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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Observation of a super-tetrahedral cluster of acetonitrile-solvated dodecaborate dianion via dihydrogen bonding

We launched a combined negative ion photoelectron spectroscopy and multiscale theoretical investigation on the geometric and electronic structures of a series of acetonitrile-solvated dodecaborate clusters, i.e., B 12 H 12 2- ·nCH 3 CN (n = 1–4). The electron binding energies of B 12 H 12 2- ·nCH 3 CN are observed to increase with cluster size, suggesting their enhanced electronic stability. B3LYP-D3(BJ)/ma-def2-TZVP geometry optimizations indicate each acetonitrile molecule binds to B 12 H 12 2- via a threefold dihydrogen bond (DHB) B3–H3 ⋮⋮⋮ H3C–CN unit, in which three adjacent nucleophilic H atoms in B 12 H 12 2- interact with the three methyl hydrogens of acetonitrile. The structural evolution from n = 1 to 4 can be rationalized by the surface charge redistributions through the restrained electrostatic potential analysis. Notably, a super-tetrahedral cluster of B 12 H 12 2- solvated by four acetonitrile molecules with 12 DHBs is observed. The post-Hartree–Fock domain-based local pair natural orbital- coupled cluster singles, doubles, and perturbative triples [DLPNO-CCSD(T)] calculated vertical detachment energies agree well with the experimental measurements, confirming the identified isomers as the most stable ones. Furthermore, the nature and strength of the intermolecular interactions between B 12 H 12 2- and CH 3 CN are revealed by the quantum theory of atoms-in-molecules and the energy decomposition analysis. Ab initio molecular dynamics simulations are conducted at various temperatures to reveal the great kinetic and thermodynamic stabilities of the selected B 12 H 12 2- ·CH 3 CN cluster. The binding motif in B 12 H 12 2- ·CH 3 CN is largely retained for the whole halogenated series B 12 X 12 2- ·CH 3 CN (X = F–I). This study provides a molecular-level understanding of structural evolution for acetonitrile-solvated dodecaborate clusters and a fresh view by examining acetonitrile as a real hydrogen bond (HB) donor to form strong HB interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

New Developments in the Embedded Statistical Coupling Method: Atomistic/Continuum Crack Propagation

A concurrent multiscale modeling methodology that embeds a molecular dynamics (MD) region within a finite element (FEM) domain has been enhanced. The concurrent MD-FEM coupling methodology uses statistical averaging of the deformation of the atomistic MD domain to provide interface displacement boundary conditions to the surrounding continuum FEM region, which, in turn, generates interface reaction forces that are applied as piecewise constant traction boundary conditions to the MD domain. The enhancement is based on the addition of molecular dynamics-based cohesive zone model (CZM) elements near the MD-FEM interface. The CZM elements are a continuum interpretation of the traction-displacement relationships taken from MD simulations using Cohesive Zone Volume Elements (CZVE). The addition of CZM elements to the concurrent MD-FEM analysis provides a consistent set of atomistically-based cohesive properties within the finite element region near the growing crack. Another set of CZVEs are then used to extract revised CZM relationships from the enhanced embedded statistical coupling method (ESCM) simulation of an edge crack under uniaxial loading.

Saether, E.↗
NASA NTRS2011

Analysis of Ultra High Resolution Sea Surface Temperature Level 4 Datasets

Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.

sea surface temperature SST)↗
DOE OSTI2026

Prelithiated SiO x /Graphite-NMC811 Cells: Capacity Loss, Impedance Rise and Hidden Degradation Pathways Revealed Using Three-Electrode Diagnostics

