Search NASA⌕ Search

SEARCH · Search NASA

Results for “Diffusion problems”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.

17 WIND ENERGY↗
DOE OSTI2018

Averaging Principle for the Higher Order Nonlinear Schrödinger Equation with a Random Fast Oscillation

This work concerns the problem associated with averaging principle for a higher order nonlinear Schrödinger equation perturbed by a oscillating term arising as the solution of a stochastic reaction–diffusion equation evolving with respect to the fast time. This model can be translated into a multiscale stochastic partial differential equations. Stochastic averaging principle is a powerful tool for studying qualitative analysis of stochastic dynamical systems with different time-scales. To be more precise, under suitable conditions, we prove that there is a limit process in which the fast varying process is averaged out and the limit process which takes the form of the higher order nonlinear Schrödinger equation is an average with respect to the stationary measure of the fast varying process. Finally, by using the Khasminskii technique we can obtain the rate of strong convergence for the slow component towards the solution of the averaged equation, and as a consequence, the system can be reduced to a single higher order nonlinear Schrödinger equation with a modified coefficient.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Linear stability analysis of two fluid columns of different densities and viscosities in a gravity field

The linear stability of a vertical interface separating two miscible fluid columns of different densities and viscosities under the influence of gravity is investigated. This flow possesses a time-dependent reference state (each column accelerates at different rates owing to their different densities) and the interface thickness grows as the square root of time (by diffusion). Numerical integration of the linear initial-value problem is carried out and discussed in detail as a function of vertical and spanwise wavenumbers and the flow parameters. Adjoint-based optimization is performed in order to determine initial conditions that lead to maximum growth of disturbances in finite time. Results indicate that the rate of growth of the perturbation energy at small wavenumbers (less affected by viscosity initially) is dominated by two-dimensional modes (no spanwise variation). Substantial transient growth is observed at higher wave modes initially, followed by asymptotic decay of the perturbations at large time. Sensitivity of perturbation growth with respect to initial time, density and viscosity ratios is investigated. This work is complementary to previous inviscid analysis of this configuration, which showed that the interface was unconditionally unstable at all wave modes, even in the presence of surface tension, and that instability grew as the exponential of time squared.

Mechanics↗
DOE OSTI2020

Turbulence-level dependence of cosmic ray parallel diffusion

ABSTRACT Understanding the transport of energetic cosmic rays belongs to the most challenging topics in astrophysics. Diffusion due to scattering by electromagnetic fluctuations is a key process in cosmic ray transport. The transition from a ballistic to a diffusive-propagation regime is presented in direct numerical calculations of diffusion coefficients for homogeneous magnetic field lines subject to turbulent perturbations. Simulation results are compared with theoretical derivations of the parallel diffusion coefficient’s dependences on the energy and the fluctuation amplitudes in the limit of weak turbulence. The present study shows that the widely used extrapolation of the energy scaling for the parallel diffusion coefficient to high turbulence levels predicted by quasi-linear theory does not provide a universally accurate description in the resonant-scattering regime. It is highlighted here that the numerically calculated diffusion coefficients can be polluted for low energies due to missing resonant interaction possibilities of the particles with the turbulence. Five reduced-rigidity regimes are established, which are separated by analytical boundaries derived in this work. Consequently, a proper description of cosmic ray propagation can only be achieved by using a turbulence-level-dependent diffusion coefficient and can contribute to solving the Galactic cosmic ray gradient problem.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A simple model of the effect of ocean ventilation on ocean heat uptake

Presentation includes slides on Earth System Models vs. Simple Climate Models; A Popular SCM: Energy Balance Model of Anomalies; On calibrating against one ESM experiment, the SCM correctly captures that ESM's surface warming response with other forcings; Multi-Model Analysis: Multiple ESMs, Single SCM; Posterior Distributions of ECS; However In Excess of 90% of TOA Energy Imbalance is Sequestered in the World Oceans; Heat Storage in the Two Layer Model; Heat Storage in the Two Layer Model; Including TOA Rad. Imbalance and Ocean Heat in Calibration Improves Repr., but Significant Errors Persist; Improved Vertical Resolution Does Not Fix Problem; A Series of Expts. Confirms That Anomaly-Diffusing Models Cannot Properly Represent Ocean Heat Uptake; Physics of the Thermocline; Outcropping Isopycnals and Horizontally-Averaged Layers; Local interactions between outcropping isopycnals leads to non-local interactions between horizontally-averaged layers; Both Surface Warming and Ocean Heat are Well Represented With Just 4 Layers; A Series of Expts. Confirms That When Non-Local Interactions are Allowed, the SCMs Can Represent Both Surface Warming and Ocean Heat Uptake; and Summary and Conclusions.

