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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quark Mass Dependence of Heavy Quark Diffusion Coefficient from Lattice QCD

We present the first study of the quark mass dependence of the heavy quark momentum and spatial diffusion coefficients using lattice QCD with light dynamical quarks corresponding to a pion mass of 320 MeV. We find that, for the temperature range 195 MeV < 𝑇 < 293 MeV, the spatial diffusion coefficients of the charm and bottom quarks are smaller than those obtained in phenomenological models that describe the 𝑝 𝑇 spectra and elliptic flow of open heavy flavor hadrons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Multiscale, mechanistic calculation of the effective silver diffusion coefficient in polycrystalline silicon carbide: application to silver release in AGR-1 TRISO particles

The silicon carbide (SiC) layer in tristructural isotropic (TRISO) fuel particles serves as a barrier to prevent the escape of fission from the fuel kernel. The release of silver (Ag) is a concern due to the long half-life of the 110mAg isotope. In this study, the effective diffusion coefficient of the fission product Ag through the grain boundary (GB) network is calculated using a combination of atomistic and phase-field methods. Atomistic calculations of Ag diffusivity in SiC bulk and GBs are leveraged to develop a mesoscale effective Ag diffusion coefficient (Deff) in SiC. Since GBs serve as pathways for Ag diffusion, Deff is defined as a function of temperature, microstructure variables, and fluence. Deff is implemented in the fuel performance code Bison to predict Ag release from AGR-1 TRISO fuel particles. We hereby quantify the impact of SiC grain size and irradiation on Ag release and improve Bison's predictions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Resolution of a few problems in the application of quasilinear theory to calculating diffusion coefficients in heliophysics [Slides]

Three problems with the historical literature were discussed. Kennel and Engelmann (1966): ‘notational error’ that implies that diffusion coefficients depend on the distribution of wave power with respect to ψ. This dependence is unphysical and is not present in Lyons+ 1971. Kennel and Engelmann (1966) assume that only a single frequency can be associated with each wave vector: not necessarily true for EMIC, e.g. Lyons (1974a, 1974b) incorrectly used the Jacobian to relate the power spectral density in (ω,χ) space to that into (κ ⊥ , κ ∥ space. The correct use of the Jacobian: (1) Eliminates undesirable properties associated with N(ω) : lack of integrability, lack of superposition; and (2) Can produce orders of magnitude changes to the diffusion coefficients, but depends on the wave distribution: bigger effect for more oblique waves, and depends on the plasma parameter: bigger effect for smaller $ω_{pe}/ω_{ce}$.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Correlation and prediction of gaseous diffusion coefficients.

A new correlation method for binary gaseous diffusion coefficients from very low temperatures to 10,000 K is proposed based on an extended principle of corresponding states, and having greater range and accuracy than previous correlations. There are two correlation parameters that are related to other physical quantities and that are predictable in the absence of diffusion measurements. Quantum effects and composition dependence are included, but high-pressure effects are not. The results are directly applicable to multicomponent mixtures.

Marrero, T. R.↗
NASA NTRS1980

Iterative solutions for one-dimensional diffusion with time varying surface composition and composition-dependent diffusion coefficient

Solutions are given for one-dimensional diffusion problems with a time varying surface composition and also a composition dependent diffusion coefficient. The most general solution does not require special mathematical functions to fit the variation in surface composition or D(C). In another solution, a series expansion may be used to fit the time dependent surface concentration. These solutions make use of iterative calculations that converge rapidly and are highly stable. Computer times are much shorter than that required for finite difference calculations and can efficiently make use of interactive graphics terminals. Existing gas carburization data were used to provide an illustration of an iterative approach with a time varying carbon composition at the free surface.

Chow, M.↗
NASA NTRS1977

A determination of the L dependence of the radial diffusion coefficient for protons in Jupiter's inner magnetosphere

In a previous paper (Thomsen et al., 1977), a technique was proposed for estimating the radial diffusion coefficient (n) in the inner magnetosphere of Jupiter from the observations of the sweeping effect of the inner Jovian satellites on the fluxes of the energetic charged particles. The present paper extends this technique to permit the unique identification of the parameters D sub O and n, where the diffusion coefficient is assumed to be of the form D = D sub O L to the nth. The derived value of D sub O depends directly on assumptions regarding the nature and efficiency of the loss mechanism operating on the particles, while the value of n depends only on the assumed width of the loss region. The extended technique is applied to the University of Iowa Pioneer 11 proton data, leading to values of n of about O and D(6) of about 3 x 10 to the -8th (R sub J)-squared/sec, when satellite sweepup losses are assumed to be the only loss operating on the protons. The small value of n is strong evidence that the radial diffusion is driven by ionospheric winds.

