Search NASA⌕ Search

SEARCH · Search NASA

Results for “Stochastic diffusion”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A feedback control strategy for the airfoil system under non-Gaussian colored noise excitation

The stability of a binary airfoil with feedback control under stochastic disturbances, a non-Gaussian colored noise, is studied in this paper. First, based on some approximated theories and methods the non-Gaussian colored noise is simplified to an Ornstein-Uhlenbeck process. Furthermore, via the stochastic averaging method and the logarithmic polar transformation, one dimensional diffusion process can be obtained. At last by applying the boundary conditions, the largest Lyapunov exponent which can determine the almost-sure stability of the system and the effective region of control parameters is calculated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A Subgrid Approach for Modeling Microtopography Effects on Overland Flow

Microtopography, or heterogeneities in the elevation across scales much smaller than the domain of interest, plays a critical role in surface water retention, surface/subsurface interactions, and runoff. Resolving microtopographic influences on flow requires high–resolution simulations, which are computationally demanding even when considering the surface system in isolation and even more so when surface flow is one component in integrated simulations that couple surface flow with unsaturated subsurface flow. There is thus significant motivation for models that allow the effects of subgrid microtopography to be better represented. Subgrid models modify coarsened models to capture the microtopography–induced nonlinear effects on hydrologic processes. We present a subgrid model that alters the water storage and flow terms in the diffusion wave equation for surface flow. Stochastically generated microtopography with strongly contrasting spatial structure, high–resolution digital elevation maps from a polygonal tundra site on the North Slope of Alaska and a hummocky microtopography from a field site in Northern Minnesota are used to assess the accuracy and applicability of the subgrid model to disparate landscapes. Approaches for determining subgrid model parameters are tested and simulation results using the subgrid model are compared to benchmark fine–scale simulations and to coarse simulations that ignore microtopography. Our findings confirm that a properly parameterized subgrid model greatly improves the coarse–scale representation of hydrographs and total water content in the system. Furthermore, using the polygonal tundra example, we propose and test a strategy for moving to application–relevant spatial scales by combining microtopography classification and a few fine–scale simulations on small subdomains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Use of Cloud Radar Doppler Spectra to Evaluate Stratocumulus Drizzle Size Distributions in Large-Eddy Simulations with Size-Resolved Microphysics

Abstract A case study of persistent stratocumulus over the Azores is simulated using two independent large-eddy simulation (LES) models with bin microphysics, and forward-simulated cloud radar Doppler moments and spectra are compared with observations. Neither model is able to reproduce the monotonic increase of downward mean Doppler velocity with increasing reflectivity that is observed under a variety of conditions, but for differing reasons. To a varying degree, both models also exhibit a tendency to produce too many of the largest droplets, leading to excessive skewness in Doppler velocity distributions, especially below cloud base. Excessive skewness appears to be associated with an insufficiently sharp reduction in droplet number concentration at diameters larger than ~200 μm, where a pronounced shoulder is found for in situ observations and a sharp reduction in reflectivity size distribution is associated with relatively narrow observed Doppler spectra. Effectively using LES with bin microphysics to study drizzle formation and evolution in cloud Doppler radar data evidently requires reducing numerical diffusivity in the treatment of the stochastic collection equation; if that is accomplished sufficiently to reproduce typical spectra, progress toward understanding drizzle processes is likely.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

KINETIC SIMULATIONS OF THE LOWEST-ORDER UNSTABLE MODE OF RELATIVISTIC MAGNETOSTATIC EQUILIBRIA

We present the results of particle-in-cell numerical pair plasma simulations of relativistic two-dimensional magnetostatic equilibria known as the “Arnold–Beltrami–Childress” fields. In particular, we focus on the lowest-order unstable configuration consisting of two minima and two maxima of the magnetic vector potential. Breaking of the initial symmetry leads to exponential growth of the electric energy and to the formation of two current layers, which is consistent with the picture of “X-point collapse” first described by Syrovatskii. Magnetic reconnection within the layers heats a fraction of particles to very high energies. After the saturation of the linear instability, the current layers are disrupted and the system evolves chaotically, diffusing the particle energies in a stochastic second-order Fermi process, leading to the formation of power-law energy distributions. The power-law slopes harden with the increasing mean magnetization, but they are significantly softer than those produced in simulations initiated from Harris-type layers. The maximum particle energy is proportional to the mean magnetization, which is attributed partly to the increase of the effective electric field and partly to the increase of the acceleration timescale. We describe in detail the evolving structure of the dynamical current layers and report on the conservation of magnetic helicity. These results can be applied to highly magnetized astrophysical environments, where ideal plasma instabilities trigger rapid magnetic dissipation with efficient particle acceleration and flares of high-energy radiation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A single-column particle-resolved model for simulating the vertical distribution of aerosol mixing state: WRF-PartMC-MOSAIC-SCM v1.0

