Hydrodynamic model of correlation of metal removal rates from repetitive drop impact
Steam turbine blade metal removal rates by repetitive water drop impact, using hydrodynamic model
Pouchot, W. D.↗
SEARCH · Engineering Papers
Results for “Modeling of Metal AM”
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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A differential CDM model for fatigue of unidirectional metal matrix composites
A multiaxial, isothermal, continuum damage mechanics (CDM) model for fatigue of a unidirectional metal matrix composite volume element is presented. The model is phenomenological, stress based, and assumes a single scalar internal damage variable, the evolution of which is anisotropic. The development of the fatigue damage model, (i.e., evolutionary law) is based on the definition of an initially transversely isotropic fatigue limit surface, a static fracture surface, and a normalized stress amplitude function. The anisotropy of these surfaces and function, and therefore the model, is defined through physically meaningful invariants reflecting the local stress and material orientation. This transversely isotropic model is shown, when taken to it's isotropic limit, to directly simplify to a previously developed and validated isotropic fatigue continuum damage model. Results of a nondimensional parametric study illustrate (1) the flexibility of the present formulation in attempting to characterize a class of composite materials, and (2) the capability of the formulation in predicting anticipated qualitative trends in the fatigue behavior of unidirectional metal matrix composites. Also, specific material parameters representing an initial characterization of the composite system SiC/Ti 15-3 and the matrix material (Ti 15-3) are reported.
Arnold, S. M.↗
The coverage-dependent effect on the barrier for oxygen penetration into a metal lattice
Model calculations for O/Cu(111) show that the rigid lattice penetration barrier is reduced from 8.2 to 5.3 eV as the O coverage is increased from 1/4 to 1/2 monolayer; it is expected that this large reduction will also apply to the relaxed lattice case. An increase of the distance between the first and second Cu layers, without changing the intralayer spacing, does not affect the barrier.
Bauschlicher, C. W., Jr.↗
Catalytic Activation of Polyethylene Model Compounds Over Metal-Exchanged Beta Zeolites
Decomposition of polymers by heterogeneous catalysts presents a promising approach for reuse of waste plastics. Here, we demonstrated non-hydrogenative decomposition of model polyolefins over proton-form and metal (Cu, Ni) ion-exchanged beta zeolites at moderate temperatures (ca. 300 degrees C). Near complete polyolefin decomposition was observed in batch reactions monitored by thermogravimetric analysis, while decomposition at partial conversion was studied in flow reactions. Ni-exchanged zeolites produced H 2 at substantially higher rates (> 10x) than other catalysts while also uniquely resisting deactivation over time. Application of the delplot formalism offered insights into the reaction network for polyolefin decomposition over Ni/BEA, most notably that H 2 is solely a primary product. We deduce that H 2 production is catalyzed by activation of C-H bonds at ionic Ni sites, and H 2 prevents buildup of polyaromatic coke species in Ni-exchanged zeolites that deactivate Cu-exchanged and protonic zeolites.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling
This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.
36 MATERIALS SCIENCE↗
Mechanistic insights into metal, nitrogen doped carbon catalysts for oxygen reduction: progress in computational modeling
We report metal and nitrogen doped carbon materials (denoted as M–N–C) synthesized through high-temperature pyrolysis have been found to exhibit activity for oxygen reduction reaction (ORR) approaching that of Pt and electrochemical stability higher than previous MN 4 -containing macrocyclic molecular catalysts. Tremendous efforts have thus been devoted to the advancement of M–N–C catalysts as an economical alternative to Pt-based catalysts for proton exchange membrane fuel cell cathodes with a focus on simultaneously improving activity and stability. To this end, novel computational modeling techniques have been developed and applied to acquire knowledge crucial for accelerating the pace of M–N–C catalyst development. In this review, recent progress in computational method development, as well as the predictions of chemical structure of active sites, reaction pathways, ORR kinetics, and catalyst stability in electrochemical environments, are critically surveyed. Moreover, the crucial role of computational modeling to elucidate the functional mechanism of M–N–C catalysts for ORR in acid media and enable rational design of M–N–C catalysts is discussed with a visionary outlook for the field.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Atmospheres of metal-deficient stars - Main sequence stars.
