI2023

Designing shape-memory-like microstructures in intercalation materials

During the reversible insertion of ions, lattices in intercalation materials undergo structural transformations. These lattice transformations generate misfit strains and volume changes that, in turn, contribute to the structural decay of intercalation materials and limit their reversible cycling. In this paper, we draw on insights from shape-memory alloys, another class of phase transformation materials, that also undergo large lattice transformations but do so with negligible macroscopic volume changes and internal stresses. We develop a theoretical framework to predict structural transformations in intercalation compounds and identify crystallographic design rules necessary for forming shape-memory-like microstructures in intercalation materials. We use our approach to systematically screen open-source structural databases comprising $n$ > 5000 pairs of intercalation compounds. We identify candidate compounds, such as Li x Mn 2 O 4 (Spinel), Li x Ti 2 (PO 4 ) 3 (NASICON), that approximately satisfy the crystallographic design rules and can be precisely doped to form shape-memory-like microstructures. Throughout, we compare our analytical results with experimental measurements of intercalation compounds. We find a direct correlation between structural transformations, microstructures, and increased capacity retention in these materials. These results, more generally, show that crystallographic designing of intercalation materials could be a novel route to discovering compounds that do not decay with continuous usage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine-Learning Microstructure for Inverse Material Design

Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here a successful machine-learning (ML) method is developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Morphology and microstructure of composite materials

Lightweight continuous carbon fiber based polymeric composites are currently enjoying increasing acceptance as structural materials capable of replacing metals and alloys in load bearing applications. As with most new materials, these composites are undergoing trials with several competing processing techniques aimed at cost effectively producing void free consolidations with good mechanical properties. As metallic materials have been in use for several centuries, a considerable database exists on their morphology - microstructure; and the interrelationships between structure and properties have been well documented. Numerous studies on composites have established the crucial relationship between microstructure - morphology and properties. The various microstructural and morphological features of composite materials, particularly those accompanying different processing routes, are documented.

Tiwari, S. N.↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Nonlinear mechanics of composite materials with periodic microstructure

This report summarizes the result of research done under NASA NAG3-882 Nonlinear Mechanics of Composites with Periodic Microstructure. The effort involved the development of non-finite element methods to calculate local stresses around fibers in composite materials. The theory was developed and some promising numerical results were obtained. It is expected that when this approach is fully developed, it will provide an important tool for calculating local stresses and averaged constitutive behavior in composites. NASA currently has a major contractual effort (NAS3-24691) to bring the approach developed under this grant to application readiness. The report has three sections. One, the general theory that appeared as a NASA TM, a second section that gives greater details about the theory connecting Greens functions and Fourier series approaches, and a final section shows numerical results.

Jordan, E. H.↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Microstructure and Mechanical Properties of Extruded Gamma Microstructure Met PX

A gamma TiAl alloy with a high Nb content is being assessed as a compressor blade material. The microstructure and mechanical properties of extruded Ti-45Al-X(Nb,B,C) (at.%) were evaluated in both an as-extruded condition and after a lamellar heat treatment. Tensile behavior of both as-extruded and lamellar heat treated specimens was studied in the temperature range of RT to 926 C. In general, the yield stress and ultimate tensile strength reached relatively high values at room temperature and decreased with increasing deformation temperature. The fatigue strength of both microstructures was characterized at 650 C and compared to a baseline TiAl alloy and to a Ni-base superalloy. Tensile and fatigue specimens were also exposed to 800 C for 200 h in air to evaluate the alloy's environmental resistance. A decrease in ductility was observed at room temperature due to the 800 C. exposure but the 650 C fatigue properties were unaffected. Compressive and tensile creep testing between 727 and 1027 C revealed that the creep deformation was reproducible and predictable. Creep strengths reached superalloy-like levels at fast strain rates and lower temperatures but deformation at slower strain rates and/or higher temperature indicated significant weakening for the as-extruded condition. At high temperatures and low stresses, the lamellar microstructure had improved creep properties when compared to the as-extruded material. Microstructural evolution during heat treatment, identification of various phases, and the effect of microstructure on the tensile, fatigue, and creep behaviors is discussed.

Draper, S. L.↗
DOE OSTI2021

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗
NASA NTRS2017

Microstructure Based Material-Sand Particulate Interactions and Assessment of Coatings for High Temperature Turbine Blades

Gas turbine engines for military/commercial fixed-wing and rotary wing aircraft use thermal barrier coatings in the high-temperature sections of the engine for improved efficiency and power. The desire to further make improvements in gas turbine engine efficiency and high power-density is driving the research and development of thermal barrier coatings and the effort of improving their tolerance to fine foreign particulates that may be contained in the intake air. Both commercial and military aircraft engines often are required to operate over sandy regions such as in the Middle-East nations, as well as over volcanic zones. For rotorcraft gas turbine engines, the sand ingestion is adverse during take-off, hovering near ground, and landing conditions. Although, most of the rotorcraft gas turbine engines are fitted with inlet particle separators, they are not 100 percent efficient in filtering fine sand particles of size 75 microns or below. The presence of these fine solid particles in the working fluid medium has an adverse effect on the durability of turbine blade thermal barrier coatings and overall performance of the engine. Typical turbine blade damages include blade coating wear, sand glazing, Calcia-Magnesia-Alumina-Silicate (CMAS) attack, oxidation, plugged cooling holes, all of which can cause rapid performance deterioration including loss of aircraft. The objective of this research is to understand the fine particle interactions with typical ceramic coatings of turbine blades at the microstructure level. A finite-element based microstructure modeling and analysis has been performed to investigate particle-surface interactions, and restitution characteristics. Experimentally, a set of tailored thermal barrier coatings and surface treatments were down-selected through hot burner rig tests and then applied to first stage nozzle vanes of the Gas Generator Turbine of a typical rotorcraft gas turbine engine. Laser Doppler velocity measurements were performed during hot burner rig testing to determine sand particle incoming velocities and their rebound characteristics upon impact on coated material targets. Further, engine sand ingestion tests were carried out to test the CMAS tolerance of the coated nozzle vanes. The findings from this on-going collaborative research to develop the next-gen sand tolerant coatings for turbine blades are presented in this paper.

Engine Tests↗
DOE OSTI2020

Spatial correlation functions of paracrystals with radial symmetry

Here, we develop a phenomenological model to describe the structure of radially symmetric paracrystals whose long-range order are destroyed by propagation of particle fluctuations. General expressions are derived for the spatial correlation functions in one-, two-, and three-dimensional spaces. And the spatial correlation in paracrystals in reciprocal space is further discussed and clarified. The developed method can be used to quantitatively analyze the microstructure of paracrystalline materials in both real and reciprocal spaces via scattering experiments and computer simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

47 OTHER INSTRUMENTATION↗