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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterising Ion-Irradiated FeCr: Hardness, Thermal Diffusivity and Lattice Strain

Ion-irradiated FeCr alloys are useful for understanding and predicting neutron damage in the structural steels of future nuclear reactors. Previous studies have largely focused on the structure of irradiation induced defects, probed by transmission electron microscopy (TEM), as well as changes in mechanical properties. Across these studies, a wide range of irradiation conditions has been employed on samples with different processing histories, which complicates the analysis of the relationship between defect structures and material properties. Furthermore, key properties, such as irradiation-induced changes in thermal transport and lattice strain, are little explored. Here we present a systematic study of Fe3Cr, Fe5Cr and Fe10Cr binary alloys implanted with 20 MeV Fe3+ ions to nominal doses of 0.01 dpa and 0.1 dpa at room temperature. Nanoindentation, transient grating spectroscopy (TGS) and X-ray micro-beam Laue diffraction were used to study the changes in hardness, thermal diffusivity and strain in the material as a function of damage and Cr content. Our results suggest that Cr leads to an increased retention of irradiation-induced defects, causing substantial changes in hardness and lattice strain. However, thermal diffusivity varies little with increasing damage and instead degrades significantly with increasing Cr content in the material. We find significant lattice strains even in samples exposed to a nominal displacement damage of 0.01 dpa. The defect density predicted from the lattice strain measurements is significantly higher than that observed in previous TEM studies, suggesting that TEM may not fully capture the irradiation-induced defect population. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

FeCr alloys↗
NASA NTRS1999

Numerical Analysis of Temperature Gradients and Interface Shape During Directional Solidification of Al and Al-Cu Alloy Under Microgravity Conditions

Numerical modeling was undertaken to analyze the influence of radial thermal gradient on solid/liquid (s/1) interface shape and convection patterns during solidification of pure Al and Al-4 wt% Cu alloy. The objective of the numerical task was to predict the influence of convective velocity on an insoluble particle near a s/l interface. These predictions would then be used to define the minimum gravity level (g) required to investigate the fundamental physics of interaction between a particle and a s/I interface. To satisfy this objective, steady state calculations were performed for different gravity levels and orientations with the gravity vector. ne furnace configuration used in this analysis is the proposed International Space Station Furnace, Quench Module Insert (QMI) 1. Results from a thermal model of the furnace core were used as initial boundary conditions for solidification modeling. General model of binary alloy solidification was based on the finite element code FIDAP. It was found that for the worst case orientation of 90 degrees with the gravity vector and a g level of 10(exp -4)g(sub o) (g(sub o) = 9.8 m/s(exp 2)) the dominant forces acting on the particle would be the fundamental drag and interfacial forces.

Bune, Andris V.↗
NASA NTRS2000

Effect of Melt Convection at Various Gravity Levels and Orientations on the Forces Acting on a Large Spherical Particle in the Vicinity of a Solidification Interface

Numerical modeling was Undertaken to analyze the influence of both radial and axial thermal gradients on convection patterns and velocities claiming solidification of pure Al and an Al-4 wt% Cu alloy. The objective of the numerical task was to predict the influence of convective velocity on an insoluble particle near a solid/liquid (s/l) interface. These predictions were then be used to define the minimum gravity level (q) required to investigate the fundamental physics of interactions between a particle and a s/l interface. This is an ongoing NASA founded flight experiment entitled "particle engulfment and pushing by solidifying interfaces (PEP)". Steady-state calculations were performed for different gravity levels and orientations with respect to the gravity vector The furnace configuration used in this analysis is the quench module insert (QMI-1) proposed for the Material Science Research Facility (MSRF) on board the International Space Station (ISS). The general model of binary alloy solidification was based on the finite element code FIDAP. At a low g level of 10(exp -4) g(sub o) (g(sub o) = 9.8 m/square s) maximum melt convection was obtained for an orientation of 90 deg. Calculations showed that even for this worst case orientation the dominant forces acting on the particle are the fundamental drag and interfacial forces.