Atmospheric models for metal deficient stars of K and G spectral type, studying reduced metal abundance effect on atmospheric structure
Krishna Swamy, K. S.↗
A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics
We study the utility and performance of a Bayesian model error embedding construction in the context of molecular dynamics modeling of metallic alloys, where we embed model error terms in existing interatomic potential model parameters. To alleviate the computational burden of this approach, we propose a framework combining likelihood approximation and Gaussian process surrogates. Here we leverage sparse Gaussian process techniques to construct a hierarchy of increasingly accurate but more expensive surrogate models. This hierarchy is then exploited by multilevel Markov chain Monte Carlo methods to efficiently sample from the target posterior distribution. We illustrate the utility of this approach by calibrating an interatomic potential model for a family of gold-copper alloys. In particular, this case study highlights effective means for dealing with computational challenges with Bayesian model error embedding in large-scale physical models, and the utility of embedded model error for model diagnostics.
Bayesian inference↗
Inhomogeneous chemical evolution of the Galactic disk
We present analytical models for inhomogeneous chemical evolution (ICE) of systems in which the star formation history resembles a series of bursts, localized in space and/or time, with intermittent periods of remixing. The additional parameter of this model is the metallicity increment of bursting subsystems, but this parameter is constrained by the spread in the age-metallicity relation. We apply this model to the solar annulus in the Galactic disk and show that ICE models yield an improved fit to the observed shape of the stellar abundance distribution function (ADF). The G-dwarf problem can be alleviated with ICE models, but infall of metal poor gas and/or some preenrichment of the disk during the epoch of protogalactic evolution is still required to explain the paucity of low-metallicity dwarfs. ICE models also suggest an explanation of the reduced frequency of metal-rich G-dwarfs relative to the predictions of the simple model. It does not seem likely that chemical evolution of the solar annulus proceeded in a medium that was well-mixed at all times.
Malinie, Guy↗
Atomistic modeling of a superconductor–transition metal dichalcogenide–superconductor Josephson junction
Using an atomistic tight-binding model, we investigate the characteristics of a Josephson junction formed by monolayers of MoS 2 sandwiched between Pb superconducting electrodes. We derive and apply Green's function–based formulation to compute the Josephson current as well as the local density of states in the junction. Our analysis of diagonal and off-diagonal components of the local density of states reveals the presence of triplet superconducting correlations in the MoS 2 monolayers and spin-polarized subgap (Andreev bound) states. Our formulation can be extended to other systems where atomistic details and large scales are needed to obtain accurate modeling of Josephson junction physics.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
High power kinetics of a two dimensional circulating liquid metal fuel reactor
Mathematical model of liquid metal fuel reactor to investigate temperature distribution on neutron kinetics
Kolar, M. J.↗
Thermo-mechanical modeling and experimental validation for multilayered metallic microstructures
This paper presents thermo-mechanical modeling on multilayered metallic microstructures consisting of two bilayers that enable 3D deployment once being Joule heated. Additionally, a representative design of such microstructures, as well as their working principle is introduced. Furthermore, a mathematical model is derived using Euler–Bernoulli beam theory, and analytical solutions describing out-of-plane displacement of the multilayered microstructures when uniformly heated, which is validated by finite element analysis. Parametric analysis on thermal load and dimensional change of beam shows a good agreement between the analytical solutions and results given by finite element analysis. The phases of the near-equiatomic NiTi layer are analyzed by EDS and XRD, which prove to be austenite during the Joule heating process. The mechanical properties of austenite NiTi are incorporated into the analytical solutions that provides a good estimation of 3D deployment of microstructures made of NiTi and aluminum. The experimental results provide an approximately 7 µm out-of-plane deployment by applying a uniform temperature increase of 132 K, and parametric analysis on the dimension of top aluminum beam offers another promising approach to larger 3D deployment. The proposed mathematical model provides an efficient tool for predicting the out-of-plane deployment of such multilayered microstructures.
