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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

THE MATRYOSHKA RUN. II. TIME-DEPENDENT TURBULENCE STATISTICS, STOCHASTIC PARTICLE ACCELERATION, AND MICROPHYSICS IMPACT IN A MASSIVE GALAXY CLUSTER

We use the Matryoshka run to study the time-dependent statistics of structure-formation-driven turbulence in the intracluster medium of a 10{sup 15} M {sub ☉} galaxy cluster. We investigate the turbulent cascade in the inner megaparsec for both compressional and incompressible velocity components. The flow maintains approximate conditions of fully developed turbulence, with departures thereof settling in about an eddy-turnover time. Turbulent velocity dispersion remains above 700 km s{sup –1} even at low mass accretion rate, with the fraction of compressional energy between 10% and 40%. The normalization and the slope of the compressional turbulence are susceptible to large variations on short timescales, unlike the incompressible counterpart. A major merger occurs around redshift z ≅ 0 and is accompanied by a long period of enhanced turbulence, ascribed to temporal clustering of mass accretion related to spatial clustering of matter. We test models of stochastic acceleration by compressional modes for the origin of diffuse radio emission in galaxy clusters. The turbulence simulation model constrains an important unknown of this complex problem and brings forth its dependence on the elusive microphysics of the intracluster plasma. In particular, the specifics of the plasma collisionality and the dissipation physics of weak shocks affect the cascade of compressional modes with strong impact on the acceleration rates. In this context radio halos emerge as complex phenomena in which a hierarchy of processes acting on progressively smaller scales are at work. Stochastic acceleration by compressional modes implies statistical correlation of radio power and spectral index with merging cores distance, both testable in principle with radio surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Kinetic Monte Carlo Simulation of Hydrogen Diffusion in Tungsten

Nuclear fusion is considered as the most promising sustainable energy source. If achieved, it could provide electricity to the mankind for millions of years. During the reaction, the fusion ash is continuously removed from the magnetic gap, the divertor region, and the divertor facing component is subjected to intense plasma bombardment. It is proposed to use tungsten as the divertor facing material, because it has low physical sputtering yield and superior thermomechanical properties. However, the high mobility of hydrogen in tungsten and the high bombardment flux bring the hydrogen isotopes deep into the tungsten tile. The presence of hydrogen in tungsten tends to weaken the gram boundaries and produce surface blistering. The ejection of tungsten particulates into the plasma core will cool the reaction and cease the fusion reactions. Therefore, it is important to have a detailed understanding of the hydrogen diffusion in tungsten bulk. The KMC method is a stochastic computer simulation to predict the time evolution of a certain system. Typically, at a given time, the system will evolve through a few possible paths with known probabilities. In each step, the KMC method analyzes the entire system and randomly determines the system evolving events based on all transition rates of all possible events and advance the physical time. This process is repeated till the simulation of the entire physical time is completed. In previous work, the authors developed a kinetic Monte Carlo (KMC) algorithm and program to simulate hydrogen diffusion process on tungsten reconstructed surface. In this article, this KMC method is extended to model the hydrogen diffusion in tungsten bulk. The diffusion coefficients are calculated and fitted to the Arrhenius equation. The result matches the experiment measurement very well. KMC diffusion simulation needs the locations of the hydrogen trapping sites, diffusion paths and the corresponding diffusion energy barriers. The hydrogen interstitial sites in tungsten crystal have been thoroughly studied by density function theory (DFT) and molecular dynamics (MD). Johnson and Carter used DFT to determine the preferred location of the hydrogen interstitial sites m tungsten crystal, which are the tetrahedral sites (T-sites) in body-centered-cubic (bcc) lattice surfaces. All T-sites are located on the surfaces of a lattice cell. There are four T-sites per surface and twelve T-sites per lattice cell. The hydrogen atoms diffuse by traveling from one T-site to one of its nearest four T-site neighbors. Thus, each T-site is surrounded by two neighbors on the same surface, and one neighbor on each of the two adjacent surfaces perpendicular to the surface holding the center T-site. The diffusion barrier between two neighboring T-sites was calculated to be 0.38 eV. Knowing the diffusion path, energy barrier, and the crystal structure, the KMC simulation is readily to be performed. Based on other researchers' DFT calculations, the hydrogen stable interstitial site in tungsten bulk is determined as the tetrahedral site on the surface of the bee lattice. Each T-site has 4 neighbors and the energy barrier between two adjacent T-sites are estimated to be 0.38 eV. Based on those facts and the KMC theory, a MATLAB program is developed to model the hydrogen diffusion in tungsten bulk. In the MATLAB program, substrate temperature, hydrogen concentration, physical time to be simulated, and the number of cycles are all customizable. To compare the KMC determined diffusion coefficients with experiment results in the literature, the simulations are performed for temperature 300 K - 2500 K and low hydrogen concentration (0.1%), mimicking the experiment condition. The calculated diffusion coefficients match the reference values measured by the experiment under low hydrogen concentration condition. The results are also fitted to the Arrhenius equation. The pre-exponential factor and the average activation energy in Arrhenius equation are obtained. The good agreement between simulation and experiment proves that the developed KMC algorithm truly reflect the well-established theory of interstitial hydrogens in tungsten. The developed program could easily be used to model the diffusion process and predict the diffusion coefficients under other concentrations and temperatures. This work is part of the ongoing development of hydrogen diffusion in tungsten integrating surface diffusion (previous work), bulk diffusion (this work), and adsorption/desorption on surface (future work)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Lattice Monte Carlo simulation of Galilei variant anomalous diffusion

