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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A combined kick-out and dissociative diffusion mechanism of grown-in Be in InGaAs and InGaAsP. A new finite difference-Bairstow method for solution of the diffusion equations

Experimental results on the diffusion of grown-in beryllium (Be) in indium gallium arsenide (In{sub 0.53}Ga{sub 0.47}As) and indium gallium arsenide phosphide (In{sub 0.73}Ga{sub 0.27}As{sub 0.58}P{sub 0.42}) gas source molecular beam epitaxy alloys lattice-matched to indium phosphide (InP) can be successfully explained in terms of a combined kick-out and dissociative diffusion mechanism, involving neutral Be interstitials (Be{sub i}{sup 0}), singly positively charged gallium (Ga), indium (In) self-interstitials (I{sub III}{sup +}) and singly positively charged Ga, In vacancies (V{sub III}{sup +}). A new numerical method of solution to the system of diffusion equations, based on the finite difference approximations and Bairstow's method, is proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Determination of the thermal diffusivity of bulk and layered samples by time domain thermoreflectance: Interest of lateral heat diffusion investigation in nanoscale time range

We report on thermal investigations performed in a time resolved experimental scheme. The time domain thermoreflectance (TDTR) is applied in an unusual geometry where the pump and probe beams are not superimposed but focused and shifted. In this way, the determination of the in-plane thermal diffusivity is achieved from temperature snapshots at different time delays. In the first part, taking into account the specific generation process and the detection inherent to the time domain thermoreflectance approach, an analytical solution for the temperature field is obtained for bulk samples, and compared to experimental data. A comparison with the frequency domain thermoreflectance microscopy is also outlined. In Part II section, the lateral heat diffusion in a layered structure is investigated. The comparison of the heat diffusion spreading in case of a highly conductive layer deposited on an insulator substrate and the reverse situation are carefully studied. Finally, we show how the time dependence is efficient to probe and identify material thermal properties or thermal interfacial resistance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A localized artificial diffusivity method to simulate compressible multiphase flows using the stiffened gas equation of state: A localized artificial diffusivity method to simulate compressible multiphase flows using the stiffened gas equation of state

The development of numerical approaches to perform direct numerical simulations of compressible multiphase flows has been an active field of research for several years. Proper treatment of fluid interfaces is crucial as important physics occur in this infinitesimally small region. Furthermore, the compressibility of the fluid requires proper treatment of discontinuities. Artificial diffusivity is among a number of methods widely used for compressible flows. This study develops a general form of consistent artificial diffusion fluxes and extends the localized artificial diffusivity method for high-order central schemes to solve multiphase flows with an interface-capturing method. These fluxes ensure an oscillation-free interface for pressure, velocity, and temperature without employing a sharpening technique. Moreover, the high-order representation of all scales in the flow helps capture the wide range of instabilities inherent in these flows. The goal is to develop an approach capable of performing high-fidelity simulations supported by physics-driven validation. lastly, this is achieved by solving the five-equation model with the stiffened-gas equation of state using the proposed method for multicomponent and multiphase flows on a variety of 1D and 2D problems.

42 ENGINEERING↗
DOE OSTI2017

Stability analysis of nonlinear two-grid method for multigroup neutron diffusion problems

We present theoretical analysis of a nonlinear acceleration method for solving multigroup neutron diffusion problems. This method is formulated with two energy grids that are defined by (i) fine-energy groups structure and (ii) coarse grid with just a single energy group. The coarse-grid equations are derived by averaging of the multigroup diffusion equations over energy. The method uses a nonlinear prolongation operator. We perform stability analysis of iteration algorithms for inhomogeneous (fixed-source) and eigenvalue neutron diffusion problems. To apply Fourier analysis the equations of the method are linearized about solutions of infinite-medium problems. The developed analysis enables us to predict convergence properties of this two-grid method in different types of problems. Numerical results of problems in 2D Cartesian geometry are presented to confirm theoretical predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Quantum Effects on H 2 Diffusion in Zeolite RHO: Inverse Kinetic Isotope Effect for Sieving

We use canonical variational theory (CVT) with small-curvature tunneling (SCT) contributions to investigate quantum effects on the H 2 diffusion process in the pure-silica zeolite RHO. At low temperature we find an inverse kinetic isotopic sieving effect in that the heavier isotopic species diffuses faster than the lighter one. Three quantum effects contribute to this kinetic isotope effect (KIE). The first one is quantum mechanical tunneling; this—on its own—would lead to a normal kinetic isotopic sieving effect, in which lighter diprotium diffuses faster than dideuterium. The second factor, which we find to dominate in the present case, is zero-point energy (ZPE). Deuterium has a lower ZPE, which leads to a smaller effective barrier for tunneling because the transition state has a larger ZPE than the precursor stable state; this results in an inverse KIE. The third factor, the thermal vibrational energy (computed from the quantized vibrational partition function), also favors a normal KIE, but it is outweighed by the ZPE effect. The vibrations of the zeolite host framework are found to play an important role at low temperatures, and our calculations consider up to 7296 degrees of freedom at the equilibrium structure and the saddle point and up to 221 degrees of freedom along the reaction path. In conclusion, the importance of quantum considerations on the dynamics is elucidated by comparison to a purely classical treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Systematic characterization of protein folding pathways using diffusion maps: Application to Trp-cage miniprotein