Abstract. The PartMC-MOSAIC particle-resolved aerosol model was previously developed to predict the aerosol mixing state as it evolves in the atmosphere. However, the modeling framework was limited to a zero-dimensional box model approach without resolving spatial gradients in aerosol concentrations. This paper presents the development of stochastic particle methods to simulate turbulent diffusion and dry deposition of aerosol particles in a vertical column within the planetary boundary layer. The new model, WRF-PartMC-MOSAIC-SCM, resolves the vertical distribution of aerosol mixing state. We verified the new algorithms with analytical solutions for idealized test cases and illustrate the capabilities with results from a 2-day urban scenario that shows the evolution of black carbon mixing state in a vertical column.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Electron acceleration by magnetosonic waves in solar flares

Results of a simulation of electron acceleration by magnetosonic waves in a solar coronal loop are presented. The dependence of the energization rate on the strength and shape of the wave spectrum is discussed. Comparisons are made with the predictions of quasilinear theory and of a model describing stochastic acceleration by waves as isotropic diffusion in momentum space.

Zweibel, Ellen G.↗
NASA NTRS1994

Simulation of magnetic field line stochasticity at the magnetopause

We have conducted a three-dimensional particle simulation to study the magnetic field line stochasticity at the magnetopause current layer. Our results show that the magnetic field lines become stochastic due to the overlap of the destabilized multiple tearing mode islands, which agrees with the percolation model suggested by Galeev et al. (1986). After the field lines become stochastic, these tearing modes grow even 2-3 times faster than in the linear stage and saturate at an amplitude level 3-4 times bigger than the single tearing mode without mode-mode coupling. The field line stochasticity also causes a strong particle diffusion across the current layer. The diffusion coefficient reaches to 10(exp 9) sq m/s for typical magnetopause parameters. Associated with the particle diffusion, the current layer becomes broader in width. As a result, the magnetic energy is dissipated into particle energy by heating parallel to the local magnetic field. The particle energy increases by 60%, while the magnetic helicity, which has always been regarded as a good invariant, changes by 20%.

Wang, Zhi↗
DOE OSTI2023

Reliability-informed end-of-use decision making for product sustainability using two-stage stochastic optimization

The concept of circular economy has been diffused in recent decades to promote economic growth that does not add to the burden on natural resource extraction. Re-X options (e.g., reuse, repair, refurbish, remanufacture, recycle) have been gradually adopted in the product development process and optimized to reduce or eliminate waste and pollution. Although manufacturing incorporating Re-X options can be more environmentally friendly, it involves more sources of uncertainty than traditional manufacturing since the end-of-use products can be collected from multiple origins with various quantities and qualities, and the market demand for both new and remanufactured products cannot be forecasted perfectly. Thus, there is a need to optimize the Re-X policy to alleviate the negative impacts of the higher uncertainty. One option is using the reliability information of new products to estimate the end-of-use conditions and applying multi-stage stochastic optimization to capture multiple demand scenarios. This paper develops a two-stage stochastic optimization model to optimize the quality thresholds for reuse, recycling, and remanufacturing options. Our objective is to minimize the total cost, energy consumption, and environmental impact of producing and providing warranty service for a product family. The model employs reliability information of product components to estimate the warranty service cost and the end-of-use conditions of the returned resources. A case study on a general product family is implemented to illustrate the efficacy of the optimization model. Finally, results show that the two-stage optimization can achieve cost and environmental impact reduction for a hybrid manufacturing and remanufacturing process.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Electron acceleration by an obliquely propagating electromagnetic wave in the regime of validity of the Fokker-Planck-Kolmogorov approach

The relativistic motion of an ensemble of electrons in an intense monochromatic electromagnetic wave propagating obliquely in a uniform external magnetic field is studied. The problem is formulated from the viewpoint of Hamiltonian theory and the Fokker-Planck-Kolmogorov approach analyzed by Hizanidis (1989), leading to a one-dimensional diffusive acceleration along paths of constant zeroth-order generalized Hamiltonian. For values of the wave amplitude and the propagating angle inside the analytically predicted stochastic region, the numerical results suggest that the diffusion probes proceeds in stages. In the first stage, the electrons are accelerated to relatively high energies by sampling the first few overlapping resonances one by one. During that stage, the ensemble-average square deviation of the variable involved scales quadratically with time. During the second stage, they scale linearly with time. For much longer times, deviation from linear scaling slowly sets in.

Hizanidis, Kyriakos↗
NASA NTRS1986

A study of the stochastic energization of charged particles with and without synchrotron energy loss

A momentum-diffusion formulation that takes the initial values of the particle distribution functions and momentum-space boundary conditions is presently employed for the computational and analytical study of the stochastic energization of charged particles by plasma waves. The nature of the particle distribution functions resulting from stochastic energization and the validity of the momentum-diffusion formulation for the energization are determined. The validity of the momentum-diffusion formulation with power law diffusion coefficients is assessed.

