Monte Carlo algorithms for calculation of diffusive characteristics of an electron avalanche in gases

2016 ◽  
Vol 31 (6) ◽  
Author(s):  
Galiya Z. Lotova
Keyword(s):  
Monte Carlo ◽  
Diffusion Coefficients ◽  
Distribution Density ◽  
High Efficiency ◽  
Time Moment ◽  
Three Dimensional ◽  
Electron Avalanche ◽  
Monte Carlo Algorithms ◽  
And Diffusion ◽  
Projection Estimators

AbstractSome problems of the theory of electron transfer in gases under the action of a strong external electric field is considered in the paper. Based on the three-dimensional ELSHOW algorithm, samples of states of particles in an electron avalanche are obtained for a given time moment in order to calculate the corresponding ‘diffusion radii’ and diffusion coefficients. Randomized projection estimators and kernel estimators (for test purpose) are constructed with the use of grouped samples for evaluation of the distribution density of particles in an avalanche. Test computations demonstrate a high efficiency of projection estimators for calculation of diffusive characteristics.

Fo and Ni Relations in Olivine Differentiate between Crystallization and Diffusion Trends

2020 ◽  
Author(s):  
Boris Gordeychik ◽  
Tatiana Churikova ◽  
Thomas Shea ◽  
Andreas Kronz ◽  
Alexander Simakin ◽  
...  
Keyword(s):  
Diffusion Coefficients ◽  
Computational Models ◽  
Three Dimensional ◽  
Crystal Shape ◽  
Diffusion Anisotropy ◽  
Progressive Loss ◽  
Theoretical And Computational Models ◽  
Diffusion Controlled ◽  
Mafic Magmas ◽  
And Diffusion

Abstract Nickel is a strongly compatible element in olivine, and thus fractional crystallization of olivine typically results in a concave-up trend on a Fo–Ni diagram. ‘Ni-enriched’ olivine compositions are considered those that fall above such a crystallization trend. To explain Ni-enriched olivine crystals, we develop a set of theoretical and computational models to describe how primitive olivine phenocrysts from a parent (high-Mg, high-Ni) basalt re-equilibrate with an evolved (low-Mg, low-Ni) melt through diffusion. These models describe the progressive loss of Fo and Ni in olivine cores during protracted diffusion for various crystal shapes and different relative diffusivities for Ni and Fe–Mg. In the case when the diffusivity of Ni is lower than that for Fe–Mg interdiffusion, then olivine phenocrysts affected by protracted diffusion form a concave-down trend that contrasts with the concave-up crystallization trend. Models for different simple geometries show that the concavity of the diffusion trend does not depend on the size of the crystals and only weakly depends on their shape. We also find that the effect of diffusion anisotropy on trend concavity is of the same magnitude as the effect of crystal shape. Thus, both diffusion anisotropy and crystal shape do not significantly change the concave-down diffusion trend. Three-dimensional numerical diffusion models using a range of more complex, realistic olivine morphologies with anisotropy corroborate this conclusion. Thus, the curvature of the concave-down diffusion trend is mainly determined by the ratio of Ni and Fe–Mg diffusion coefficients. The initial and final points of the diffusion trend are in turn determined by the compositional contrast between mafic and more evolved melts that have mixed to cause disequilibrium between olivine cores and surrounding melt. We present several examples of measurements on olivine from arc basalts from Kamchatka, and published olivine datasets from mafic magmas from non-subduction settings (lamproites and kimberlites) that are consistent with diffusion-controlled Fo–Ni behaviour. In each case the ratio of Ni and Fe–Mg diffusion coefficients is indicated to be <1. These examples show that crystallization and diffusion can be distinguished by concave-up and concave-down trends in Fo–Ni diagrams.

scholarly journals Bayesian Random Tomography of Particle Systems

2021 ◽  
Vol 8 ◽  
Author(s):  
Nima Vakili ◽  
Michael Habeck
Keyword(s):  
Monte Carlo ◽  
Three Dimensional ◽  
Sampling Strategy ◽  
Particle Systems ◽  
High Dimensionality ◽  
Monte Carlo Algorithms ◽  
Posterior Sampling ◽  
Imaging Science ◽  
Speed Up ◽  
Projection Images

Random tomography is a common problem in imaging science and refers to the task of reconstructing a three-dimensional volume from two-dimensional projection images acquired in unknown random directions. We present a Bayesian approach to random tomography. At the center of our approach is a meshless representation of the unknown volume as a mixture of spherical Gaussians. Each Gaussian can be interpreted as a particle such that the unknown volume is represented by a particle cloud. The particle representation allows us to speed up the computation of projection images and to represent a large variety of structures accurately and efficiently. We develop Markov chain Monte Carlo algorithms to infer the particle positions as well as the unknown orientations. Posterior sampling is challenging due to the high dimensionality and multimodality of the posterior distribution. We tackle these challenges by using Hamiltonian Monte Carlo and a global rotational sampling strategy. We test the approach on various simulated and real datasets.