Bune, Andris V.↗
DOE OSTI2021

Grain size dependencies of intergranular solute segregation in nanocrystalline materials

Grain boundary segregation is of increasing importance for stabilizing nanocrystalline alloys, and while segregation is typically modeled as a scale-independent phenomenon, at the finest nanocrystalline grain sizes there are several sources of grain size dependence. Here we provide quantitative detail on two grain size dependencies not previously elaborated, both of which arise from the spectrality of intergranular segregation sites. The first of these arises because a change in grain size affects which portions of the segregation spectrum are occupied by the solutes, with no change in the shape or nature of the spectrum itself. The second of these arises from the presence of higher-order grain junctions, which have unique segregation behaviors and an increasing relative presence at finer grain sizes. This paper uses molecular statics to quantify these size dependencies in detail for the case of an Al(Mg) binary alloy. Together, these two effects combined have a significant effect on the effective McLean segregation energy of more than 6 kJ/mol (from approximately -18.5 kJ/mol at 5 nm to -12.5 kJ/mol at more than 40 nm), with the majority of grain size dependence arising from the spectrality of segregation sites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A stress-sensitive precipitate nucleation model beyond classical nucleation theory

The dynamic evolution of precipitates and second phases dictates the strength and stability of most engineering alloys. By design, or as a consequence of thermo-mechanical aging, engineering metals and alloys often form precipitates of second phases when subjecting to diverse thermal and mechanical loads. Precipitation is governed by several factors, including the alloy’s composition, processing/operating temperature, and stresses — either as a result of external loads or from residual stresses. However, state-of-the-art models for precipitate nucleation (i.e., classical nucleation theory) typically lacks consistent method to capture the effects of externally applied and/or internal stresses on nucleation; thereby severely limiting the applicability of these models to complex materials systems and to representative loading scenarios. Here, in this work, we extend upon classical nucleation theory to account for the effect of stresses on precipitation kinetics and thermodynamics. This is achieved via the use of an Eshelbian micromechanics framework keeping track of (i) the stress build up resulting from second phase formation as a function of mechanical load and, (ii) the effects of dislocations on precipitate formation. This new model is applied to σ precipitate in Fe–Cr binary alloys and M 23 C 6 precipitate in 316H stainless steel (SS). Simulations demonstrate the important role of both the remotely applied loads and dislocation pile ups on precipitate nucleation.

36 MATERIALS SCIENCE↗
NASA NTRS1985

MPS solidification model. Analysis and calculation of macrosegregation in a casting ingot

Work performed on several existing solidification models for which computer codes and documentation were developed is presented. The models describe the solidification of alloys in which there is a time varying zone of coexisting solid and liquid phases; i.e., the S/L zone. The primary purpose of the models is to calculate macrosegregation in a casting or ingot which results from flow of interdendritic liquid in this S/L zone during solidification. The flow, driven by solidification contractions and by gravity acting on density gradients in the interdendritic liquid, is modeled as flow through a porous medium. In Model 1, the steady state model, the heat flow characteristics are those of steady state solidification; i.e., the S/L zone is of constant width and it moves at a constant velocity relative to the mold. In Model 2, the unsteady state model, the width and rate of movement of the S/L zone are allowed to vary with time as it moves through the ingot. Each of these models exists in two versions. Models 1 and 2 are applicable to binary alloys; models 1M and 2M are applicable to multicomponent alloys.