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories in a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Growth rate of crystalline ice and the diffusivity of supercooled water from 126 to 262 K

Understanding deeply supercooled water is key to unraveling many of water’s anomalous properties. However, this has proven difficult due to rapid and uncontrolled crystallization. Using a pulsed laser heating technique, we measure the growth rate of crystalline ice, G(T), for 180 K < T < 262 K, i.e. deep within water’s “no man’s land.” The self-diffusion of supercooled liquid water, D(T), is obtained from G(T) using the Wilson-Frenkel model of crystal growth. For T > 237 K, G(T) and D(T) have super-Arrhenius (“fragile”) temperature dependences, but both crossover to Arrhenius (“strong”) behavior with a large activation energy in “no man’s land.” The fact that G(T) and D(T) are smoothly varying rules out the hypothesis that liquid water’s properties have a singularity at or near 228 K. However the results are consistent with a previous prediction for D(T) that assumed no thermodynamic transitions occur in “no man’s land.

Xu, Yuntao↗
DOE OSTI2014

Dynamic implicit 3D adaptive mesh refinement for non-equilibrium radiation diffusion

The time dependent non-equilibrium radiation diffusion equations are important for solving the transport of energy through radiation in optically thick regimes and find applications in several fields including astrophysics and inertial confinement fusion. The associated initial boundary value problems that are encountered often exhibit a wide range of scales in space and time and are extremely challenging to solve. To efficiently and accurately simulate these systems we describe our research on combining techniques that will also find use more broadly for long term time integration of nonlinear multi-physics systems: implicit time integration for efficient long term time integration of stiff multi-physics systems, local control theory based step size control to minimize the required global number of time steps while controlling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton–Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

In Operando Analysis of Diffusion in Porous Metal‐Organic Framework Catalysts

Abstract The potential to exert atomistic control over the structure of site‐isolated catalyst sites, as well as the topology and chemical environment of interstitial pore spaces, has inspired efforts to apply porous metal‐organic frameworks (MOFs) as catalysts for fine chemical synthesis. In analogy to enzyme‐catalyzed reactions, MOF catalysts have been proposed as platforms in which substrate confinement could be used to achieve chemo‐ and stereoselectivities that are orthogonal to solution‐phase catalysts. In order to leverage the tunable pore topology of MOFs to impact catalyst selectivity, catalysis must proceed at interstitial catalyst sites, rather than at solvent‐exposed interfacial sites. This Minireview addresses challenges inherent to interstitial MOF catalysis by 1) describing the diffusional processes available to sorbates in porous materials, 2) discussing critical factors that impact the diffusion rate of substrates in porous materials, and 3) presenting in operando experimental strategies to assess the relative rates of substrate diffusion and catalyst turnover in MOF catalysis. It is anticipated that the continued development of in operando tools to evaluate substrate diffusion in porous catalysts will advance the application of these materials in fine chemical synthesis.

Gao, Wen‐Yang↗
DOE OSTI2018

Short Diffusion Time Diffusion-Weighted Imaging With Oscillating Gradient Preparation as an Early Magnetic Resonance Imaging Biomarker for Radiation Therapy Response Monitoring in Glioblastoma: A Preclinical Feasibility Study

To investigate a novel alternative diffusion-weighted imaging (DWI) approach using oscillating gradients preparation (OGSE) to obtain much shorter effective diffusion times (Δ{sub eff}) for tumor response monitoring by apparent diffusion coefficient (ADC) mapping in a glioblastoma mouse model.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Irradiation-enhanced diffusion and diffusion-limited creep in U 3 Si 2

U 3 Si 2 is an advanced fuel candidate due to its relatively high fissile density and attractive thermal properties. Compared to standard UO 2 fuel, there are significant data gaps for the thermophysical and thermomechanical properties of U 3 Si 2 . Point defect concentrations and mobilities under irradiation govern a number of important fuel performance properties, such as creep and fission gas release. In this work, we utilized density functional theory (DFT) data to inform a cluster dynamics framework to predict point defect concentrations in U 3 Si 2 under irradiation. Molecular dynamics (MD) simulations were used to examine the contribution of atomic mixing during ballistic cascades to diffusion, as well as the diffusivity of U and Si at grain boundaries. These atomic scale models for diffusivity were then used to inform a creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusional creep, and climb-limited dislocation creep. The model compares well against available experimental data and has been implemented in the BISON fuel performance code. Finally, a demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U 3 Si 2 in-reactor, a consequence of its high thermal conductivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