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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

"Sintering" Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

3D printing, Additive Manufacturing, polymer, sint↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2015

Physical formulation and numerical algorithm for simulating N immiscible incompressible fluids involving general order parameters

We present a family of physical formulations, and a numerical algorithm, based on a class of general order parameters for simulating the motion of a mixture of N (N⩾2) immiscible incompressible fluids with given densities, dynamic viscosities, and pairwise surface tensions. The N-phase formulations stem from a phase field model we developed in a recent work based on the conservations of mass/momentum, and the second law of thermodynamics. The introduction of general order parameters leads to an extremely strongly-coupled system of (N−1) phase field equations. On the other hand, the general form enables one to compute the N-phase mixing energy density coefficients in an explicit fashion in terms of the pairwise surface tensions. We show that the increased complexity in the form of the phase field equations associated with general order parameters in actuality does not cause essential computational difficulties. Our numerical algorithm reformulates the (N−1) strongly-coupled phase field equations for general order parameters into 2(N−1) Helmholtz-type equations that are completely de-coupled from one another. This leads to a computational complexity comparable to that for the simplified phase field equations associated with certain special choice of the order parameters. We demonstrate the capabilities of the method developed herein using several test problems involving multiple fluid phases and large contrasts in densities and viscosities among the multitude of fluids. In particular, by comparing simulation results with the Langmuir–de Gennes theory of floating liquid lenses we show that the method using general order parameters produces physically accurate results for multiple fluid phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Nanoscale Control of Phase Variants in Strain-Engineered BiFeO3

Development of magnetoelectric, electromechanical, and photovoltaic devices based on mixed-phase rhombohedral tetragonal (R-T) BiFeO3 (BFO) systems is possible only if the control of the engineered R phase variants is realized. Accordingly, we explore the mechanism of a bias induced phase transformation in this system. Single point spectroscopy demonstrates that the T->R transition is activated at lower voltages compared to T->T polarization switching. With phase field modeling, the transition is shown to be electrically driven. We further demonstrate that symmetry of formed R-phase rosettes can be broken by a proximal probe motion, allowing controlled creation of R variants with defined orientation. This approach opens a pathway to designing next-generation magnetoelectronic and data storage devices in the nanoscale.

Bismuth ferrite↗
DOE OSTI2018

Role of Reversible Phase Transformation for Strong Piezoelectric Performance at the Morphotropic Phase Boundary

A functional material with coexisting energetically equivalent phases often exhibits extraordinary properties such as piezoelectricity, ferromagnetism, and ferroelasticity, which is simultaneously accompanied by field-driven reversible phase transformation. The study on the interplay between such phase transformation and the performance is of great importance. Here, we have experimentally revealed the important role of field-driven reversible phase transformation in achieving enhanced electromechanical properties using in situ high-energy synchrotron x-ray diffraction combined with 2D geometry scattering technology, which can establish a comprehensive picture of piezoelectric-related microstructural evolution. High-throughput experiments on various Pb/Bi-based perovskite piezoelectric systems suggest that reversible phase transformation can be triggered by an electric field at the morphotropic phase boundary and the piezoelectric performance is highly related to the tendency of electric-field-driven phase transformation. A strong tendency of phase transformation driven by an electric field generates peak piezoelectric response. Further, phase-field modeling reveals that the polarization alignment and the piezoelectric response can be much enhanced by the electric-field-driven phase transformation. In conclusion, the proposed mechanism will be helpful to design and optimize the new piezoelectrics, ferromagnetics, or other related functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Determination of permeability for hydrocarbon release due to excavation-induced stress redistribution in rock salt

