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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Phosphate amendments for chemical immobilization of uranium in contaminated soil

Uranium (U) contamination is a major environmental problem associated with the mining and processing of nuclear materials for both weapons and power production. When possible, in situ soil remediation techniques are preferable for reducing the risk associated with diffuse low-level U contamination. Uranium is known to form sparingly soluble phosphate compounds that persist in the environment. Therefore, batch experiments were performed to evaluate the efficacy of three phosphate amendments, hydroxyapatite (HA), sodium phytate (IP6) and sodium tripolyphosphate (TPP), to immobilize U in contaminated sediments. The amendments were added at equivalent phosphorus (P) concentrations and then equilibrated under a range of test conditions, with changes in soluble U and Ptotal monitored at pre-set time intervals. Only HA was effective at reducing the soluble U soil fraction when compared to the control, with IP6 and TPP increasing the soluble U soil fraction. After equilibration, changes in contaminant partitioning in the amended sediments were evaluated using operational extraction methods. Sequential extraction results for HA generally indicated a transfer of U from labile to more recalcitrant phases, while the results for IP6 and TPP were more ambiguous.

Uranyl↗
DOE OSTI2025

Moment-based adaptive time integration for thermal radiation transport

Here, in this paper we develop a framework for moment-based adaptive time integration of deterministic multifrequency thermal radiation transpot (TRT). We generalize our recent semi-implicit-explicit (IMEX) integration framework for gray TRT to multifrequency TRT, and also introduce a semi-implicit variation that facilitates higher-order integration of TRT, where each stage is implicit in all components except opacities. To appeal to the broad literature on adaptivity with Runge–Kutta methods, we derive new embedded methods for four asymptotic preserving IMEX Runge–Kutta schemes we have found to be robust in our previous work on TRT and radiation hydrodynamics. We then use a moment-based high-order-low-order representation of the transport equations. Due to the high dimensionality, memory is always a concern in simulating TRT. We form error estimates and adaptivity in time purely based on temperature and radiation energy, for a trivial overhead in computational cost and memory usage compared with the base second order integrators. We then test the adaptivity in time on the tophat and Larsen problem, demonstrating the ability of the adaptive algorithm to naturally vary the timestep across 4–5 orders of magnitude, ranging from the dynamical timescales of the streaming regime to the thick diffusion limit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mitigating spectral bias in neural operators via high-frequency scaling for physical systems

Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Unusual electrical conductivity driven by localized stoichiometry modification at vertical epitaxial interfaces

Precise control of lattice mismatch accommodation and interfacial cation diffusion is critical to modulate correlated functionalities in epitaxial heterostructures, particularly when the interface composition is positioned near a compositional phase transition boundary. Here we select La 1-x Sr x MnO 3 (LSMO) as a prototype phase transition oxide and establish vertical epitaxial interfaces with NiO for exploring the strong interplay between strain accommodation, stoichiometry modification, and localized electron transport across the interface. It is found that localized stoichiometry modification overcomes the dead layer problem plaguing LSMO and leads to strongly directional conductivity, as manifested by a more than three orders of magnitude difference between out-of-plane and in-plane conductivity. Comprehensive atomic scale structural characterization and transport measurements reveal that this emerging behavior is created by a compositional change produced by preferential cation diffusion that pushes the LSMO phase transition from insulating into metallic within an ultrathin interface region. This study explores the nature of unusual electrical conductivity at vertical epitaxial interfaces and highlights the beneficial role of controllable cation diffusion that enables emerging functionalities for a broad range of potential applications such as memristors, spintronic devices, and novel nanoelectronic devices using strongly correlated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A new approach to model geomaterials with heterogeneous properties in thermo-hydro-mechanical coupled problems

The main objective of this article is to present a new approach to model coupled thermo-hydro-mechanical problems considering geomaterials with heterogeneous properties. This approach has been implemented in the software CODE_BRIGHT and it provides the possibility of considering geomaterials with a spatially correlated heterogeneous field of porosity, following a normal distribution. This spatial correlation can be isotropic or anisotropic. An important feature of this approach is that material properties such as intrinsic permeability, thermal conductivity, diffusivity, retention curve, elastic modulus or cohesion are defined as a function of porosity and, thus, they become heterogeneous with spatial correlation and, eventually, anisotropic. A validation exercise and other basic numerical examples have been carried out to illustrate the possibilities of the proposed approach. The results, which have been compared with a homogeneous case, show that considering heterogeneous fields can be relevant in different modelling problems, especially coupled thermo-hydro-mechanical problems.