Borovsky, J. E.↗
NASA NTRS1985

The polar ionosphere as a source of the storm time ring current

The transport of ions from the polar ionosphere to the inner magnetosphere during storm time conditions has been computed using a Monte Carlo diffusion code. The effect of the electrostatic turbulence assumed to be present during the substorm expansion phase was simulated by a process that accelerated the ions stochastically perpendicular to the magnetic field with a diffusion coefficient proportional to the rate of energization of the ions by the induced electric field. This diffusion process was continued as the ions were convected from the plasma sheet boundary layer to the double-spiral injection boundary. Inward of the injection boundary the ions were convected adiabatically. By using as input an O(+) flux of 2.8 x 10 to the 8th per sq cm per s (w greater than 10 eV) and an H(+) flux of 5.5 x 10 to the 8th per sq cm per s (w greater than .63 eV) the computed distribution functions of the ions in the ring current were found to be in good agreement, over a wide range in L (4-8), with measurements made with the ISEE 1 satellite during a storm. This O(+) flux and a large part of the H(+) flux appear to be consistent with the DE-1 and DE-2 satellite measurements of the polar ionospheric outflow during disturbed times.

Cladis, J. B.↗
NASA NTRS1985

Transport of ions in presence of induced electric field and electrostatic turbulence - Source of ions injected into ring current

The transport of ions from the polar ionosphere to the inner magnetosphere during stormtime conditions has been computed using a Monte Carlo diffusion code. The effect of the electrostatic turbulence assumed to be present during the substorm expansion phase was simulated by a process that accelerated the ions stochastically perpendicular to the magnetic field with a diffusion coefficient proportional to the energization rate of the ions by the induced electric field. This diffusion process was continued as the ions were convected from the plasma sheet boundary layer to the double-spiral injection boundary. Inward of the injection boundary, the ions were convected adiabatically. By using as input an O(+) flux of 2.8 x 10 to the 8th per sq cm per s (w greater than 10 eV) and an H(+) flux of 5.5 x 10 to the 8th per sq cm per s (w greater than 0.63 eV), the computed distribution functions of the ions in the ring current were found to be in good agreement, over a wide range in L (4 to 8), with measurements made with the ISEE-1 satellite during a storm. This O(+) flux and a large part of the H(+) flux are consistent with the DE satellite measurements of the polar ionospheric outflow during disturbed times.

Cladis, J. B.↗
NASA NTRS1992

The energy spectra of solar flare hydrogen, helium, oxygen, and iron - Evidence for stochastic acceleration

The time-integrated differential energy spectra of H, He, O, and Fe measured in 10 large flare events observed at 1 AU over the energy range of 0.3-80 MeV/nucleon showed consistent patterns in their spectral shapes: particles with larger mean mass-to-charge ratios were generally less abundant at higher energies. A steady state model of stochastic particle acceleration with rigidity-dependent diffusion coefficients fit the spectra best; spectra representative of diffusive shock acceleration also described the spectra of some events with the same number of free parameters, but often fell off faster in energy above 30 MeV per nucleon than the observations. The two model predictions differed most at energies near 0.1 MeV per nucleon, below the lowest energies observed in this study. The stochastic model quantitatively described the observed spectral ordering with less efficient acceleration of species with larger mean mass-to-charge ratios.

Mazur, J. E.↗
DOE OSTI2022

Stochastic Modeling in a Multimaterial Continuum Mixture Shock Physics Code

Stochastic modelling approaches are presented to capture random effects at multiple time and length scales. Random processes that occur at the microscale produce nondeterministic effects at the macroscale. Here we present three stochastic modeling approaches that describe random processes at microscopic length scales and map these processes to the macroscopic length scale. The first stochastic modeling approach is based upon a particle based numerical technique to solve a Stochastic Differential Equation (SDE) using an arbitrary diffusion process to capture random processes at the microstructural level. The second approach prescribes a Probability Density Function (PDF) for the drift and diffusion of the random variable derived using the forward and backward Kolmogorov equations. This method requires mean and drift evolution PDF transport equations. The third approach is the coupling of multiple random variables which are dependent on each other. The relationship of the PDFs and a coupling function, known as a copula, produces a Joint Probability Density Function (JPDF). These stochastic modeling approaches are implemented into a Multiple Component (MC) shock physics computational code and used to model statistical fracture and reactive flow applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Hybrid framework for the simulation of stochastic chemical kinetics

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Code for the manuscript titled "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces"

We would like to disclose two scripts, written in Jupyter notebook, in which we implement the "Blackout Diffusion Process" described in the manuscript "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces" (LA-UR-23-20509), to be submitted to the International Conference of Machine Learning (ICML). The abstract of the manuscript is append below. == Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, which occur in many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. We relate the theory to the existing continuous-state Gaussian diffusion and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce "Blackout Diffusion", which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10 dataset confirm the feasibility of generative diffusion modeling in a discrete space. Generalizing from specific (Gaussian) forward processes to a more general framework also sheds light on how to interpret generative diffusion models and their mathematical structure, which we comment on.

Lin, Yen Ting↗
DOE OSTI2023

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process using exact (as opposed to variational) analysis. We relate the theory to the existing continuous-state Gaussian diffusion as well as other approaches to discrete diffusion, and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce “Blackout Diffusion”, which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10, Binarized MNIST, and CelebA datasets confirm the feasibility of our approach. Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss.

Santos, Javier E.↗