Due to a stress redistribution after the excavation of an underground tunnel for radioactive waste disposal, an Ed/DZ (excavation disturbed/damaged zone) will be generated in the near field of the opening, resulting in significant changes in the hydraulic and mechanical properties of the rock mass in the zone. Initially more or less randomly distributed hydrocarbons at grain boundaries in rock salt, which sometimes can only be observed with ultraviolet light, can then be mobilised and migrate at a potentially significant rate towards the excavation. Within the international cooperative project DECOVALEX 2019, the migration mechanism of such fluid inclusions in rock salt is being studied intensively. Specifically, a multi-scale modelling strategy has been developed. A macroscale coupled hydro-mechanical modelling of an underground excavation was performed to determine hydraulic and time-dependent deviatoric stress conditions, by taking into account the rock salt creep behaviour. Under the obtained macro-scale constraints, micro-scale modelling of a pathway dilation along halite grain boundaries was performed using different model strategies: a) coupled hydromechanical modelling with a consideration of hydraulic pressure-induced dilatant deformation, b) nonlinear dynamic model taking account of fluid migration, stress-dependent grain boundary wetting and shear-induced dilatancy of salt, and c) phase-field modelling of flow pathway propagation. The permeability increase resulting from the pathway dilation is estimated to be as high as two orders of magnitude. Based on the permeability determined, a series of pressure buildups measured from a borehole with a high hydrocarbon release rate, a total of 430 build-ups within a monitoring time of 938 days, can be simulated with a macro-scale compressible flow model accounting for different zones around the opening.

58 GEOSCIENCES↗
DOE OSTI2022

Cation reordering instead of phase transitions: Origins and implications of contrasting lithiation mechanisms in 1D ζ- and 2D α-V 2 O 5

Substantial improvements in cycle life, rate performance, accessible voltage, and reversible capacity are required to realize the promise of Li-ion batteries in full measure. Here, we have examined insertion electrodes of the same composition (V 2 O 5 ) prepared according to the same electrode specifications and comprising particles with similar dimensions and geometries that differ only in terms of their atomic connectivity and crystal structure, specifically two-dimensional (2D) layered α-V 2 O 5 that crystallizes in an orthorhombic space group and one-dimensional (1D) tunnel-structured ζ-V 2 O 5 crystallized in a monoclinic space group. By using particles of similar dimensions, we have disentangled the role of specific structural motifs and atomistic diffusion pathways in affecting electrochemical performance by mapping the dynamical evolution of lithiation-induced structural modifications using ex situ scanning transmission X-ray microscopy, operando synchrotron X-ray diffraction measurements, and phase-field modeling. We find the operation of sharply divergent mechanisms to accommodate increasing concentrations of Li-ions: a series of distortive phase transformations that result in puckering and expansion of interlayer spacing in layered α-V 2 O 5 , as compared with cation reordering along interstitial sites in tunnel-structured ζ-V 2 O 5 . By alleviating distortive phase transformations, the ζ-V 2 O 5 cathode shows reduced voltage hysteresis, increased Li-ion diffusivity, alleviation of stress gradients, and improved capacity retention. The findings demonstrate that alternative lithiation mechanisms can be accessed in metastable compounds by dint of their reconfigured atomic connectivity and can unlock substantially improved electrochemical performance not accessible in the thermodynamically stable phase.

25 ENERGY STORAGE↗
DOE OSTI2018

Electronic switching by metastable polarization states in BiFe O 3 thin films

Here, we present an approach to control resistive switching in metal-ferroelectric contacts using a radially symmetric electric field. In ferroelectrics with significant polarization along the corresponding field lines, the field above a critical threshold will induce polarization discontinuity, a corresponding nanoscale volume of space charge, and a conducting junction. We demonstrate this principle using nanoscale polarization switching of a conventional (001)-oriented thin film of BiFeO 3 . Without any optimization, the conducting state created in this regime of resistive switching exhibits local currents of ~1–10nA, approaching the ~100nA threshold required for device implementation [J. Jiang et al., Nat. Mater. 17, 49 (2018)]. The corresponding electronic function is that of a volatile resistive switch, which is directly compatible with neuristor functionality that encodes the functioning basis of an axon [M. D. Pickett et al., Nat. Mater. 12, 114 (2013)]. Phase-field modeling further reveals that in the strongly charged local configuration, BiFeO 3 locally undergoes a rhombohedral-tetragonal (R-T) phase transition, in part due to substantial piezoelectric expansion of the lattice. The estimated local charge density can be as high as ~10 21 cm -3 , which would be extremely difficult to achieve by conventional doping approaches without altering other material properties. Therefore, this method for creating stable and reproducible strongly charged ferroelectric junctions enables more systematic studies of their physical properties, such as the possibility of structural and electronic phase transitions, and it can lead to new ferroelectric devices for advanced information functions.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Early Career: In-situ Monitoring of Dynamic Phenomena during Solidification