Monte Carlo Simulation of the Thermal Conductivity and Phonon Transport in Nanocomposites

2005 ◽  
Author(s):  
Ming-Shan Jeng ◽  
Ronggui Yang ◽  
David Song ◽  
Gang Chen
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Silicon Germanium ◽  
High Efficiency ◽  
Three Dimensional ◽  
Simulation Method ◽  
Phonon Transport ◽  
Boltzmann Transport ◽  
Nanoparticle Composites

This paper presents a Monte Carlo simulation scheme to study the phonon transport and thermal conductivity of nanocomposites. Special attention has been paid to the implementation of periodic boundary condition in Monte Carlo simulation. The scheme is applied to study the thermal conductivity of silicon germanium (Si-Ge) nanocomposites, which are of great interest for high efficiency thermoelectric material development. The Monte Carlo simulation was first validated by successfully reproducing the results of (two dimensional) nanowire composites using the deterministic solution of the phonon Boltzmann transport equation and the experimental thermal conductivity of bulk germanium, and then the validated simulation method was used to study (three dimensional) nanoparticle composites, where Si nanoparticles are embedded in Ge host. The size effects of phonon transport in nanoparticle composites were studied and the results show that the thermal conductivity of nanoparticle composites can be lower than alloy value. It was found that randomly distributed nanopaticles in nanocomposites rendered the thermal conductivity values close to that of periodic aligned patterns.

Radiolysis of supercritical water at 400 °C and liquid-like densities near 0.5 g/cm3 — A Monte Carlo calculation

2010 ◽  
Vol 88 (7) ◽  
pp. 646-653 ◽  
Author(s):  
Jintana Meesungnoen ◽  
David Guzonas ◽  
Jean-Paul Jay-Gerin
Keyword(s):  
Monte Carlo ◽  
Supercritical Water ◽  
Diffusion Coefficients ◽  
Monte Carlo Calculation ◽  
Picosecond Pulse ◽  
Good Accord ◽  
Radiation Induced ◽  
Sum Formula ◽  
Available Information ◽  
And Diffusion

Monte Carlo simulations are used to calculate the primary radical yields [Formula: see text], g(•OH), the sum [[Formula: see text] + g(•OH) + g(H•)], and the ratio g(H•)/[Formula: see text] in the low linear energy transfer (LET) radiolysis of supercritical water (SCW) at 400 °C in the high-density, liquid-like region near ∼0.5 g/cm3. Using all the currently available information on the reactivities and diffusion coefficients of the radiation-induced species under these conditions, and assuming the aqueous medium to be a “continuum”, a good accord is found between our calculations and the available experimental data. In particular, our computed [Formula: see text] yields at 60 ps and 1 ns compare very well with recently reported direct time-dependent [Formula: see text] yield measurements in SCW (D2O) at 400 °C and 0.570 g/cm3 using picosecond pulse radiolysis experiments.

scholarly journals 3D trajectories and diffusion of single ceria particles near a glass surface and their removal

2021 ◽  
Author(s):  
Jihoon Seo ◽  
Akshay Gowda ◽  
Panart Khajornrungruang ◽  
Satomi Hamada ◽  
S.V. Babu
Keyword(s):  
Diffusion Coefficient ◽  
Diffusion Coefficients ◽  
Glass Surface ◽  
Evanescent Wave ◽  
Three Dimensional ◽  
Separation Distance ◽  
Dimensional Diffusion ◽  
Mean Squared Displacement ◽  
Glass Surfaces ◽  
And Diffusion

AbstractWe extend our recent 2D trajectory (x–y plane) and diffusion coefficient data of ceria particles near a glass surface obtained at pH 3, 5, and 7 using evanescent wave microscopy and video imaging to 3D trajectories by analyzing the separation distance between the particles and the glass surface in the vertical z‐direction. Mean squared displacement (MSD3D) of ceria particles was calculated to quantify 3D trajectories. Three‐dimensional diffusion coefficients were obtained from the MSD3D curves and were compared with two‐dimensional diffusion coefficients. By analyzing the MSD curves, we found that ceria particles exhibited only confined motion at pH 3 and 5, while both confined and Brownian motion were showed at pH 7. We also evaluated the cleaning ability of DI water adjusted to pH 10 and 12 to remove ceria particles from glass surfaces and related the results to the calculated trajectory, diffusion coefficient, and interaction potential data.

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