Poirier, D. R.↗
DOE OSTI2022

Structural heterogeneity in non-crystalline Te x Se1−x thin films

Rapid crystallization behavior of amorphous TexSe1−x thin films limits the use of these alloys as coatings and in optoelectronic devices. Understanding the short- and medium-range ordering of the amorphous structure and the fundamental physics governing the crystallization of the films is crucial. Although the lack of long range crystalline order restricts the characterization of the amorphous films, electron microscopy offers a way to extract information about the nanoscale ordering. In this paper, the local ordering of amorphous TexSe1−x thin films with x=0.22, 0.61, 0.70, 0.90, and 1 grown by thermal evaporation is investigated using radial distribution function (RDF) and fluctuation electron microscopy (FEM) analysis. RDF results show that the nearest-neighbor distances of selenium (Se) and tellurium (Te) in their crystalline structure are preserved, and their bond lengths increase with the addition of Te. Density functional theory (DFT) calculations predict structures with interatomic distances similar to those measured experimentally. Additionally, fluctuations in atomic coordination are analyzed. Medium range order (MRO) analysis obtained from FEM and DFT calculations suggests that there are at least two populations within the chain network structure, which are close to the Se–Se and Te–Te intrachain distances. For the binary alloy with x > 0.61, TexSe1−x, Te–Te like populations increase and Te fragments might form, suggesting that the glass forming ability decreases rapidly.

Sari, Bengisu (ORCID:0000000244217098)↗
DOE OSTI2024

First Principles Evaluation of Phase Stability in the In-Sn Binary System

Abstract The In-Sn binary alloy system exhibits several unusual features that challenge crystallographic and thermodynamic expectations. We combine first principles total energy calculation with simple thermodynamic modeling to address two key points. First, we evaluate energies along the Bain path to interpret the discontinuous transition between the phases α-In (Pearson type tI2) and β-In 3 Sn (also Pearson type tI2) that are identical in symmetry. Second, we demonstrate that the solid solution phases β-In 3 Sn and γ-InSn 4 (Pearson type hP1) exist at high temperatures only, and they exhibit eutectoid decompositions at low temperatures.

Chemistry↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analytical gradient-based optimization of CALPHAD model parameters

The calibration of CALPHAD (CALculation of PHAse Diagrams) models involves the solution of a very challenging high-dimensional multiobjective optimization problem. Traditional approaches to parameter fitting predominantly rely on gradient-free methods, which while robust, are computationally inefficient and often scale poorly with model complexity. In this work, we introduce and demonstrate a generalizable framework for analytic gradient-based optimization of the parameters of the CALPHAD model enabled by the recently formalized Jansson derivative technique. This method allows for efficient evaluation of gradients of thermodynamic properties at equilibrium with respect to model parameters, even in the presence of arbitrarily complex internal degrees of freedom. Leveraging these semi-analytic gradients, we employ the conjugate gradient (CG) method to optimize thermodynamic model parameters for four binary alloy systems: Cu-Mg, Fe-Ni, Cr-Ni, and Cr-Fe. Across all systems, CG achieves comparable or superior optimality relative to Bayesian ensemble Markov Chain Monte Carlo (MCMC) with improvements in computational efficiency ranging from one to three orders of magnitude. Furthermore, our results establish a new paradigm for CALPHAD assessments in which high fidelity data-rich model calibration becomes tractable using deterministic gradient-informed algorithms.

CALPHAD↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reconstructing parent microstructures in martensitic and pearlitic Ti-Cu

Evaluating the microstructural evolution of parent phases has long been a challenge in metals with a partial or complete solid state transformation. Most parent microstructure evolution has to be inferred from the product microstructure, with some information permanently lost. Parent microstructure reconstructions relying on orientation relationships and EBSD data are a useful tool to overcome this challenge, though much background knowledge is required to do so. Here, this work introduces a reconstruction process for a eutectoid composition Ti-Cu binary alloy to evaluate the high temperature β-Ti phase from martensitic and pearlitic product microstructures. Martensitic microstructures were accurately reconstructed, enabling elucidation of β-Ti grain size and texture, and the employed script included for scientific reference. Pearlitic microstructures did not reconstruct effectively with the current process. However, new insight into the interfacial crystallographic orientations of pearlitic Ti-Cu microstructures was gleaned via this analysis, and future investigations of interest are discussed accordingly. Ultimately, these findings demonstrate tools widely implemented on conventional titanium and ferrous alloys can also be applied to understand parent microstructures in Ti-Cu and other alloys of emerging interest. This is especially of use as new manufacturing processes for such materials require understanding how parent phase microstructures respond to new, novel material processing such as AM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Utilizing Site Disorder in the Development of New Energy-Relevant Semiconductors