58 GEOSCIENCES↗
DOE OSTI2018

Electric-Loading Enhanced Kinetics in Oxide Ceramics: Pore Migration, Sintering and Grain Growth: Final Report

Solid oxide fuel cells and solid oxide electrolysis cells rely on solid electrolytes in which a large ionic current dominates. This project was initiated to investigate microstructural changes in such devices under electrochemical forces, because nominally insignificant processes may couple to the large ionic current to yield non-equilibrium phenomena that alter the microstructure. Our studies had focused on yttria-stabilized cubic zirconia (YSZ) widely used in these devices. The experiments have revealed enhanced grain growth at higher temperatures, pore and gas bubble migration at all temperatures, and the latter also lead to enhanced sintering of highly porous ceramics into fully dense ceramics at unprecedentedly low temperatures. These results have shed light on kinetic processes that fall completely outside the realm of classical ceramic processing. Other fast-oxygen oxide ceramics closely related to, and often used in conjunction with zirconia ceramics, have also be investigated, as are closely related scientific problems in zirconia ceramics. These include crystal structures, defects, diffusion kinetics, oxygen potentials, low temperature sintering, flash sintering, and coarsening theory, and all have resulted in greater clarity in scientific understanding. The knowledge is leveraged to provide new insight to electrode kinetics and near-electrode mixed conductivity and to new materials. In the following areas, our research has resulted in completely new knowledge that defines the state-of-the-art of the field. (a) Electrical current driven non-equilibrium phenomena, (b) Enhanced grain growth under electrochemically reducing conditions, (c) Development of oxygen potential polarization in electrically loaded electrolyte, (d) Low temperature sintering and grain growth, and (e) Structure, defects and cation kinetics of fluorite-structured oxides. Our research has also contributed to synthesis of new energy-relevant electrochemical materials and new understanding of flash sintering, which is a rapid sintering process initiated by a large electrical loading.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems

We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A front-fixing finite element method for pricing American options under regime-switching jump-diffusion models

A front-fixing finite element method is applied to solve the partial integro-differential equations (PIDEs) arising in pricing American options under Markov-modulated jump-diffusion models with the free boundaries feature. For this purpose, we used a front-fixing method to transform the pricing problem into a nonlinear parabolic integro-differential equation on a fixed domain. Then the variational form of the resulting problem is solved by a finite element method. Under some appropriate assumptions, we establish the stability of the method and illustrate some numerical results to examine the rate of convergence of the proposed method for the pricing problem and compare its accuracy to some recent works on pricing American options under regime-switching jump-diffusion models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems

Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatial profiles of photon chemical potential in near-field thermophotovoltaic cells

Emitted photons stemming from the radiative recombination of electron–hole pairs carry chemical potential in radiative energy converters. This luminescent effect can substantially alter the local net photogeneration in near-field thermophotovoltaic cells. Several assumptions involving the luminescent effect are commonly made in modeling photovoltaic devices; in particular, the photon chemical potential is assumed to be zero or a constant prescribed by the bias voltage. The significance of photon chemical potential depends upon the emitter temperature, the semiconductor properties, and the injection level. Hence, these assumptions are questionable in near-field thermophotovoltaic devices with nanoscale separation distances between the emitter and the cell. In the present work, an iterative solver that combines fluctuational electrodynamics with the drift-diffusion model is developed to tackle the coupled photon and charge transport problem, enabling the determination of the spatial profile of photon chemical potential beyond the detailed balance approach. Here, the difference between the results obtained by allowing the photon chemical potential to vary spatially and by assuming a constant value demonstrates the limitations of the conventional approaches. This study is critically important for performance evaluation of near-field thermophotovoltaic systems with nanoscale vacuum gaps.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Efficient smoothed particle radiation hydrodynamics I: Thermal radiative transfer