Solidification is critical to processes like casting and additive manufacturing and the manufacture of metallic alloy components we use in our everyday lives. State-of-the-art characterization techniques, now available at U.S. DOE User Facilities and in the laboratory, are not only enabling fundamental studies of metallic alloy solidification dynamics, but also in-operando, in-situ deformation, and manufacturing studies. Here we use x-ray, proton, and electron imaging to study solidification dynamics from the micro-scale to the macro-scale, at times ranging from microseconds to minutes. Our experimental results are used to inform, develop, and validate computational models at the same length and time-scales. Integrating in-situ characterization and modeling will yield the prediction and control of metallic alloy solidification dynamics and the creation of microstructures and properties by design with advanced manufacturing. Major research accomplishments during this period include: 1. Exploring dynamical microstructure selection by means of real-time synchrotron x-ray imaging of controlled directional solidification of aluminum alloys and quantitative phase field modeling. 2. In-situ and ex-situ electron microscopy of rapid solidification after laser melting of metallic alloys. 3. Exploring the impact of microstructural heterogeneities that develop during solidification on solid-state phase transitions, with an emphasis on phase growth, (meta)stability, and transition pathways in aluminum alloys. 4. Demonstrations of four-dimensional (three-dimensions plus time) x-ray microscopy during controlled directional solidification and the first quasi-static/dynamic transmission high energy electron microscopy of metallic alloy solidification dynamics. 5. Experiments and modeling of solidification structure evolution and forward modeling and testing of micromechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that draw upon big-data classification problems. Though, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one aspect of applying machine learning to materials physics.

42 ENGINEERING↗
DOE OSTI2017

Two-dimensional lithium diffusion behavior and probable hybrid phase transformation kinetics in olivine lithium iron phosphate

Olivine lithium iron phosphate is a technologically important electrode material for lithium-ion batteries and a model system for studying electrochemically driven phase transformations. Despite extensive studies, many aspects of the phase transformation and lithium transport in this material are still not well understood. Here we combine operando hard X-ray spectroscopic imaging and phase-field modeling to elucidate the delithiation dynamics of single-crystal lithium iron phosphate microrods with long-axis along the [010] direction. Lithium diffusivity is found to be two-dimensional in microsized particles containing ~3%lithium-iron anti-site defects. Our study provides direct evidence for the previously predicted surface reaction-limited phase-boundary migration mechanism and the potential operation of a hybrid mode of phase growth, in which phase-boundary movement is controlled by surface reaction or lithium diffusion in different crystallographic directions. These findings uncover the rich phase-transformation behaviors in lithium iron phosphate and intercalation com-pounds in general and can help guide the design of better electrodes.

25 ENERGY STORAGE↗
DOE OSTI2015

Avalanching strain dynamics during the hydriding phase transformation in individual palladium nanoparticles

Phase transitions in reactive environments are crucially important in energy and information storage, catalysis and sensors. Nanostructuring active particles can yield faster charging/ discharging kinetics, increased lifespan and record catalytic activities. However, establishing the causal link between structure and function is challenging for nanoparticles, as ensemble measurements convolve intrinsic single-particle properties with sample diversity. Here we study the hydriding phase transformation in individual palladium nanocubes in situ using coherent X-ray diffractive imaging. The phase transformation dynamics, which involve the nucleation and propagation of a hydrogen-rich region, are dependent on absolute time (aging) and involve intermittent dynamics (avalanching). A hydrogen-rich surface layer dominates the crystal strain in the hydrogen-poor phase, while strain inversion occurs at the cube corners in the hydrogen-rich phase. A three-dimensional phase-field model is used to interpret the experimental results. In conclusion, our experimental and theoretical approach provides a general framework for designing and optimizing phase transformations for single nanocrystals in reactive environments.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Unraveling irradiation induced grain growth with in situ transmission electron microscopy and coordinated modeling

