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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AA 7075 Sheet with 700 MPa Strength for Automotive Structural Components: CRADA 520 [Abstract only]

Fairmount Technologies is developing a new metal forming machine intended to produce ultra-high strength Aluminum Alloy AA 7075 sheets and is seeking to collaborate with PNNL to optimize the production process, as well as the strength and formability of the sheet produced. Results will be used to infer (and optionally demonstrate) whether aluminum components such as side-impact beams can be successfully formed from this sheet.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CRADA Number NFE-21-08827 with DayLyte, Inc. (CRADA Final Report)

DayLyte Batteries is developing an aqueous Sodium-Air battery made with abundant materials while being half to a third the weight of Li-ion to power electric aircraft, electric vehicles and provide renewable energy on demand. To make this battery practical, DayLyte is developing a thin film ceramic Nasicon solid electrolyte amenable to rapid scaleup as well as a protective coating that will protect the solid electrolyte from 50wt% NaOH during the battery’s end-of-discharge while still allowing Na-ions to pass back and forth. If the thin film solid electrolyte and stable coating can be developed it will open the world to step change in battery performance all while not sacrificing the environment on the way. The project was designed to first focus on making and characterizing the Nasicon membrane followed by measuring its electrochemical performance, then assembling a full cell, optimizing its NaOH stability and then optimize the overall prototype design. Over the past two years, we have significantly improved the Nasicon film uniformity, demonstrated protective ligands that are stable in 50wt% NaOH by measuring no mass change with a quartz crystal microbalance, tested different heating methods like tube furnace and rapid heating and developed glassy carbon coatings on the current collector to prevent Nasicon-current collector alloying. We have found that getting film uniformity as measured by electrochemical impedance spectroscopy (EIS) is difficult because of different factors like surface roughness, deposition uniformity and heat treatment time and uniformity. The glassy carbon coating should significantly reduce the surface roughness and by tuning the coating conditions the film deposition uniformity improves and using an automated heating method should lead to much improved film uniformity.

25 ENERGY STORAGE↗
DOE OSTI2021

Creating Hydride Rim Structures in Zircaloy 4 Cladding Tubes to Mimic High Burnup

During their long service in commercial light water reactors, zirconium alloy cladding tubes are subject to oxidation and subsequent hydrogen pickup which can degrade the materials mechanical properties [1]. These degradations can lead to premature cladding ruptures during reactor power transients [2] as well as negatively impact the claddings post quench ductility following high temperature steam oxidation in thermal hydraulic transients [3]. Hydrogen has low solid solubility in zirconium and usually precipitates as brittle zirconium hydride platelets in the cladding metal matrix. Due to temperature driven diffusion (Soret effect) and the grain texture of stress relieved zirconium metal tubes, these hydrides precipitate with a circumferential orientation near the outer surface of the cladding tubes forming a hydride rim which can serve as a site for crack initiation during circumferential ‘hoop’ loading of the cladding [4rim formation][5]. In order to better understand the effect of hydride rims on the fracture behavior of stress relieved zirconium alloy cladding tubes a procedure to produce these rim-like structures is being developed at Idaho National Laboratory (INL).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Tuning the temperature range of superelastic Ni-Ti alloys for elastocaloric cooling via thermal processing

Abstract Caloric cooling enlisting solid-state refrigerants is potentially a promising eco-friendly alternative to conventional cooling based on vapor compression. The most common refrigerant materials for elastocaloric cooling to date are Ni-Ti based superelastic shape memory alloys. Here, we have explored tuning the operation temperature range of Ni 50.8 Ti 49.2 for elastocaloric cooling. In particular, we have studied the effect of thermal treatments (a.k.a. aging) on the transformation temperature, superelasticity, and elastocaloric effects of Ni 50.8 Ti 49.2 shape memory alloy tubes. The isothermal compressive test revealed that the residual strain of thermally-treated Ni-Ti tubes at room temperature approaches zero as aging time is increased. Short-time aging treatment at 400 °C resulted in good superelasticity and elastocaloric cooling performance with a large tunable austenite finish ( A f ) temperature range of 24.7 °C, as determined from the A f temperature of the samples that were aged 5–120 min. The main reason of the property change is the formation of a different amount of Ni 4 Ti 3 precipitates in the NiTi matrix. Our findings show that it is possible to tailor the A f temperature range for development of cascade elastocaloric cooling systems by thermally treating a starting single composition Ni-Ti alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance Investigation of High-temperature Latent Heat Storage for Integration with Advanced Nuclear Reactors