42 ENGINEERING↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2017

An iterative phase-space explicit discontinuous Galerkin method for stellar radiative transfer in extended atmospheres

In this work, a phase-space discontinuous Galerkin (PSDG) method is presented for the solution of stellar radiative transfer problems. It allows for greater adaptivity than competing methods without sacrificing generality. The method is extensively tested on a spherically symmetric, static, inverse-power-law scattering atmosphere. Results for different sizes of atmospheres and intensities of scattering agreed with asymptotic values. The exponentially decaying behavior of the radiative field in the diffusive-transparent transition region, and the forward peaking behavior at the surface of extended atmospheres were accurately captured. The integrodifferential equation of radiation transfer is solved iteratively by alternating between the radiative pressure equation and the original equation with the integral term treated as an energy density source term. In each iteration, the equations are solved via an explicit, flux-conserving, discontinuous Galerkin method. Finite elements are ordered in wave fronts perpendicular to the characteristic curves so that elemental linear algebraic systems are solved quickly by sweeping the phase space element by element. Two implementations of a diffusive boundary condition at the origin are demonstrated wherein the finite discontinuity in the radiation intensity is accurately captured by the proposed method. This allows for a consistent mechanism to preserve photon luminosity. The method was proved to be robust and fast, and a case is made for the adequacy of parallel processing. In addition to classical two-dimensional plots, results of normalized radiation intensity were mapped onto a log-polar surface exhibiting all distinguishing features of the problem studied.

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

Effects of operator splitting and low Mach-number correction in turbulent mixing transition simulations

In this paper, transition and turbulence decay with the Taylor–Green vortex have been effectively used to demonstrate emulation of high Reynolds-number ( R e ) physical dissipation through numerical convective effects of various non-oscillatory finite-volume algorithms for implicit large eddy simulation (ILES), e.g. using the Godunov-based Eulerian adaptive mesh refinement code xRAGE. The inverse-chevron shock tube experiment simulations have been also used to assess xRAGE based ILES for shock driven turbulent mixing, compared with available simulation and laboratory data. The previous assessments are extended to evaluate new directionally-unsplit high-order algorithms in xRAGE, including a correction to address the well-known issue of excessive numerical diffusion of shock-capturing (e.g., Godunov-type) schemes for low Mach numbers. The unsplit options for hydrodynamics in xRAGE are discussed in detail, followed by fundamental tests with representative shock problems. Basic issues of transition to turbulence and turbulent mixing are discussed, and results of simulations of high- R e turbulent flow and mixing in canonical test cases are reported. Finally, compared to the directional-split cases, and for each grid resolution considered, unsplit results exhibit transition to turbulence with much higher effective R e —and significantly more so with the low Mach number correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Electron acceleration in laboratory-produced turbulent collisionless shocks

Astrophysical collisionless shocks are among the most powerful particle accelerators in the Universe. Generated by violent interactions of supersonic plasma flows with the interstellar medium, supernova remnant shocks are observed to amplify magnetic fields and accelerate electrons and protons to highly relativistic speeds. In the well-established model of diffusive shock acceleration, relativistic particles are accelerated by repeated shock crossings. However, this requires a separate mechanism that pre-accelerates particles to enable shock crossing. This is known as the ‘injection problem’, which is particularly relevant for electrons, and remains one of the most important puzzles in shock acceleration. In most astrophysical shocks, the details of the shock structure cannot be directly resolved, making it challenging to identify the injection mechanism. In this letter we report results from laser-driven plasma flow experiments, and related simulations, that probe the formation of turbulent collisionless shocks in conditions relevant to young supernova remnants. We show that electrons can be effectively accelerated in a first-order Fermi process by small-scale turbulence produced within the shock transition to relativistic non-thermal energies, helping overcome the injection problem. Our observations provide new insight into electron injection at shocks and open the way for controlled laboratory studies of the physics underlying cosmic accelerators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Magnesium abundances in cool metal-polluted white dwarfs

ABSTRACT The accretion of rocky material is responsible for the presence of heavy elements in the atmospheres of a large fraction of white dwarf stars. Those objects represent a unique opportunity to infer the bulk composition of exoplanetesimals. This chemical characterization requires the use of detailed atmosphere models to determine the elemental abundances at the photospheres of white dwarfs. In this work, we use a state-of-the-art model atmosphere code to reanalyse the first large survey of metal-polluted white dwarfs for which abundances are found for multiple elements. We show that the improved constitutive physics of our models lead to systematically higher Mg abundances than previous analyses. We find an average $\log \, {\rm Mg/Ca}$ number abundance ratio of 1.5. This value is significantly above the reference abundance for chondrites, which is expected as current diffusion models predict that for the cool helium-atmosphere white dwarfs of our sample, Mg should remain in the atmosphere longer than Ca. This helps resolve a recently identified Mg depletion problem, where the planetesimals accreted by white dwarfs were reported to be Mg-deficient compared to the expected composition of their planetary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Comparison of RESRAD and GoldSim Models for Assessing Radiological Dose for a RCRA Landfill - 20418