Electrodes containing SiO x /graphite (Gr) materials are attractive as anodes for high-energy lithium-ion batteries. However, their mechanical deformation, electrochemical response, and impedance evolution during long-term cycling are strongly coupled, complicating accurate diagnosis of performance fade mechanisms. In this work, the behavior of electrochemically prelithiated SiO x /Gr anodes paired with NMC811 cathodes is systematically investigated using techniques that include in-situ dilatometry, three-electrode electrochemistry, and multiscale post-cycling microscopy. The SiO x /Gr electrode exhibits a maximum expansion of 49% upon lithiation to 10 mV vs Li + /Li, with 84% of the expansion and 91% of the capacity being reversible. In full cells, relatively stable cycling with only 12% capacity fade over 500 cycles is observed. Three-electrode experiments reveal cell-level impedance growth, which is dominated by the NMC811 cathode: the SiO x /Gr anode exhibits minimal net impedance rise and an initial impedance decrease at low potentials. Despite this apparent electrochemical stability, cross-sectional SEM, PFIB tomography, and cryo-STEM reveal irreversible anode thickening caused by the accumulation of an inorganic-rich solid electrolyte interphase (SEI) permeating the anode bulk. Electrode potential-shift analysis further demonstrates that Li + ions released from lithium reservoirs in the prelithiated anode mask true lithium inventory loss during aging. These results demonstrate that low-expansion SiO x /Gr anodes can simultaneously exhibit favorable cycling and impedance metrics while undergoing substantial, hidden degradation, underscoring the importance of electrode-resolved diagnostics for evaluating prelithiated silicon-based anodes.

25 ENERGY STORAGE↗
DOE OSTI2020

The Role of Multiscale Interaction in Tropical Cyclogenesis and Its Predictability in Near-Global Aquaplanet Cloud-Resolving Simulations

Tropical cyclogenesis (TCG) is a multiscale process that involves interactions between large-scale circulation and small-scale convection. A near-global aquaplanet cloud-resolving model (NGAqua) with 4-km horizontal grid spacing that produces tropical cyclones (TCs) is used to investigate TCG and its predictability. This study analyzes an ensemble of three 20-day NGAqua simulations, with initial white-noise perturbations of low-level humidity. TCs develop spontaneously from the northern edge of the intertropical convergence zone (ITCZ), where large-scale flows and tropical convection provide necessary conditions for barotropic instability. Zonal bands of positive low-level absolute vorticity organize into cyclonic vortices, some of which develop into TCs. A new algorithm is developed to track the cyclonic vortices. A vortex-following framework analysis of the low-level vorticity budget shows that vertical stretching of absolute vorticity due to convective heating contributes positively to the vorticity spinup of the TCs. A case study and composite analyses suggest that sufficient humidity is key for convective development. TCG in these three NGAqua simulations undergoes the same series of interactions. The locations of cyclonic vortices are broadly predetermined by planetary-scale circulation and humidity patterns associated with ITCZ breakdown, which are predictable up to 10 days. Whether and when the cyclonic vortices become TCs depend on the somewhat more random feedback between convection and vorticity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multiscale Reactive Model for 1,3,5-Triamino-2,4,6-trinitrobenzene Inferred by Reactive MD Simulations and Unsupervised Learning

When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behaviors is systematically induced. Herein we develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) at the mesoscopic scale, where the chemical behavior is determined by underlying microscopic reactive simulations. The slow carbon cluster formation is not discussed in the present work. All-atom reactive molecular dynamics (MD) simulations are performed with the ReaxFF potential, and a reduced-order chemical kinetics model for TATB is fitted to isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four-component model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that non-negative matrix factorization captures the main chemical decomposition steps of TATB and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism, and simulation results are compared one-to-one against MD simulations of 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both the temporal and spatial evolution of the temperature field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. Here, we show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Alumina – Stabilized SEI and CEI in Potassium Metal Batteries

Aluminum oxide (Al 2 O 3 ) nanopowder is spin-coated onto both sides of commercial polypropene separator to create artificial solid-electrolyte interphase (SEI) and artificial cathode electrolyte interface (CEI) in potassium metal batteries (KMBs). Here, this significantly enhances the stability, including of KMBs with Prussian Blue (PB) cathodes. For example, symmetric cells are stable after 1,000 cycles at 0.5 mA/cm 2 –0.5 mAh/cm 2 and 3.0 mA/cm 2 –0.5 mAh/cm 2 . Alumina modified separators promote electrolyte wetting and increase ionic conductivity (0.59 vs. 0.2 mS/cm) and transference number (0.81 vs. 0.23). Cryo-stage focused ion beam (cryo-FIB) analysis of cycled modified anode demonstrates dense and planar electrodeposits, versus unmodified baseline consisting of metal filaments (dendrites) interspersed with pores and SEI. Alumina-modified CEI also suppresses elemental Fe crossover and reduces cathode cracking. Mesoscale modeling of metal – SEI interactions captures crucial role of intrinsic heterogeneities, illustrating how artificial SEI affects reaction current distribution, conductivity and morphological stability.