Integrating thermal energy storage (TES) with advanced nuclear reactors enhances the flexible use of nuclear energy, facilitating its expansion beyond conventional electricity generation. High-temperature TES, capable of storing heat above 400 degree C, emerges as a vital carbon-free energy solution for decarbonizing industrial sector, especially when combined with low-emission energy sources like advanced nuclear reactors. Idaho National Laboratory (INL) has recently developed a novel design for high-temperature latent heat storage system, called Heat pipe Integrated Thermal Battery (HITB), and efforts are underway to experimentally demonstrate the concept. HITB employs liquid-metal heat pipes to establish thermal linkage between TES and nuclear systems without direct fluid exchange, minimizing the potential risk of the integrated systems such as cross-contamination. HITB is designed to achieve high charging and discharging efficiency as well as high energy storage density by employing metal alloys as Phase Change Material (PCM). Initial proof-of-concept experiments are being undertaken using an aluminum alloy (Al59%-Mg35%-Zn6%). This paper discusses current progress of the HITB project at INL, seeking to develop high-temperature TES for versatile integration with advanced nuclear reactors, and shares insights from the performance evaluations via numerical modeling and analysis.

25 ENERGY STORAGE↗
DOE OSTI2022

One-Step Facile Synthesis of High-Activity Nitrogen-Doped PtNiN Oxygen Reduction Catalyst

PtM alloy electrocatalysts (M = Fe, Co, Ni) have been the subject of many investigations aimed at increasing their attractive properties, in particular oxygen reduction reaction (ORR) activity, while reducing total platinum-group-metal content and improving durability. Despite some success, these catalysts still have relatively high Pt content and lack the necessary durability as M metals leach out from the alloys during potential cycling. Previously, we synthesized nitrogen (N)-doped PtMN/C catalysts consisting of thin Pt shells on M nitride cores by a two-step method, which showed higher ORR activity and stability than their PtM counterparts. In the present work, we developed a facile one-step synthesis method, which comprises a single thermal annealing process of the N-doped PtNiN/C alloy. The ORR performance of the one-step synthesized PtNiN/C catalyst is much higher than that of the two-step synthesized PtNiN/C, as revealed by rotating disk electrode measurements. Membrane electrode assembly fuel cell testing demonstrated superb durability and high activity. Formation of Pt monolayer shells on the nitrided (Pt x Ni 1-x ) 4 N cores was confirmed by in situ X-ray absorption spectroscopy. The origins of the enhanced activity and stability of the one-step synthesized PtNiN/C catalyst are elucidated based on density functional theory calculations together with the experimental results.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Spectral quadrature for the first principles study of crystal defects: Application to magnesium

In this work, we present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk environment. Specifically, employing non-local pseudopotentials, local reformulation of electrostatics, and truncation of the spatial Kohn-Sham Hamiltonian, and the Linear Scaling Spectral Quadrature method to solve for the pointwise electronic fields in real-space and the non-local component of the atomic force, we develop a parallel finite difference framework suitable for distributed memory computing architectures to simulate non-periodic systems embedded in a bulk environment. Choosing examples from magnesium-aluminum alloys, we first demonstrate the convergence of energies and forces with respect to spectral quadrature polynomial order, and the width of the spatially truncated Hamiltonian. Next, we demonstrate the parallel scaling of our framework, and show that the computation time and memory scale linearly with respect to the number of atoms. Next, we use the developed framework to simulate isolated point defects and their interactions in magnesium-aluminum alloys. Our findings conclude that the binding energies of divacancies, Al solute-vacancy and two Al solute atoms are anisotropic and are dependent on cell size. Furthermore, the binding is favorable in all three cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Grain boundary transport through thermally grown alumina scales on NiAl