Thomsen, M. F.↗
DOE OSTI2024

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO 2 Conversion Reagents in Metal–Organic Frameworks

Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO 2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO 2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

Crack diffusion coefficient - A candidate fracture toughness parameter for short fiber composites

In brittle matrix composites, crack propagation occurs along random trajectories reflecting the heterogeneous nature of the strength field. Considering the crack trajectory as a diffusive process, the 'crack diffusion coefficient' is introduced. From fatigue crack propagation experiments on a set of identical SEN polyester composite specimens, the variance of the crack tip position along the loading axis is found to be a linear function of the effective 'time'. The latter is taken as the effective crack length. The coefficient of proportionality between variance of the crack trajectory and the effective crack length defines the crack diffusion coefficient D which is found in the present study to be 0.165 mm. This parameter reflects the ability of the composite to deviate the crack from the energetically most efficient path and thus links fracture toughness to the microstructure.

Mull, M. A.↗
DOE OSTI2023

Cation–polymer interactions and local heterogeneity determine the relative order of alkali cation diffusion coefficients in PEGDA hydrogels

Current research efforts are focused on endowing polymer membranes with ion–ion selectivity by incorporating ion–polymer interactions into materials to bias the selective partitioning and or diffusivity of one species over another. However, little is known about the impact of such interactions on the mechanisms of ion transport. In this study, we probe the influence of cation–polymer interactions on cation, anion, and salt diffusivity in a model membrane material, poly(ethylene glycol) diacrylate (PEGDA) by modeling concentrated polyethylene oxide solutions via molecular dynamics simulations. These results are compared to published experimental data for LiCl, NaCl, and KCl diffusion in PEGDA. Experimentally, the order of salt and cation diffusion coefficients for LiCl, NaCl, and KCl deviate from the order in aqueous solutions. Here, simulations identify these deviations to arise from cation–polymer coordination in the membrane. Both the fraction of bound cations and the average binding lifetime increases with decreasing cation hydration free energy (moving down the alkali series), leading to different diffusivity trends in the membrane compared to solution. However, to recover the experimentally observed order of diffusivities cations and salt in our simulations, we needed to incorporate membrane heterogeneity explicitly via a polymer charge scaling procedure. Together, our results indicate that cation–polymer interactions, as well as spatial heterogeneity within the membrane, play a critical role in dictating the observed order of alkali cation and salt diffusion coefficients in membranes.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Spatial dependence of the local diffusion coefficient measured upstream of the November 12, 1978 interplanetary traveling shock

Characteristics of wuprathermal particles accelerated by quasi-parallel interplanetary traveling shocks have been generally explained in terms of a first order Fermi mechanism. Such models require diffusive scattering of particles upstream of the shock. This scattering is characterized by a local diffusion coefficient, kappa, which is determined by the local power density of waves in the upstream region. The dependence of the diffusion coefficient of suprathermal upstream protons on distance from the November 12, 1978 interplanetary traveling shock using a different approach is studied. Unlike previous studies this method, which is based on measurements of particle streaming and intensity gradients, does not rely on predictions. The local spatial variations of Kappa upstream of the November 12, 1978 shock have been chosen for study because the characteristics of this quasi-parallel shock have been extensively studied, and also because of its favorable geometry (i.e. B field nearly radial).