The observation of an increasing number of anomalous diffusion phenomena motivates the study to reveal the actual reason for such stochastic processes. When it is difficult to get analytical solutions or necessary to track the trajectory of particles, lattice Monte Carlo (LMC) simulation has been shown to be particularly useful. To develop such an LMC simulation algorithm for the Galilei variant anomalous diffusion, we derive explicit solutions for the conditional and unconditional first passage time (FPT) distributions with double absorbing barriers. According to the theory of random walks on lattices and the FPT distributions, we propose an LMC simulation algorithm and prove that such LMC simulation can reproduce both the mean and the mean square displacement exactly in the long-time limit. However, the error introduced in the second moment of the displacement diverges according to a power law as the simulation time progresses. We give an explicit criterion for choosing a small enough lattice step to limit the error within the specified tolerance. We further validate the LMC simulation algorithm and confirm the theoretical error analysis through numerical simulations. The numerical results agree with our theoretical predictions very well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

First-passage time statistics on surfaces of general shape: Surface PDE solvers using Generalized Moving Least Squares (GMLS)

Here, we develop numerical methods for computing statistics of stochastic processes on surfaces of general shape with drift-diffusion dynamics d X t = a (X t ) dt + b(X t ) d W t . We formulate descriptions of Brownian motion and general drift-diffusion processes on surfaces. We consider statistics of the form u (x) = E x [$∫^{τ}_{0}$ g (X t ) dt ] + E x [ f (X τ )] for a domain Ω and the exit stopping time τ = inf t { t >0 | X i Ω}, where f , g are general smooth functions. For computing these statistics, we develop high-order Generalized Moving Least Squares (GMLS) solvers for associated surface PDE boundary-value problems based on Backward- Kolmogorov equations. We focus particularly on the mean First Passage Times (FPTs) given by the case f = 0, g = 1 where u (x) = E x [τ]. We perform studies for a variety of shapes showing our methods converge with high-order accuracy both in capturing the geometry and the surface PDE solutions. We then perform studies showing how statistics are influenced by the surface geometry, drift dynamics, and spatially dependent diffusivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Thermodynamic and kinetic properties of layered-CaCo2O4 for the Ca-ion batteries: a systematic first-principles study