36 MATERIALS SCIENCE↗
Electrochemical CO2 Reduction over Metal-/Nitrogen-Doped Graphene Single-Atom Catalysts Modeled Using the Grand-Canonical Density Functional Theory
Renewably driven, electrochemical conversion of carbon dioxide into value-added products is expected to be a critical tool in global decarbonization. However, theoretical studies based on the computational hydrogen electrode largely ignore the nonlinear effects of the applied potential on the calculated results, leading to inaccurate predictions of catalytic behavior or mechanistic pathways. Here, we use grand canonical density functional theory (GC-DFT) to model electrochemical CO2 reduction (CO2R) over metal- and nitrogen-doped graphene catalysts (MNCs) and explicitly include the effects of the applied potential. We used GC-DFT to compute the CO2 to CO reaction intermediate energies at -0.3, -0.7, and -1.2 VSHE catalyzed by MNCs each doped with 1 of the 10 3d block metals coordinated by four pyridinic nitrogen atoms. Our results predict that Sc-, Ti-, Co-, Cu-, and Zn-N4Cs effectively catalyze CO2R at moderate to large reducing potentials (-0.7 to -1.2 VSHE). ZnN4C is a particularly promising electrocatalyst for CO2R to CO both at low and moderate applied potentials based on our thermodynamic analysis. Our findings also explain the observed pH independence of CO production over FeN4C and predict that the rate-determining step of CO2R over FeN4C is not *CO2- formation but rather *CO desorption. Additionally, the GC-DFT-computed density of states analysis illustrates how the electronic states of MNCs and adsorbates change non-uniformly with applied potential, resulting in a significantly increased *CO2- stability relative to other intermediates and demonstrating that the formation of the adsorbed *CO2- anion is critical to CO2R activation. This work demonstrates how GC-DFT paves the way for physically realistic and accurate theoretical simulations of reacting electrochemical systems.
CO2 reduction↗
Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning
The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.
36 MATERIALS SCIENCE↗
Micromechanics Modeling of Fracture in Nanocrystalline Metals
Nanocrystalline metals have very high theoretical strength, but suffer from a lack of ductility and toughness. Therefore, it is critical to understand the mechanisms of deformation and fracture of these materials before their full potential can be achieved. Because classical fracture mechanics is based on the comparison of computed fracture parameters, such as stress intlmsity factors, to their empirically determined critical values, it does not adequately describe the fundamental physics of fracture required to predict the behavior of nanocrystalline metals. Thus, micromechanics-based techniques must be considered to quanti@ the physical processes of deformation and fracture within nanocrystalline metals. This paper discusses hndamental physicsbased modeling strategies that may be useful for the prediction Iof deformation, crack formation and crack growth within nanocrystalline metals.
Glaessgen, E. H.↗
Coupling of Carbon Nanotubes to Metallic Contacts
The modeling of carbon nanotube-metal contacts is important from both basic and applied view points. For many applications, it is important to design contacts such that the transmission is dictated by intrinsic properties of the nanotube rather than by details of the contact. In this paper, we calculate the electron transmission probability from a nanotube to a free electron metal, which is side-contacted. If the metal-nanotube interface is sufficiently ordered, we find that k-vector conservation plays an important role in determining the coupling, with the physics depending on the area of contact, tube diameter, and chirality. The main results of this paper are: (1) conductance scales with contact length, a phenomena that has been observed in experiments and (2) in the case of uniform coupling between metal and nanotube, the threshold value of the metal Fermi wave vector (below which coupling is insignificant) depends on chirality. Disorder and small phase coherence length relax the need for k-vector conservation, thereby making the coupling stronger.
Anantram, M. P.↗
The Chocolate Chip Cookie Model: Dust-to-metal Ratio of H ii Regions
Using a sample of face-on star-forming galaxies selected from the Sloan Digital Sky Survey, we statistically derive the typical optical depth $\tau$ cl of individual H II regions based on the "Chocolate Chip Cookie" model of Lu et al. (2022). By binning galaxies into stellar mass and gas-phase metallicity bins and interpreting $\tau$ cl as the dust-to-gas ratio (DGR) of H II regions, we further investigate the correlations between DGR and stellar mass and between DGR and gas-phase metallicity, respectively. We find that DGR increases monotonically with the stellar mass of galaxies. At a given stellar mass, DGR shows a linear correlation with the gas-phase metallicity, which implies a constant dust-to-metal ratio (DTM) of galaxies at a given stellar mass. These results adequately indicate that the DTM of galaxies is simply a function of their stellar masses. In terms of gas-phase metallicity, because of the mass–metallicity relation, DTM increases with increasing metallicity with a power-law index 1.45 in the low-metallicity region, while it remains constant at the high-metallicity end.
79 ASTRONOMY AND ASTROPHYSICS↗
Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases
Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.
36 MATERIALS SCIENCE↗