25 ENERGY STORAGE↗
DOE OSTI2024

Structural complexity of snapshots of two-dimensional Fermi-Hubbard systems

The development of quantum gas microscopy for two-dimensional optical lattices has provided an unparalleled tool to study the Fermi-Hubbard model (FHM) with ultracold atoms. Spin-resolved projective measurements, or snapshots, have played a significant role in quantifying correlation functions, theory verification, and thus the uncovering of underlying physical phenomena such as antiferromagnetism at commensurate filling on bipartite lattices and other charge and spin correlations, as well as dynamical properties at various densities. Here we employ a recent concept, the multiscale structural complexity, and show that when computed for the snapshots (of single spin species, local moments, or total density) it can provide a theory-free property, immediately accessible to experiments. Specifically, after benchmarking results for Ising and $XY$ models, we study the structural complexity of snapshots of the repulsive FHM in the two-dimensional square lattice as a function of doping and temperature. We generate projective measurements using determinant quantum Monte Carlo and compare their complexities against those from the experiment. We demonstrate that these complexities are linked to relevant physical observables such as the entropy and double occupancy. Their behaviors capture the development of correlations and relevant length scales in the system. Furthermore, we provide an open-source code in python which can be implemented into data analysis routines in experimental settings for the square lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Repairing High γ’ Hot Section Gas Turbine Components Using Advanced Manufacturing

This article describes the ability to use laser-blown powder deposition to repair high γ' IN-100 superalloy gas turbine components. The influence of various process conditions on the ability to make crack-free IN-100 deposits over surrogate high γ' alloys was investigated to identify cracking mechanisms in the deposit and heat-affected zones (HAZs). The various crack formation mechanisms, such as solidification cracking and liquation cracking, were evaluated using multiscale characterization and numerical simulation. The cracking in the deposit region was predominantly solidification cracking, while those observed in the HAZ were liquation cracking. The results showed that controlling thermally induced residual stresses is the key to eliminating cracking, and the optimum preheat temperature was determined. In conclusion, the results were then contrasted with those in published literature and an approach to effectively repair hot section parts was presented.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights

Abstract Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year‐round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co‐management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

Livernois, Mariah C.↗
DOE OSTI2024

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multiscale Investigation of the Mechanisms Controlling the Corrosion of Borosilicate Glasses in Hyper-Alkaline Media

The overarching goal of the present multiscale investigation is to unearth the kinetics and mechanisms of corrosion of borosilicate glasses in hyper-alkaline (pH = 13) environments as a function of their chemical composition. Accordingly, a series of 3- to 6-component borosilicate glasses have been designed starting from Na 2 O–B 2 O 3 –SiO 2 ternary, wherein the compositional complexity has been added in a systematic tiered approach, finally resulting in the composition of the well-known international simple glass (ISG). Tetramethylammonium hydroxide (TMAH), one of the most widely used alkaline etchant in the glass and electronics industry, has been used as the corrosion media. A series of state-of-the-art characterization techniques including magic angle spinning nuclear magnetic resonance spectroscopy, X-ray photoelectron spectroscopy, inductively coupled plasma optical emission spectroscopy, elastic recoil detection analysis, and high-resolution transmission electron microscopy have been employed to unearth the compositional dependence of glass corrosion in hyper-alkaline environments. The glass compositions underwent congruent corrosion in the forward rate regime, whereas the controlling mechanism of corrosion in the residual rate regime depends on the presence/absence of Ca in the surrounding environment and can be explained on the basis of the dissolution–reprecipitation model. The dependence of corrosion kinetics and the chemistry of alteration products (in the residual rate regime) on the glass composition have been discussed. Here, the results presented in this contribution will ultimately supplement the scientific literature attempting to understand the fundamental science governing the aqueous corrosion of silicate-based glass chemistries and add to the growing database required to develop non-empirical predictive models for designing glasses with controlled dissolution rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Ionic Interdiffusion at Cathode|Solid-Electrolyte Interface: A Machine Learning–Assisted Multiscale Investigation and Mitigation Strategies