One of the more promising directions in multivalent energy storage is systems based on Ca ion intercalation due to the potential for high voltage and capacity. A major challenge for enabling such a battery is to find cathode materials capable of fast ionic diffusion and reversible insertion of Ca ions. Here, on the basis of first-principles calculations, we have demonstrated that layered CaCo 2 O 4 exhibits favorable thermodynamic and kinetic properties that should enable topotactic Ca ion intercalation reactions. The P3-type layered Ca x Co 2 O 4 (0 < x < 1) with either of space groups of P 1 or P 2 1 / m are stable at multiple Ca concentrations and show a smooth voltage plateau higher than 3 V up to X = 0.5. The energy barriers of the single Ca ion migration are as low as 0.36 eV and 0.27 eV at the dilute and high vacancy concentration limits, respectively. Therefore, although varying the vacancy environments of the diffusing atom influences the migration barriers, they do not exceed 0.6 eV. Stochastic analysis of Ca hopping events performed by ab initio molecular dynamics (AIMD) simulation has shown that the migration barriers are lower than 0.32 eV. Therefore, the Ca diffusivity at room temperature extrapolated from the AIMD results is comparable to Li diffusivity (>10 -10 cm 2 s -1 ) in conventional Li cathode materials, suggesting the feasibility of layered Ca x Co 2 O 4 as multivalent cathode materials. Finally, the structural factors that enable fast diffusion are discussed.

25 ENERGY STORAGE↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
DOE OSTI2014

Surface self-diffusion of silicon during high temperature annealing

The atomic-scale mechanisms driving thermally activated self-diffusion on silicon surfaces are investigated by atomic force microscopy. The evolution of surface topography is quantified over a large spatial bandwidth by means of the Power Spectral Density functions. We propose a parametric model, based on the Mullins-Herring (M-H) diffusion equation, to describe the evolution of the surface topography of silicon during thermal annealing. Usually, a stochastic term is introduced into the M-H model in order to describe intrinsic random fluctuations of the system. In this work, we add two stochastic terms describing the surface thermal fluctuations and the oxidation-evaporation phenomenon. Using this extended model, surface evolution during thermal annealing in reducing atmosphere can be predicted for temperatures above the roughening transition. A very good agreement between experimental and theoretical data describing roughness evolution and self-diffusion phenomenon is obtained. The physical origin and time-evolution of these stochastic terms are discussed. Finally, using this model, we explore the limitations of the smoothening of the silicon surfaces by rapid thermal annealing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

The role of fluctuations in bistability and oscillations during the H{sub 2} + O{sub 2} reaction on nanosized rhodium crystals

A combined experimental and theoretical study is presented of fluctuations observed by field ion microscopy in the catalytic reaction of water production on a rhodium tip. A stochastic approach is developed to provide a comprehensive understanding of the different phenomena observed in the experiment, including burst noise manifesting itself in a bistability regime, noisy oscillations, and nanopatterns with a cross-like oxidized zone separating the surface into four quadrants centered on the (111) facets. The study is based on a stochastic model numerically simulating the processes of adsorption, desorption, reaction, and transport. The surface diffusion of hydrogen is described as a percolation process dominated by large clusters corresponding to the four quadrants. The model reproduces the observed phenomena in the ranges of temperature, pressures, and electric field of the experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Fluctuating Hydrodynamic Methods for Fluid-Structure Interactions in Confined Channel Geometries

Computational methods are developed for the study of fluid-structure interactions subject to thermal fluctuations when conned within channels having slit-like geometry. The methods take into account the hydrodynamic coupling and diusivity of microstructures when influenced by their proximity to no-slip walls. We develop stochastic numerical methods subject to no-slip boundary conditions using a staggered nite volume discretization. We show that by imposing an exact fluctuation-dissipation condition to discretize the stochastic driving elds combined with using an exact projection method to enforce incompressibility is sucient to ensure results consistent with statistical mechanics. We demonstrate our methods by investigating how the proximity of ellipsoidal colloids to the channel wall eects their active hydrodynamic responses and passive diusivity. We also study the collective dynamics of a large number of particles by considering the intermediate scattering functions for the relaxation of density fluctuations. We expect our introduced stochastic computational methods to be applicable broadly to applications in which confinement effects play an important role in the dynamics of microstructures subject to hydrodynamic coupling and thermal fluctuations. uctuations.