The oxidation resistance of Ni-based alloys is typically improved by additions of elements segregating to grain boundaries, contributing to reducing scale growth, and improving scale adherence. To develop a mechanistic understanding of doping, outward Al and inward O fluxes through the scale formed on β-NiAl with Ti, Y, and Ti+Y additions were characterized for oxidation at 1200°C and times up to 100h. Doped alloys exhibited varying reductions in oxidation rates and Al and O fluxes affecting scale grain morphologies. In conclusion, Ti+Y co-doping further reduced the outward Al and inward O transport compared to doping with Ti or Y alone.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Abstract Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt 3 Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

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

Emulation of neutron-irradiated microstructure of austenitic 21Cr32Ni model alloy using dual-ion irradiation

Here, in this study, the capability of heavy-ion irradiation to emulate neutron irradiation was demonstrated on an austenitic 21Cr32Ni type ternary model alloy. The model alloy used in this study is chemically analogous but compositionally simpler than of alloy 800H, which is a candidate austenitic Ni alloy which has been proposed for use in Generation IV reactors. The microstructure of the 21Cr32Ni model alloy irradiated in the BOR-60 fast reactor to 17.1 dpa and 35 dpa at ~380°C was characterized using transmission electron microscopy (TEM). The 17.1 dpa BOR-60 irradiated microstructure was then compared with the microstructure of the same material developed under dual-ion (DI) irradiation using various He/dpa ratios between 0.1 and 16.6 appm He/dpa in the temperature range of 430°C-500°C. The results showed that both neutron and DI irradiation of 21Cr32Ni model alloy produced dislocations in the form of a dislocation network as well as {111}-type faulted dislocation loops, cavities, and radiation-induced Ni enrichment at radiation-induced sinks. When the dose and the He/dpa ratio were kept similar to those in neutron irradiation, DI irradiation of the 21Cr32Ni model alloy at 460°C resulted in over-nucleation of small cavities and in a high density of faulted dislocation loops compared to those observed in the fast-neutron irradiated alloy of the same heat irradiated at ~380°C. The optimal condition for reproducing the neutron-irradiated microstructure was DI irradiation at 460°C and 0.1 appm He/dpa. In that case, the faulted loop and cavity size distributions in the BOR-60 irradiated 21Cr32Ni model alloy samples closely matched with those measured in the DI irradiated 21Cr32Ni model alloy sample. The fact that the He/dpa is an order of magnitude smaller than the helium generation rate for fast neutron irradiation, stops over nucleation and allows for the development of a similar microstructure as for neutron irradiation.

21Cr32Ni model alloy↗
DOE OSTI2023

Effect of local chemical order on the irradiation-induced defect evolution in CrCoNi medium-entropy alloy

High- (and medium-) entropy alloys have emerged as potentially suitable structural materials for nuclear applications, particularly as they appear to show promising irradiation resistance. Recent studies have provided evidence of the presence of local chemical order (LCO) as a salient feature of these complex concentrated solid-solution alloys. However, the influence of such LCO on their irradiation response has remained uncertain thus far. Here, in this work, we combine ion irradiation experiments with large-scale atomistic simulations to reveal that the presence of chemical short-range order, developed as an early stage of LCO, slows down the formation and evolution of point defects in the equiatomic medium-entropy alloy CrCoNi during irradiation. In particular, the irradiation-induced vacancies and interstitials exhibit a smaller difference in their mobility, arising from a stronger effect of LCO in localizing interstitial diffusion. This effect promotes their recombination as the LCO serves to tune the migration energy barriers of these point defects, thereby delaying the initiation of damage. These findings imply that local chemical ordering may provide a variable in the design space to enhance the resistance of multi-principal element alloys to irradiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Non-local corrections to the typical medium theory of Anderson localization

We use the recently developed finite cluster typical medium approach to study the Anderson localization transition in three dimensions. Applying our method to the box and binary alloy disorder distributions, we find a fast convergence with the cluster size. We demonstrate the importance of the typical medium environment and the non-local spatial correlations for the proper characterization of the localization transition. As the cluster size increases, our typical medium cluster method recovers the correct critical disorder strength for the transition. Our findings highlight the importance of the non-local cluster corrections for capturing the localization behavior of the mobility edge trajectories. Our results demonstrate that the typical medium cluster approach developed here provides a consistent and systematic description of the Anderson localization transition in the framework of the effective medium embedding schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