We report controlling site disorder in ternary and multinary compounds enables tuning optical and electronic properties at fixed lattice constants and stoichiometries, moving beyond many of the challenges facing binary alloy systems. Here, we consider possible enhancements to energy-related applications through the integration of disorder-tunable materials in devices such as light-emitting diodes, photonics, photovoltaics, photocatalytic materials, batteries, and thermoelectrics. However, challenges remain in controlling and characterizing disorder. Focusing primarily on II–IV–V 2 materials, we identify three metrics for experimentally characterizing cation site disorder. Complementary to these experiments, we discuss simulation methods to understand disordered materials. Nonidealities, such as off-stoichiometry and oxygen incorporation, can occur while synthesizing metastable disordered materials. While nonidealities may seem undesirable, we describe how if harnessed they could provide another knob for tuning disorder and subsequently properties. To illustrate the effects of disorder on device-relevant properties, we provide case examples of disordered materials and their potential in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring topological transitions in pattern-forming processes with self-supervised learning

Abstract The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.

Abram, Marcin↗
DOE OSTI2024

Computed entropy spectra for grain boundary segregation in polycrystals

Abstract Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

Chemistry↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phase-field model of freeze casting

Directional solidification of water-based solutions has emerged as a versatile technique for templating hierarchical porous materials. However, the underlying mechanisms of pattern formation remain incompletely understood. In this work, we present a detailed derivation and analysis of a quantitative phase-field model for simulating this nonequilibrium process. The phase-field model extends the thin-interface formulation of dilute binary alloy solidification with antitrapping to incorporate the highly anisotropic energetic and kinetic properties of the partially faceted ice-water interface. This interface is faceted in the basal plane normal to the ⟨0001⟩ directions and atomically rough in other directions within the basal plane. On the basal plane, the model reproduces a linear or nonlinear relationship between the interface growth rate and the kinetic undercooling that can be linked to experimental measurements. In both cases, spontaneous parity breaking of the solidification front is observed when the preferred growth direction is aligned with the temperature gradient. This phenomenon leads to the formation of partially faceted ice lamellae that drift laterally in one of the ⟨0001⟩ directions. Here, we demonstrate that the drifting velocity of the ice lamellae is controlled by the kinetics on the basal plane and converges as the thickness of the diffuse solid-liquid interface decreases. Furthermore, we examine the effect of the form of the kinetic anisotropy, which is chosen here such that the inverse of the kinetic coefficient varies linearly from a finite value in the ⟨0001⟩ directions to zero in all other directions within the basal plane, consistent with the assumption that the interface grows in local thermodynamic equilibrium in this plane. Our results indicate that the drifting velocity of ice lamellae is not affected by the slope of this linear relation, and the radius and undercooling at the tip of an ice lamella converge at relatively small slope values. Consequently, the phase-field simulations remain quantitative with computationally tractable choices of both the interface thickness and the slope assumed in the form of the kinetic anisotropy.

Materials science↗
DOE OSTI2025

Stability and Mobility of Disconnections in Solute Atmospheres: Insights from Interfacial Defect Diagrams

This Letter explores the stability of disconnections (step-dislocation defects) at grain boundaries in binary alloys. We introduce interfacial defect diagrams, derived from atomistic simulations and segregation theory, to predict the stability of disconnections in the temperature-solute concentration phase space and relate it to governing segregation mechanisms. These diagrams reveal multiple stability regimes influenced by solute-induced clustering and pinning effects impacting the thermal migration of disconnections and offering insights into their thermodynamics and kinetic properties.

Crystal structure↗