Wang, Yaohong↗
DOE OSTI2020

Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for ab initio electronic structure calculations, in practice, the fermionic sign problem necessitates the use of the fixed-node approximation and trial wavefunctions with approximate nodes (or zeros). This approximation introduces a variational error in the energy that potentially can be tested and systematically improved. Here, we present a computational method that produces trial wavefunctions with systematically improvable nodes for DMC calculations of periodic solids. These trial wavefunctions are efficiently generated with the configuration interaction using a perturbative selection made iteratively (CIPSI) method. A simple protocol in which both exact and approximate results for finite supercells are used to extrapolate to the thermodynamic limit is introduced. This approach is illustrated in the case of the carbon diamond using Slater–Jastrow trial wavefunctions including up to one million Slater determinants. Fixed-node DMC energies obtained with such large expansions are much improved, and the fixed-node error is found to decrease monotonically and smoothly as a function of the number of determinants in the trial wavefunction, a property opening the way to a better control of this error. The cohesive energy extrapolated to the thermodynamic limit is in close agreement with the estimated experimental value. Interestingly, this is also the case at the single-determinant level, thus, indicating a very good error cancellation in carbon diamond between the bulk and atomic total fixed-node energies when using single-determinant nodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Computational Mechanics for Heterogeneous Materials

The subject of this work is the development of models for the numerical simulation of matter, momentum, and energy balance in heterogeneous materials. These are materials that consist of multiple phases or species or that are structured on some (perhaps many) scale(s). By computational mechanics we mean to refer generally to the standard type of modeling that is done at the level of macroscopic balance laws (mass, momentum, energy). We will refer to the flow or flux of these quantities in a generalized sense as transport. At issue here are the forms of the governing equations in these complex materials which are potentially strongly inhomogeneous below some correlation length scale and are yet homogeneous on larger length scales. The question then becomes one of how to model this behavior and what are the proper multi-scale equations to capture the transport mechanisms across scales. To address this we look to the area of generalized stochastic process that underlie the transport processes in homogeneous materials. The archetypal example being the relationship between a random walk or Brownian motion stochastic processes and the associated Fokker-Planck or diffusion equation. Here we are interested in how this classical setting changes when inhomogeneities or correlations in structure are introduced into the problem. Aspects of non-classical behavior need to be addressed, such as non-Fickian behavior of the mean-squared-displacement (MSD) and non-Gaussian behavior of the underlying probability distribution of jumps. We present an experimental technique and apparatus built to investigate some of these issues. We also discuss diffusive processes in inhomogeneous systems, and the role of the chemical potential in diffusion of hard spheres is considered. Also, the relevance to liquid metal solutions is considered. Finally we present an example of how inhomogeneities in material microstructure introduce fluctuations at the meso-scale for a thermal conduction problem. These fluctuations due to random microstructures also provide a means of characterizing the aleatory uncertainty in material properties at the mesoscale.

36 MATERIALS SCIENCE↗
NASA NTRS1993

On the structure of solar and stellar coronae - Loops and loop heat transport

We discuss the principal constraints on mechanisms for structuring and heating the outer atmospheres - the coronae - of stars. We argue that the essential cause of highly localized heating in the coronae of stars like the sun is the spatially intermittent nature of stellar surface magnetic fields, and that the spatial scale of the resulting coronal structures is related to the spatial structure of the photospheric fields. We show that significant constraints on coronal heating mechanisms derive from the observed variations in coronal emission, and, in addition, show that the observed structuring perpendicular to coronal magnetic fields imposes severe constraints on mechanisms for heat dispersal in the low-beta atmosphere. In particular, we find that most of commonly considered mechanisms for heat dispersal, such as anomalous diffusion due to plasma turbulence or magnetic field line stochasticity, are much too slow to account for the observed rapid heating of coronal loops. The most plausible mechanism appears to be reconnection at the interface between two adjacent coronal flux bundles. Based on a model invoking hyperresistivity, we show that such a mechanism naturally leads to dominance of isolated single bright coronal loops and to bright coronal plasma structures whose spatial scale transverse to the local magnetic field is comparable to observed dimensions of coronal X-ray loops.