Gloeckler, G.↗
DOE OSTI2026

Construction of generalized quasilinear diffusion coefficient using neural networks with physical restrictions

The quasilinear diffusion coefficient (D QL ) derived from our machine learning framework shows comparable trends with the ground truth D QL obtained from GENRAY-CQL3D simulations. Additionally, for the strong absorption cases, the radial current drive profiles generated using the D QL from our model exhibit consistent behavior with those obtained from the original simulation. These findings indicate the potential of our surrogate modeling approach with physical restrictions to replicate key wave–plasma interaction characteristics while reducing computational costs. Traditionally, calculating D QL for wave–particle interactions relies on computationally intensive wave simulations coupled with Fokker–Planck solvers. To address this challenge, we developed a machine learning-based surrogate model with physical restrictions derived from cold plasma theory and bounce-averaged damping effects. First, we establish the propagation domain of Lower Hybrid Waves in the (N∥, ρ) space by identifying the accessibility limit and determining the upper and lower bounds of N∥ using the Potential Power Deposition (PPD) method. Subsequently, leveraging a database constructed using Latin hypercube sampling alongside the underlying physical restrictions (e.g. PPD), machine learning methods including U-Net and Recurrent Neural Networks are employed to design a physics-restricted machine learning framework capable of reconstructing D QL .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1974

A relation between cosmic-ray fluctuations, gradient, and diffusion coefficient

The motion of charged particles in a stochastic magnetic field is considered via a generalized quasi-linear expansion of Liouville's equation. The result is an equation relating cosmic-ray scintillations to particle gradients and to magnetic-field fluctuations (or diffusion coefficient). The resulting theory may be regarded as an example of a fluctuation-dissipation phenomenon, in which the diffusion coefficient plays the role of the dissipative parameter. The resonant interaction between particles and the random interplanetary magnetic field is considered explicitly, and it is shown that observed scintillations of high-energy (about 1 GeV) cosmic rays may be reasonably explained by the model.

Owens, A. J.↗
NASA NTRS1982

Vertical eddy diffusion coefficient from the LANDSAT imagery

Analysis of five stable cases of the smoke plumes that originated in eastern Cabo Frio (22 deg 59'S; 42 deg 02'W), Brazil using LANDSAT imagery is presented for different months and years. From these images the lateral standard deviation (sigma sub y) and the lateral eddy diffusion coefficient (K sub y) are obtained from the formula based on Taylor's theory of diffusion by continuous moment. The rate of kinetic energy dissipation (e) is evaluated from the diffusion parameters sigma sub y and K sub y. Then, the vertical diffusion coefficient (K sub z) is estimated using Weinstock's formulation. These results agree well with the previous experimental values obtained over water surfaces by various workers. Values of e and K sub z show the weaker mixing processes in the marine stable boundary layer. The data sample is apparently to small to include representative active turbulent regions because such regions are so intermittent in time and in space. These results form a data base for use in the development and validation of mesoscale atmospheric diffusion models.

Viswanadham, Y.↗
DOE OSTI2024

Role of Short-Range Order on Diffusion Coefficients in the Li–Mg Alloy

Li–Mg alloys are important because of their beneficial role in fostering uniform plating and stripping of lithium in all-solid-state batteries. The alloy Li x Mg 1–x forms a solid solution on the BCC crystal structure when the lithium content is greater than x ≈ 0.3. The activation barriers of lithium and magnesium exchanges with a vacancy, crucial for substitutional diffusion, are predicted to be exceptionally low and almost identical, with negligible dependence on the alloy composition. The equilibrium vacancy concentration at room temperature is predicted to be very low, and it also remains almost constant with no dependence on Mg content in the alloy (for x Li ≥ 0.5). Nevertheless, both experiments and kinetic Monte Carlo simulations indicate that the tracer diffusion coefficients decrease by almost an order of magnitude with the addition of Mg to the alloy. In this contribution, the crucial role that chemical short-range order plays in affecting the diffusion coefficients is studied. Chemical short-range order is found to increase the effective activation barrier for lithium and magnesium diffusion by making successive atomic hops with vacancies correlated.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Interplanetary diffusion coefficients for cosmic rays

Information on the cosmic-ray diffusion coefficient, kappa, derived from near-earth observations of the solar modulation of galactic electron fluxes and from the near-earth power spectra of the interplanetary magnetic field, has been used to study the heliocentric radial dependence of kappa, and to derive limits on the spatial extent of the solar modulation region. Representing kappa, as a separable function of radius r and rigidity, and assumming kappa(r) proportional to r to the n-th power, we can place a limit on the power law exponent, n not greater than 1.2. The distance of the modulation boundary is a function of n, and, e.g., for n = 0, falls into the range of 6-25 AU.

Cummings, A. C.↗