SEARCH · Search NASA

Results for “Grain-boundary engineering”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

6 records

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements

Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li 0.375 Sr 0.4375 )(Ta 0.375 Nb 0.375 Zr 0.125 Hf 0.125 )O 3-δ compared with the (Li 0.375 Sr 0.4375 )(Ta 0.75 Zr 0.25 )O 3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Accurate machine-learning predictions of coercivity in high-performance permanent magnets

Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH max . While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (H c ). Although it is possible to calculate H c theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H c predictions for materials such as Ce-doped Nd 2 ⁢Fe 14 ⁢B, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to H c . With DFT calculations, we predict the Nd-site-dependent magnetic anisotropy behavior in Nd 2 ⁢Fe 14 ⁢B, confirming that Nd 4⁢g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T c ). Finally, both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict H c with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1974

Improved toughness of refractory compounds

The concept of grain-boundary-engineering through elimination of the grain-boundary silicate phase in silicon nitride was developed. The process involved removal of the silica from the nitride powder via a thermal treatment coupled with the use of nitride additives to compensate the remaining oxygen. Magnesium and aluminum nitrides are found to be the most effective additive for use as oxygen compensators. Strength decreases at elevated temperatures are not observed in the alumina containing material. The creep rate of a dual additive sialon composition was two orders of magnitude lower at 1400 C than commercial silicon nitride. A cursory analysis of the creep mechanism indicate that grain-boundary sliding is avoided through elimination of the grain-boundary silicate phase.

Wright, T. R.↗

DOE OSTI2026

Unlocking superplasticity in medium and high-entropy alloys

Superplasticity, the capacity of materials to sustain extraordinary tensile elongations at elevated temperatures, underpins a range of advanced metal-forming technologies. Conventionally, it is achieved in fine-grained alloys where deformation is dominated by grain-boundary sliding, accommodated by diffusion and dislocation activity. The advent of medium- and high-entropy alloys (M/HEAs), with their high chemical complexity and unconventional phase stability, offers new pathways to superplastic behavior beyond traditional alloy systems. Although investigated only recently, several M/HEAs already exhibit elongations that rival or exceed those of classical superplastic materials, particularly when ultrafine or metastable microstructures are engineered. Here, we review progress in understanding superplastic deformation in M/HEAs, emphasizing the interplay among composition, initial microstructure, thermomechanical processing, and microstructural evolution during high-temperature deformation. We discuss approaches to generating the fine-grained structures necessary for grain-boundary sliding, including severe plastic deformation and tailored heat treatments. We further highlight dynamic phenomena such as phase transformations, evolving grain-boundary chemistry, and deformation-induced grain refinement that can enhance plasticity in these systems. These mechanisms often shift the balance of deformation processes, enabling large elongations even outside classical criteria. Finally, we outline key challenges for application, including cost, scalability, recyclability, and microstructural stability.

klenam, Desmond [University of the Witwatersrand, ↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Method Developed for Improving the Thermomechanical Properties of Silicon Carbide Matrix Composites

Today, a major thrust for achieving engine components with improved thermal capability is the development of fiber-reinforced silicon-carbide (SiC) matrix composites. These materials are not only lighter and capable of higher use temperatures than state-of-the-art metallic alloys and oxide matrix composites (approx. 1100 C), but they can provide significantly better static and dynamic toughness than unreinforced silicon-based monolithic ceramics. However, for successful application in advanced engine systems, the SiC matrix composites should be able to withstand component service stresses and temperatures for the desired component lifetime. Since the high-temperature structural life of ceramic materials is typically controlled by creep-induced flaw growth, a key composite property requirement is the ability to display high creep resistance under these conditions. Also, because of the possibility of severe thermal gradients in the components, the composites should provide maximum thermal conductivity to minimize the development of thermal stresses. State-of-the-art SiC matrix composites are typically fabricated via a three-step process: (1) fabrication of a component-shaped architectural preform reinforced by high-performance fibers, (2) chemical vapor infiltration of a fiber coating material such as boron nitride (BN) into the preform, and (3) infiltration of a SiC matrix into the remaining porous areas in the preform. Generally, the highest performing composites have matrices fabricated by the CVI process, which produces a SiC matrix typically more thermally stable and denser than matrices formed by other approaches. As such, the CVI SiC matrix is able to provide better environmental protection to the coated fibers, plus provide the composite with better resistance to crack propagation. Also, the denser CVI SiC matrix should provide optimal creep resistance and thermal conductivity to the composite. However, for adequate preform infiltration, the CVI SiC matrix process typically has to be conducted at temperatures below 1100 C, which results in a SiC matrix that is fairly dense, but contains metastable atomic defects and is nonstoichiometric because of a small amount of excess silicon. Because these defects typically exist at the matrix grain boundaries, they can scatter thermal phonons and degrade matrix creep resistance by enhancing grain-boundary sliding. To eliminate these defects and improve the thermomechanical properties of ceramic composites with CVI SiC matrices, researchers at the NASA Glenn Research Center developed a high-temperature treatment process that can be used after the CVI SiC matrix is deposited into the fiber preform.

Bhatt, Ramakrishna T.↗