Nanostructuring has been proposed as a method to enhance radiation tolerance, but many metallic systems are rejected due to significant concerns regarding long term grain boundary and interface stability. This work utilized recent advancements in transmission electron microscopy (TEM) to quantitatively characterize the grain size, texture, and individual grain boundary character in a nanocrystalline gold model system before and after in situ TEM ion irradiation with 10 MeV Si. The initial experimental measurements were fed into a mesoscale phase field model, which incorporates the role of irradiation-induced thermal events on boundary properties, to directly compare the observed and simulated grain growth with varied parameters. The observed microstructure evolution deviated subtly from previously reported normal grain growth in which some boundaries remained essentially static. In broader terms, the combined experimental and modeling techniques presented herein provide future avenues to enhance quantification and prediction of the thermal, mechanical, or radiation stability of grain boundaries in nanostructured crystalline systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Phase field benchmark problems targeting fluid flow and electrochemistry

Here, we continue our development of phase field model benchmark problems with the addition of a third set, complimenting our previously developed problems for diffusion, precipitation, dendritic growth and linear elasticity. These benchmark problems are being jointly developed by the Center for Hierarchical Materials Design (CHiMaD) and the National Institute of Standards and Technology (NIST) along with input from the phase field community. The first problem in this third set targets Stokes flow, with a particular emphasis on flow around an obstruction placed asymmetrically in the domain. While Stokes flow is not traditionally in the canon of phase field problems, it is a class of problems gaining importance in areas such as filtration and water purification. The second problem deals with coupled Cahn-Hilliard diffusion and electrostatic forces, which is an important area in energy storage and battery sciences. We present our own solutions and discuss sources of numerical errors for the Stokes problem as well as simple checks to avoid fundamental issues in the coupled diffusion-electrostatics problem. The latter problem contains some subtleties that we expand on in an Appendix.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An electrochemical mesoscale tool for modeling the corrosion of structural alloys by molten salt

Understanding the impact of microstructure on corrosion rates can aid the development of corrosion-resistant alloys for molten salt reactors. Here in this work, we develop an electrochemical phase-field model for capturing the microstructure-dependent corrosion of structural alloys by molten salts. As a demonstration problem, we apply this model to capture the selective depletion of Cr from Ni-Cr grain boundaries during corrosion in molten FLiBe salt. We perform sensitivity analysis and model verification on 1D simulations to confirm that the model predicts diffusion-limited kinetics. The model is validated using 1D, 2D, and 3D simulations against experimental data for Ni-5Cr and Ni-20Cr corrosion in molten FLiBe. The 1D simulations predict the corrosion behavior with reasonable accuracy when using an effective diffusion coefficient that accurately represents the grain boundary diffusion. 2D simulations that represent the grain structure underpredict the corrosion. 3D simulations that represent the grain structure predict the corrosion with reasonable accuracy. The corrosion rate predicted by the 3D simulations is proportional to the average grain size at the alloy/salt interface.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Phase field benchmark problems for nucleation

In this work, we present nucleation phase field model benchmark problems, expanding on our previous benchmark problems on diffusion, precipitation, dendritic growth, linear elasticity, fluid flow and electrochemistry. Nucleation is the process in which either a new thermodynamic phase or a new structure is created, such as solidification from the melt, or self-assembly of particulates. Based on where the nucleation occurs, it can be divided into two main categories: homogeneous nucleation and heterogeneous nucleation. In the first nucleation benchmark problem, we focus on homogeneous nucleation for both single seed under different initial conditions and multiple seeds. The second nucleation benchmark problem focuses on athermal heterogeneous nucleation and nucleation behavior near the free growth limit with different undercooling driving forces.

36 MATERIALS SCIENCE↗