360 research outputs found
Irreversible thermodynamics of creep in crystalline solids
We develop an irreversible thermodynamics framework for the description of
creep deformation in crystalline solids by mechanisms that involve vacancy
diffusion and lattice site generation and annihilation. The material undergoing
the creep deformation is treated as a non-hydrostatically stressed
multi-component solid medium with non-conserved lattice sites and
inhomogeneities handled by employing gradient thermodynamics. Phase fields
describe microstructure evolution which gives rise to redistribution of vacancy
sinks and sources in the material during the creep process. We derive a general
expression for the entropy production rate and use it to identify of the
relevant fluxes and driving forces and to formulate phenomenological relations
among them taking into account symmetry properties of the material. As a simple
application, we analyze a one-dimensional model of a bicrystal in which the
grain boundary acts as a sink and source of vacancies. The kinetic equations of
the model describe a creep deformation process accompanied by grain boundary
migration and relative rigid translations of the grains. They also demonstrate
the effect of grain boundary migration induced by a vacancy concentration
gradient across the boundary
The NBS: Processing/Microstructure/Property Relationships in 2024 Aluminum Alloy Plates
As received plates of 2024 aluminum alloy were examined. Topics covered include: solidification segregation studies; microsegregation and macrosegregation in laboratory and commercially cast ingots; C-curves and nondestructive evaluation; time-temperature precipitation diagrams and the relationships between mechanical properties and NDE measurements; transmission electron microscopy studies; the relationship between microstructure and properties; ultrasonic characterization; eddy-current conductivity characterization; the study of aging process by means of dynamic eddy current measurements; and Heat flow-property predictions, property degradations due to improve quench from the solution heat treatment temperature
Phase-field modeling of microstructural pattern formation during directional solidification of peritectic alloys without morphological instability
During the directional solidification of peritectic alloys, two stable solid
phases (parent and peritectic) grow competitively into a metastable liquid
phase of larger impurity content than either solid phase. When the parent or
both solid phases are morphologically unstable, i.e., for a small temperature
gradient/growth rate ratio (), one solid phase usually outgrows and
covers the other phase, leading to a cellular-dendritic array structure closely
analogous to the one formed during monophase solidification of a dilute binary
alloy. In contrast, when is large enough for both phases to be
morphologically stable, the formation of the microstructurebecomes controlled
by a subtle interplay between the nucleation and growth of the two solid
phases. The structures that have been observed in this regime (in small samples
where convection effect are suppressed) include alternate layers (bands) of the
parent and peritectic phases perpendicular to the growth direction, which are
formed by alternate nucleation and lateral spreading of one phase onto the
other as proposed in a recent model [R. Trivedi, Metall. Mater. Trans. A 26, 1
(1995)], as well as partially filled bands (islands), where the peritectic
phase does not fully cover the parent phase which grows continuously. We
develop a phase-field model of peritectic solidification that incorporates
nucleation processes in order to explore the formation of these structures.
Simulations of this model shed light on the morphology transition from islands
to bands, the dynamics of spreading of the peritectic phase on the parent phase
following nucleation, which turns out to be characterized by a remarkably
constant acceleration, and the types of growth morphology that one might expect
to observe in large samples under purely diffusive growth conditions.Comment: Final version, minor revisions, 16 pages, 14 EPS figures, RevTe
Phase field modeling of electrochemistry I: Equilibrium
A diffuse interface (phase field) model for an electrochemical system is
developed. We describe the minimal set of components needed to model an
electrochemical interface and present a variational derivation of the governing
equations. With a simple set of assumptions: mass and volume constraints,
Poisson's equation, ideal solution thermodynamics in the bulk, and a simple
description of the competing energies in the interface, the model captures the
charge separation associated with the equilibrium double layer at the
electrochemical interface. The decay of the electrostatic potential in the
electrolyte agrees with the classical Gouy-Chapman and Debye-H\"uckel theories.
We calculate the surface energy, surface charge, and differential capacitance
as functions of potential and find qualitative agreement between the model and
existing theories and experiments. In particular, the differential capacitance
curves exhibit complex shapes with multiple extrema, as exhibited in many
electrochemical systems.Comment: v3: To be published in Phys. Rev. E v2: Added link to
cond-mat/0308179 in References 13 pages, 6 figures in 15 files, REVTeX 4,
SIUnits.sty. Precedes cond-mat/030817
Phase field modeling of electrochemistry II: Kinetics
The kinetic behavior of a phase field model of electrochemistry is explored
for advancing (electrodeposition) and receding (electrodissolution) conditions
in one dimension. We described the equilibrium behavior of this model in [J. E.
Guyer, W. J. Boettinger, J.A. Warren, and G. B. McFadden, ``Phase field
modeling of electrochemistry I: Equilibrium'', cond-mat/0308173]. We examine
the relationship between the parameters of the phase field method and the more
typical parameters of electrochemistry. We demonstrate ohmic conduction in the
electrode and ionic conduction in the electrolyte. We find that, despite making
simple, linear dynamic postulates, we obtain the nonlinear relationship between
current and overpotential predicted by the classical ``Butler-Volmer'' equation
and observed in electrochemical experiments. The charge distribution in the
interfacial double layer changes with the passage of current and, at
sufficiently high currents, we find that the diffusion limited deposition of a
more noble cation leads to alloy deposition with less noble species.Comment: v3: To be published in Phys. Rev. E v2: Attempt to work around
turnpage bug. Replaced color Fig. 4a with grayscale 13 pages, 7 figures in 10
files, REVTeX 4, SIunits.sty, follows cond-mat/030817
Moving the pulsed heating technique beyond monolithic specimens: Experiments with coated wires
Pulsed heating experiments that measure high-temperature thermophysical properties using pyrometric measurement of the temperature-time history of metal specimens rapidly heated by passage of electric current have a 30-year history at the National Institute of Standards and Technology. In recent years, efforts have been made to move beyond the limitations of the standard technique of using costly, black-body geometry specimens. Specifically, simultaneous polarimetry measurement of the spectral emissivity has permitted study of sheet and wire specimens. This paper presents the results of two efforts to expand beyond the macroscopically monolithic, single-phase materials of all previous studies. In the first study the melting temperatures of coatings, including Ti and Ti(Al) alloys, deposited on higher melting Mo substrates are measured. In the second study the melting temperatures of substrates, Ti and Cr, covered by higher melting W and Mo coatings are measure
Machine learning for predicting soil classes in three semi-arid landscapes
Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes.
Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination.
Overall, complex models were consistently more accurate than simple or moderately complex models.Random forests (RF) using covariates selected via recursive feature elimination was consistently most accurate, or was among the most accurate, classifiers sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used.
Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area
Thermodynamic limits of crystallization and the prediction of glass formation tendency
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