High temperature inelastic constitutive models for the ASME Section III, Division 5 Class A materials

This report describes the exploration of a universal high temperature inelastic constitutive model for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5, Class A design rules. The idea, developed based on feedback from reactor vendors, is to simplify the current bespoke material models for Grade 91, 316H, and Alloy 617 and the new model for Alloy 800H into a simple, single model form. The report describes a new parallel time integration technique implemented in the pyoptmat package which allowed us to explore a wide variety of model forms, searching for a suitable common model. The report then describes preliminary work on models for monontonic deformation and then the development of a set of models suitable for capturing high temperature cyclic deformation. These models are suitable for use with the ASME design rules, except potentially for a narrow, material-specific band of temperatures at the edge of the creep regime. An incremental improvement the current models could overcome this discrepancy and provide a new, simpler set of constitutive model for all four materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ORNL_AISD_NiNb

This dataset describes the nickel-niobium solid solution binary alloy, where the two constituent elements nickel (Ni) and niobium (Nb) are randomly placed on an underlying crystal lattice. This dataset for nickel-niobium (Ni-Nb) alloys available includes the formation energy and bulk modulus for each crystal structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). The geometry optimization ensures that all the alloy samples reached the equilibrium with negative formation energy. We perform geometry optimizations using the LAMMPS simulation package [1], a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. We utilized the embedded atom model (EAM) potential for Ni and Nb developed in a previous study [2]. The potential could describe behaviors of the liquid and solid phases of Ni-Nb alloy. The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Ã… for FCC, 3.32 Ã… for BCC, and 3.5 Ã… for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. The dataset consists of three sets of crystal structures. The first set contains 46,086 irregular crystal structures, each of them with 54 atoms, obtained through optimization starting from a regular BCC crystal structure. The second set contains 24,543 irregular crystal structures, each of them with 32 atoms, obtained through optimization starting from a regular FCC crystal structure. The third set contains 39,303 irregular crystal structures, each of them with 48 atoms, obtained through optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been unified in a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. Organization of files inside the dataset: the dataset contains three subdirectories called • BCC_opt • FCC_opt • HCP_opt based on the type of initial regular structure used to start the geometry optimization. Inside each of these folders, every atomic structure is identified by a string “A_B_Câ€, where A denotes the number of Nb in the system, B denotes index of structure with a given Nb number, and C denotes the total number of structures generated with a given Nb number. For each optimized crystal structure identified by the unique string of characters “A_B_Câ€, three files are provided: • A_B_C_opt.xyz: The optimized geometries in xyz format • A_B_C_opt.cfg: The optimized geometries in cfg format. It includes cell information and atomic energy, and forces calculated from LAMMPS. • A_B_C.elastic: Raw data of 21 elastic constants from LAMMPS output. • A_B_C.bulk: Calculated upper and lower bounds of bulk modulus and averaged one based on Voigt-Reuss-Hill approach from *.elastic. References: [1] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 271:108171, 2022. [2] Y Zhang, R Ashcraft, MI Mendelev, CZ Wang, and KF Kelton. Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy. The Journal of chemical physics, 145(20):204505, 2016.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of physically transparent, predictive structure-performance relationships for rational design of multi-component catalytic materials. Final Report

Solid surfaces act as catalysts for many important chemical processes, including commodity chemicals production, energy conversion and pollution mitigation. The outcome of catalytic processes is mainly governed by interactions of adsorbates with active sites on the catalyst surface. These interactions can be dramatically affected by the changes in the local characteristics of the active site, which can be accomplished by an introduction of various promoters and poisons, or by alloying with other metal atoms. In fact, many commercial heterogeneous catalysts contain metal nanoparticles promoted by various additives. Our goal is to develop fundamental understanding about the impact of perturbations in the local geometry of a catalytic site on the local surface reactivity. These insights are crucial for a more systematic and rational catalyst design. The project aims to advance the field of catalysis by formulating reliable structure/performance relationships and utilizing these relationships to identify novel catalytic materials. Benefits for DOE and our society are in the development of fundamental theories that guide the process of discovery of energy-efficient and environmentally friendly heterogeneous catalysis.

36 MATERIALS SCIENCE↗