US Ecology Idaho (USEI) operates a Resource Conservation and Recovery Act (RCRA) Subtitle C, Hazardous Waste Treatment, Storage, and Disposal (TSD) Facility on a 640-acre property in Owyhee County, Idaho. USEI accepts a wide variety of RCRA-exempt low activity radioactive wastes, including naturally occurring radioactive material (NORM) and technologically enhanced NORM (TENORM) (USEI 2009). The performance of the landfill with regard to radiological operating permit requirements was previously assessed using a model constructed with the residual radioactivity (RESRAD) computer program, developed by Argonne National Laboratory for the U.S. Department of Energy (DOE). RESRAD supports the evaluation of several environmental transport pathways related to a radionuclide-contaminated soil source term, but it was not conceived as a model for the evaluation of landfill radiological performance. A more comprehensive and realistic model is desired in order to better support submissions to both the State of Idaho's Department of Environmental Quality as well as the U.S. Nuclear Regulatory Commission. A Performance Assessment (PA) computer model was developed using GoldSim software for the USEI RCRA Subtitle C landfill. The RESRAD computer model was specifically developed for calculating soil cleanup criteria and radiological dose and cancer risk from residual radioactive material in soil. To evaluate site-specific conditions, users may select from among a number of available environmental transport and exposure pathways and modify 'default' parameter values. In GoldSim, a user must construct the model 'from scratch.' However, this modeling is facilitated by a number of specialized elements available in GoldSim to support a radiological mass transport model. These include elements for defining radiological decay and ingrowth, container failure and radiological release, advective and diffusive transport, and other processes. Advantages afforded by the use of RESRAD for modeling radiological dose for a RCRA Subtitle C landfill include ease of use and reasonable flexibility in specifying site-specific conditions. GoldSim allows for considerably more flexibility and site-specificity than RESRAD, including evaluation of potentially relevant environmental transport processes not supported in RESRAD. The probabilistic modeling capabilities of GoldSim also far exceed those of RESRAD. The pros and cons of RESRAD and GoldSim for this modeling problem are explored with a focus on identifying approaches and critical factors in identifying the appropriate platform. The capabilities of RESRAD and GoldSim for mathematically modeling the disposal system will be contrasted and compared. For example, the existing RESRAD radiological safety assessment evaluated potentially complete exposure pathways related to infiltration to groundwater and upwards diffusion of radon. Additional transport pathways identified in the USEI Idaho facility Conceptual Site Model include deposition of radon decay products in cover material, root uptake of radionuclides in disposed wastes by native plants, and mixing of cover material by burrowing animals. Results of the two models and how the different transport and dose pathways affect results will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Mathematical Model of Transfer and Deposition of Finely Dispersed Particles in a Turbulent Flow of Emulsions and Suspensions

The problem of modeling turbulent transfer of finely dispersed particles in liquids has been considered. An approach is used where the transport of particles is represented in the form of a variety of the diffusion process with the coefficient of turbulent transfer to the wall. Differential equations of transfer are written for different cases, and a solution of the cell model is obtained for calculating the efficiency of separation in a channel. Based on the theory of turbulent transfer of particles and of the boundary layer model, an expression has been obtained for calculating the rate of turbulent deposition of finely dispersed particles. The application of this expression in determining the efficiency of physical coagulation of emulsions in different channels and on the surface of chaotic packings is shown.

42 ENGINEERING↗
DOE OSTI2019

A space-time adaptive finite element method with exponential time integrator for the phase field model of pitting corrosion

In this paper we propose a space-time adaptive finite element method for the phase field model of pitting corrosion, which is a parabolic partial differential equation system consisting of a phase variable and a concentration variable. A major challenge in solving this phase field model is that the problem is very stiff, which makes the time step size extremely small for standard temporal discretizations. Another difficulty is that a high spatial resolution is required to capture the steep gradients within the diffused interface, which results in very large number of degrees of freedom for uniform meshes. To overcome the stiffness of this model, we combine the Rosenbrock–Euler exponential integrator with Crank–Nicolson scheme for the temporal discretization. Moreover, by exploiting the fact that the speed of the corroding interface decreases with time, we derive an adaptive time stepping formula. For the spatial approximation, we propose a simple and efficient strategy to generate adaptive meshes that reduces the computational cost significantly. Thus, the proposed method utilizes local adaptivity and mesh refinement for efficient simulation of the corrosive dissolution over long times in heterogeneous media with complex microstructures. We also present an extensive set of numerical experiments in both two and three dimensional spaces to demonstrate efficiency and robustness of the proposed method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Coupling TRACE with nodal neutronics code Ants using the Exterior Communications Interface and VTT's multi-physics driver Cerberus