This work presents efficient solution techniques for radiative transfer in the smoothed particle hydrodynamics discretization. Two choices that impact efficiency are how the material and radiation energy are coupled, which determines the number of iterations needed to converge the emission source, and how the radiation diffusion equation is solved, which must be done in each iteration. The coupled material and radiation energy equations are solved using an inexact Newton iteration scheme based on nonlinear elimination, which reduces the number of Newton iterations needed to converge within each time step. During each Newton iteration, the radiation diffusion equation is solved using Krylov iterative methods with a multigrid preconditioner, which abstracts and optimizes much of the communication when running in parallel. The code is verified for an infinite medium problem, a one-dimensional Marshak wave, and a two and three-dimensional manufactured problem, and exhibits first-order convergence in time and second-order convergence in space. For these problems, the number of iterations needed to converge the inexact Newton scheme and the diffusion equation is independent of the number of spatial points and the number of processors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

On Integral of a Semi-Markov Diffusion Process

Let (X(t)) (t ≥ 0) be a semi-Markov diffusion process. The process (J(T )) (T ≥ 0) equal to the integral of (X(t)) on interval [0, T ) is studied. The relation between one-dimensional differential equation of the second order of elliptical type and asymptotics of a solution to Dirichlet problem on an interval with length tending to zero is established. This relation is used to derive a differential equation for the Laplace transform of the semi-Markov generating function of the process (J(t)).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hand Calculations for Nuclear Criticality Safety – Primer Revision

The document “ Hand Calculation Methods for Criticality Safety – A Primer, ” was published by the authors (Los Alamos National Laboratory report LA-14244-M) in December 2006 to provide an overview of the most common hand calculation methods used for criticality safety analysis. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information quicker than using a criticality code, such as KENO or MCNP. This report has been revised to augment existing basis information and example problems for each of the methods discussed: 1-group and modified 1- group diffusion theory, core-density conversions, buckling conversions, limiting surface density method, density analog method, surface density method, and solid angle method. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the example problem results. The example problems provided are simple, practical problems, and the reference data to solve each example problem is provided. This paper will provide an overview of the Primer revision and how it can be useful for new Nuclear Criticality Safety practitioners.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Hand Calculations for Nuclear Criticality Safety — Primer Revision [Abstract]

The document “ Hand Calculation Methods for Criticality Safety –A Primer, ” was published by the authors (Los Alamos National Laboratory report LA-14244-M) in December 2006 to provide an overview of the most common hand calculation methods used for criticality safety analysis. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information quicker than using a criticality code, such as KENO or MCNP. This report has been revised to augment existing basis information and example problems for each of the methods discussed: 1-group and modified 1-group diffusion theory, core-density conversions, buckling conversions, limiting surface density method, density analog method, surface density method, and solid angle method. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the example problem results. The example problems provided are simple, practical problems, and the reference data to solve each example problem is provided. This paper will provide an overview of the Primer revision and how it can be useful for new Nuclear Criticality Safety practitioners.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

An Investigation of Chaotic Diffusion in a Family of Hamiltonian Mappings Whose Angles Diverge in the Limit of Vanishingly Action

The chaotic diffusion for a family of Hamiltonian mappings whose angles diverge in the limit of vanishingly action is investigated by using the solution of the diffusion equation. The system is described by a two-dimensional mapping for the variables action, I, and angle, θ and controlled by two control parameters: (i) ϵ, controlling the nonlinearity of the system, particularly a transition from integrable for ϵ=0 to non-integrable for ϵ≠0 and; (ii) γ denoting the power of the action in the equation defining the angle. For ϵ≠0 the phase space is mixed and chaos is present in the system leading to a finite diffusion in the action characterized by the solution of the diffusion equation. The analytical solution is then compared to the numerical simulations showing a remarkable agreement between the two procedures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

New discretization of Caputo–Fabrizio derivative

We derive a numerical approximation, namely L1–2 formula, to the Caputo–Fabrizio derivative by using a quadratic interpolation. Quadratic and cubic convergence rates are achieved for L1 and L1–2 formulas using Lagrange interpolation, respectively. We compute Caputo–Fabrizio derivatives of some known functions both theoretically and numerically. In addition, we solve non/linear sub-diffusion equations to test theoretical findings. Numerical results confirm the theoretically observed convergence rates.

97 MATHEMATICS AND COMPUTING↗