Future lithium batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. However, most solid electrolytes are thermodynamically unstable against layered oxide cathodes. In this study, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivating interphase layer, and corresponding decay in cell performance is addressed using a continuum model. Large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of cobalt (Co) and other ionic species, leading to the formation and growth of a resistive interphase and to dramatic capacity fade even in the first cycle. We examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that substituting lithium (Li) in LNTO with Co is thermodynamically unfavorable, thereby inhibiting ionic interdiffusion. The stable Nb5+/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of niobium (Nb) or tantalum (Ta). However, continuum-level analysis suggests that the higher mechanical stiffness of LNTO can lead to interfacial delamination between the LCO and LNTO. This phenomenon reduces the effectiveness of the protective layer. This paper, therefore, highlights the need to develop novel interlayers that balance low ionic interdiffusion with low mechanical stiffness.

Ncube, Musawenkosi K.↗
DOE OSTI2022

Helium interaction with solutes and impurities in neutron-irradiated nanostructured ferritic alloys: A first principles study

Density functional theory calculations are performed to explore the binding between He and alloying solutes, impurities, and transmutation products expected in neutron irradiated nanostructured ferritic alloys (NFAs), here 14YWT is taken as an example. Elements that exhibit significant binding (attraction) with an interstitial He are Y (binding energy = 0.46 eV), Mg (0.32), O (0.33), Ti (0.16), and C (0.15). Those that provide significant binding to a substitutional He are O (1.44), Y (1.24), N (0.73), H (0.56), Mg (0.52), Ti (0.34), Si (0.34), C (0.33), Al (0.32), Ni (0.26), Ta (0.23), and Mn (0.16). The presence of these elements in Fe matrix could reduce the transport of He towards oxide particles, dislocations, and internal boundaries, and could promote He bubble nucleation in the matrix. For convenience, we compile existing binding energy data of He with He n and He n V (He-vacancy) clusters. Dissociation pathway analysis reveals that, in general, the most likely dissociation of a He n V cluster is by a sequential emission of individual He atoms. Furthermore, larger bubbles are more prone to dissociation than smaller ones. In addition, we estimate the binding energy (segregation energy) of He in bulk Y 2 Ti 2 O 7 (YTO) single crystal, YTO/Fe interface, and YTO particle embedded in Fe, with respect to interstitial He in Fe, from existing formation energies of He in these structures. We also compile available data of He binding with Fe self-interstitial atom (SIA), SIA clusters, and edge and screw dislocations. Note that given the absence of DFT data, the binding with SIA clusters and dislocations are gathered from simulations with empirical potentials. Finally, the data presented in this paper is important to inform multiscale simulations of He bubble accumulation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach

Abstract Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li 7- x La 3 Zr 2 O 12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.

25 ENERGY STORAGE↗
NASA NTRS2020

An Algorithm for Characterization of Fiber Aggregation in Composite Microstructures

Composite structures are susceptible to localized flaws, or variability, that drive global failure. To capture this variance, a multiscale model needs to be introduced that not only accurately represents the statistical nature of the composite microstructure but is also efficient. At the microscale, fiber aggregation creates local stress concentrations, where failure is likely to occur sooner than expected. A method of rapid microstructure generation, in conjunction with cluster characterization, is examined to develop accurate reproductions of 2D composite cross-sections. Parameterization of shape and size of fiber clusters is used to characterize representative volume elements. The discrete element method is used to generate pseudo-microstructures. The clustering parameters from the pseudo- and actual microstructure arrangements (obtained from micrographs) can be compared to determine the validity of the representative volume element generated with the discrete element method. Results show a promising approach to evaluating randomness of fiber distributions and how to accurately recreate microstructures for strength analysis.

Carbon fiber↗