In this paper a recently developed coupling between system code TRACE and nodal neutronics code Ants is presented. The TRAC/RELAP Advanced Computational Engine (TRACE) is a reactor system code developed by U.S. Nuclear Regulatory Commission (NRC). The code has been designed to analyse loss-of-coolant accidents (LOCAs), operational transients and other accident scenarios in light water reactors. Ants is the reduced order nodal neutronics code of the Kraken framework. The diffusion solution method in Ants is based on the Analytic Function Expansion Nodal method (AFEN) and Flux Expansion Nodal Method (FENM). Rectangular, hexagonal and triangular geometries are supported. Both steady state and burnup problems including micro-depletion can be simulated. The coupling is implemented using VTT's multi-physics driver Cerberus and the Exterior Communications Interface (ECI) which is supplied with TRACE and enables the coupling of TRACE without source code modifications. The main focus in the paper is on describing the TRACE-Ants coupling in detail in order to share to other code developers what we have learned from the ECI based coupling process. The coupled code system is briefly tested by simulating a simple transient in a SMR core initiated by control rod movement. The results of the test calculation look reasonable and more complicated test cases will be modelled in the near future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Fourier analysis of continuous fractional diffusion synthetic acceleration schemes in slab geometry

We propose two fractional extensions of continuous diffusion synthetic acceleration (DSA) with fractional derivative order α varying over the interval 2 ≥ α ≥ 1 . We investigate the spectral properties of the corresponding continuous families of fractional preconditioners by performing Fourier analysis for a model infinite homogeneous medium problem in slab geometry. The first family results in a fractional acceleration scheme, FrDSAo, that reduces to traditional DSA for .α = 2 and scattering ratio c limiting to a unit value (c → 1) but is otherwise optimized via the Fourier analysis, to obtain the smallest possible spectral radius, for c < 1 and 2 ≥ α ≥ 1. The second family corresponds to a fractional acceleration scheme, FrDSAs, that reduces to traditional DSA for α = 2 for all values of c. The latter scheme is not optimized but has the advantage of lending itself to a more straightforward implementation. For high values of c, the results of the Fourier analysis point to the existence of an interval 2 > α > ∼1.8 where both FrDSAo and FrDSAs can achieve a lower spectral radius than DSA. For example, DSA has a spectral radius of ∼0.2246 for c = 0.9999 while FrDSAo produces a value of ∼0.1616 at α = 1.92 and FrDSAs results in ∼0.2116 at α =1.93. (author)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Implicit and coupled fluid plasma solver with adaptive Cartesian mesh and its applications to non-equilibrium gas discharges

In this work, we present a new fluid plasma solver with adaptive Cartesian mesh (ACM) based on a full-Newton (nonlinear, implicit) scheme for non-equilibrium gas discharge plasma. The electrons and ions are described using drift-diffusion approximation coupled to Poisson equation for the electric field. The electron-energy transport equation is solved to account for electron thermal conductivity, Joule heating, and energy loss of electrons in collisions with neutral species. The rate of electron-induced ionization is a function of electron temperature and could also depend on electron density (important for plasma stratification). The ion and gas temperature are kept constant. The transport equations are discretized using a non-isothermal Scharfetter-Gummel scheme to resolve possible large temperature gradients in the sheaths. We demonstrate the new solver for simulations of direct current (DC) and radiofrequency (RF) discharges. The implicit treatment of the coupled equations allows using large time steps. The full-Newton method (FNM) enables fast nonlinear convergence at each time step, offering significantly improved simulation efficiency. We discuss the selection of time steps for solving different plasma problems. The new solver enables solving several problems we could not solve before with existing software: two- and three-dimensional structures of the entire DC discharges including cathode and anode regions, electric field reversals and double-layer formation, the normal cathode spot and an anode ring, moving striations in diffuse and constricted DC discharges, and standing striations in RF discharges. The developed FNM-ACM technique offers many benefits for tackling the disparity of gas discharge plasma systems' time scales and nonlinearity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