Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userKinetics of formation of twinned structures under L1_0 type orderings in alloys
50
0
0.0
(
0
)
Added by
Kirill Belashchenko
Publication date
2001
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The earlier-developed master equation approach and kinetic cluster methods are applied to study kinetics of L1_0 type orderings in alloys, including the formation of twinned structures characteristic of cubic-tetragonal-type phase transitions. A microscopical model of interatomic deformational interactions is suggested which generalizes a similar model of Khachaturyan for dilute alloys to the physically interesting case of concentrated alloys. The model is used to simulate A1->L1_0 transformations after a quench of an alloy from the disordered A1 phase to the single-phase L1_0 state for a number of alloy models with different chemical interactions, temperatures, concentrations, and tetragonal distortions. We find a number of peculiar features in both transient microstructures and transformation kinetics, many of them agreeng well with experimental data. The simulations also demonstrate a phenomenon of an interaction-dependent alignment of antiphase boundaries in nearly-equilibrium twinned bands which seems to be observed in some experiments.
rate research
Read More
We review a series of works where the fundamental master equation is used to develop a microscopical description of evolution of non-equilibrium atomic distributions in alloys. We describe exact equations for temporal evolution of local concentrations and their correlators as well as approximate methods to treat these equations, such as the kinetic mean-field and the kinetic cluster methods. We also describe an application of these methods to studies of kinetics of L1_0 type orderings in FCC alloys which reveal a number of peculiar microstructural effects, many of them agreeing well with experimental observations.
Spin kinetics in $n$-type InAs quantum wells under intense terahertz laser fields is investigated by developing fully microscopic kinetic spin Bloch equations via the Floquet-Markov theory and the nonequilibrium Greens function approach, with all the relevant scattering, such as the electron-impurity, electron-phonon, and electron-electron Coulomb scattering explicitly included. We find that a {em finite} steady-state terahertz spin polarization induced by the terahertz laser field, first predicted by Cheng and Wu [Appl. Phys. Lett. {bf 86}, 032107 (2005)] in the absence of dissipation, exists even in the presence of all the scattering. We further discuss the effects of the terahertz laser fields on the spin relaxation and the steady-state spin polarization. It is found that the terahertz laser fields can {em strongly} affect the spin relaxation via hot-electron effect and the terahertz-field-induced effective magnetic field in the presence of spin-orbit coupling. The two effects compete with each other, giving rise to {em non-monotonic} dependence of the spin relaxation time as well as the amplitude of the steady state spin polarization on the terahertz field strength and frequency. The terahertz field dependences of these quantities are investigated for various impurity densities, lattice temperatures, and strengths of the spin-orbit coupling. Finally, the importance of the electron-electron Coulomb scattering on spin kinetics is also addressed.
Neutron irradiation tends to promote disorder in ordered alloys through the action of the thermal spikes that it generates, while simultaneously introducing point defects and defect clusters. As they migrate, these point defects will promote reordering of the alloys, acting against irradiation-induced disordering. In this study, classical molecular dynamics and a highly parallel accelerated sampling method are used to study the reordering kinetics of Ni$_3$Al under the diffusion of self-interstitial atoms (SIA). By monitoring the order parameter and potential energy from atomistic simulations, we show that the SIA acts as a reordering agent in Ni$_3$Al. A mean-field rate theory model of the interstitialcy-based reordering kinetics is introduced, which reproduces simulation data and predicts reordering at temperatures as low as 500 K.
Neutron irradiation induces in steels nanostructural changes, which are at the origin of the mechanical degradation that these materials experience during operation in nuclear power plants. Some of these effects can be studied by using as model alloy the iron-carbon system. The Object Kinetic Monte Carlo technique has proven capable of simulating in a realistic and quantitatively reliable way a whole irradiation process. We have developed a model for simulating Fe-C systems using a physical description of the properties of vacancy and self-interstitial atom (SIA) clusters, based on a selection of the latest data from atomistic studies and other available experimental and theoretical work from the literature. Based on these data, the effect of carbon on radiation defect evolution has been largely understood in terms of formation of immobile complexes with vacancies that in turn act as traps for SIA clusters. It is found that this effect can be introduced using generic traps for SIA and vacancy clusters, with a binding energy that depends on the size of the clusters, also chosen on the basis on previously performed atomistic studies. The model proved suitable to reproduce the results of low (<350 K) temperature neutron irradiation experiments, as well as the corresponding post-irradiation annealing up to 700 K, in terms of defect cluster densities and size distribution, when compared to available experimental data from the literature. The use of traps proved instrumental for our model.
Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic-oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over a wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