Litwin, Christof↗
DOE OSTI2014

The heat released during catalytic turnover enhances the diffusion of an enzyme

Recent studies have shown that the diffusivity of enzymes increases in a substrate-dependent manner during catalysis. Although this observation has been reported and characterized for several different systems, the precise origin of this phenomenon is unknown. Calorimetric methods are often used to determine enthalpies from enzyme-catalysed reactions and can therefore provide important insight into their reaction mechanisms. The ensemble averages involved in traditional bulk calorimetry cannot probe the transient effects that the energy exchanged in a reaction may have on the catalyst. Here we obtain single-molecule fluorescence correlation spectroscopy data and analyse them within the framework of a stochastic theory to demonstrate a mechanistic link between the enhanced diffusion of a single enzyme molecule and the heat released in the reaction. We propose that the heat released during catalysis generates an asymmetric pressure wave that results in a differential stress at the protein-solvent interface that transiently displaces the centre-of-mass of the enzyme (chemoacoustic effect). We find this novel perspective on how enzymes respond to the energy released during catalysis suggests a possible effect of the heat of reaction on the structural integrity and internal degrees of freedom of the enzyme.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

On the spin-axis dynamics of a Moonless Earth

The variation of a planet's obliquity is influenced by the existence of satellites with a high mass ratio. For instance, Earth's obliquity is stabilized by the Moon and would undergo chaotic variations in the Moon's absence. In turn, such variations can lead to large-scale changes in the atmospheric circulation, rendering spin-axis dynamics a central issue for understanding climate. The relevant quantity for dynamically forced climate change is the rate of chaotic diffusion. Accordingly, here we re-examine the spin-axis evolution of a Moonless Earth within the context of a simplified perturbative framework. We present analytical estimates of the characteristic Lyapunov coefficient as well as the chaotic diffusion rate and demonstrate that even in absence of the Moon, the stochastic change in Earth's obliquity is sufficiently slow to not preclude long-term habitability. Our calculations are consistent with published numerical experiments and illustrate the putative system's underlying dynamical structure in a simple and intuitive manner.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A spectral analysis of the domain decomposed Monte Carlo method for linear systems

The domain decomposed behavior of the adjoint Neumann-Ulam Monte Carlo method for solving linear systems is analyzed using the spectral properties of the linear operator. Relationships for the average length of the adjoint random walks, a measure of convergence speed and serial performance, are made with respect to the eigenvalues of the linear operator. In addition, relationships for the effective optical thickness of a domain in the decomposition are presented based on the spectral analysis and diffusion theory. Using the effective optical thickness, the Wigner rational approximation and the mean chord approximation are applied to estimate the leakage fraction of stochastic histories from a domain in the decomposition as a measure of parallel performance and potential communication costs. The one-speed, two-dimensional neutron diffusion equation is used as a model problem to test the models for symmetric operators. In general, the derived approximations show good agreement with measured computational results. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Communication: Memory effects and active Brownian diffusion

A self-propelled artificial microswimmer is often modeled as a ballistic Brownian particle moving with constant speed aligned along one of its axis, but changing direction due to random collisions with the environment. Similarly to thermal noise, its angular randomization is described as a memoryless stochastic process. Here, we speculate that finite-time correlations in the orientational dynamics can affect the swimmer’s diffusivity. To this purpose, we propose and solve two alternative models. In the first one, we simply assume that the environmental fluctuations governing the swimmer’s propulsion are exponentially correlated in time, whereas in the second one, we account for possible damped fluctuations of the propulsion velocity around the swimmer’s axis. The corresponding swimmer’s diffusion constants are predicted to get, respectively, enhanced or suppressed upon increasing the model memory time. Possible consequences of this effect on the interpretation of the experimental data are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