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Results for “Transport phenomena”

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 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Modelling and analysis of salt-convection effect on oxide reduction process for uranium oxides using smoothed particle hydrodynamics

Oxygen ion transport via molten salt continuum is a key kinetic feature of electrochemical reduction process for uranium oxides in molten salt. During the electrolysis, the evolution of oxygen ion concentration field in molten salt continuum appears from porous metallic layer to open bulk salt. Despite the important role affecting process throughput, its effect coupled with controlled salt movement was not adequately investigated. To capture the reactive oxygen ion transport phenomena, a multidimensional reduction model based on smoothed particle hydrodynamics (SPH) was combined with the advection-diffusion model. A pelletized oxide fuel bed capturing interconnected salt continuum is considered as a reactant material geometry, which allows forced convective delivery of molten salt through the reactant materials with a relatively low pressure head. Here, in this study, reduction time of a pelletized-fuel bed was quantified with respect to key design parameters of the reactant material configuration. The simulation results demonstrated the advantage of salt convection which effectively removes oxygen ion products from the reactant materials and consequently accelerates the reduction process. Moreover, the reduction-time correlation of a single pelletized fuel was developed as a function of relevant dimensionless parameters and was extended to the application for a pelletized-fuel bed. Finally, several insights and implications in the use of a pelletized oxide fuel were presented. The developed SPH-based framework enabled multi-physics analysis of governing dynamics in appreciating and screening candidate reactant material configurations in a cost-effective manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Characterization of mechanical discontinuities based on data-driven classification of compressional-wave travel times

Wave propagation and diffusive transport phenomena are influenced by the mechanical discontinuities in material. Here this study shows that certain bulk properties of the network of low-velocity mechanical discontinuities (e.g. air-filled cracks) in a material can be characterized by processing compressional-wave travel times using traditional data-driven classification techniques. To that end, we perform three tasks in chronological order: (1) use the discrete fracture network (DFN) method to create two-dimensional (2D) numerical models of crack-bearing material embedded with various types of low-velocity mechanical discontinuities, (2) use the fast marching method (FMM) to simulate the propagation of the wave/diffusion front from a single source through the 2D crack-bearing material to multiple receivers placed on the boundary of the material, and (3) train 9 data-driven classifiers to characterize the crack-bearing materials (i.e. bulk properties of the network of mechanical discontinuities in the crack-bearing material) by learning from the simulations of travel times detected by multiple receivers placed around the crack-bearing material. The classifiers identified the orientation, spatial distribution, and dispersion of the low-velocity mechanical discontinuities. Voting classifier performs the best among the 9 classifiers. For the characterization of bulk dispersion and distribution of discontinuities, the sensors located on the adjacent boundaries are more important; whereas for the characterization of bulk orientation of discontinuities, the sensors located on the opposite side are more important.

42 ENGINEERING↗

DOE OSTI2021

Magnetism and topological Hall effect in antiferromagnetic Ru 2 MnSn-based Heusler compounds

Heusler compounds and alloys based on them are of great recent interest because they exhibit a wide variety of spin structures, magnetic properties, and electron-transport phenomena. Their properties are tunable by alloying and we have investigated L2 1 -orderd compound Ru 2 MnSn and its alloys by varying the atomic Mn:Sn composition. While antiferromagnetic ordering with a Néel temperature of 361 K was observed in Ru 2 MnSn, the Mn-poor Ru 2 Mn 0.8 Sn 1.2 alloy exhibits properties of a diluted antiferromagnet in which there are localized regions of uncompensated Mn spins. Furthermore, a noncoplanar spin structure, evident from a topological Hall-effect contribution to the room-temperature Hall resistivity, is realized in Ru 2 Mn 0.8 Sn 1.2 . Finally, our combined experimental and theoretical analysis shows that in the Ru 2 Mn 0.8 Sn 1.2 alloy, the magnetic properties can be explained in terms of a noncoplanar antiferromagnetic scissor mode, which creates a small net magnetization in a magnetic field and subsequently yields a Berry curvature with a strong topological Hall effect.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multiscale study of helium diffusion in Ni-Cr alloys: Short-range trapping versus long-range channeling

Ni-Cr alloys are widely employed as structural materials in fast nuclear reactors but are vulnerable to high-temperature helium (He) embrittlement (HTHE) under fast neutron irradiation. A comprehensive understanding of He diffusion in Ni-Cr alloys, which governs the kinetics of HTHE, is therefore essential for developing resilient materials and preventing failure. In this work, we reveal the underlying mechanisms of He diffusion in pure Ni and Ni-Cr alloys by integrating density functional theory (DFT) with atomic kinetic Monte Carlo (AKMC) simulations. Our findings uncover a non-monotonic dependence of He diffusivity on Cr concentration, contradicting the monotonic trends predicted by DFT-parameterized theories. At low Cr concentrations, He diffusion is dominated by short-range trapping, characterized by multiple trapping sites and a distinct mechanism within the first nearest neighbor of Cr, differing from that in pure Ni. At high Cr concentrations, these local traps become interconnected, forming long-range fast diffusion channels that enhance He mobility. The competition between localized trapping and extended channeling results in a diffusivity that first decreases, then increases with rising Cr content. These atomic-scale insights offer critical guidance for the design of radiation-tolerant Ni-based alloys. Moreover, the combined DFT-AKMC methodology and the concept of random walker diffusion through interconnected energy basins present a broadly applicable framework for studying transport phenomena in disordered systems.

36 - MATERIALS SCIENCE↗

DOE OSTI2020

Chirality transfer and chiral turbulence in gauge theories

Chirality transfer between fermions and gauge fields plays a crucial role for understanding the dynamics of anomalous transport phenomena such as the Chiral Magnetic Effect. In this proceeding we present a first principles study of these processes based on classical-statistical real-time lattice simulations of strongly coupled QED (e 2 N f = 64). Furthermore, our simulations demonstrate that a chirality imbalance in the fermion sector triggers chiral plasma instabilities in the gaugefield sector, which ultimately lead to the generation of long range helical magnetic fields via an self-similar turbulent cascade of the magnetic helicity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Tritium generation, release, and retention from in-core fluoride salt irradiations

Further understanding of tritium transport mechanisms in the combined molten fluoride salt and graphite environment is necessary for the design and licensing of a Fluoride-Salt-Cooled High-Temperature Reactor (FHR). The three in-core fluoride salt irradiations completed at the Massachusetts Institute of Technology Reactor (MITR) are a useful parallel for studying transport phenomena expected in a FHR environment. During the irradiations, evolution of tritium from the flibe salt was monitored and compared to the calculated total generation rate. A difference of 22 ± 10% between the integrated calculated tritium generation rate and the total release was measured for the third MITR irradiation (FS-3). The fraction of tritium which was not released from the salt could be explained by tritium retention in graphite. Additionally, for post irradiation examination, a thermal desorption furnace was used to heat nuclear graphite samples in order to release and measure retained tritium. The desorption analysis in this work utilized seven subsections of graphite from the second salt irradiation (FS-2); three from a disc of IG-110U and four from ARB matrix graphite. Observed desorption versus temperature as well as total tritium content in the samples after irradiation indicate that the graphites were not volumetrically saturated with tritium, but rather tritium retention was likely limited to the near-surface region. Measurements of the samples resulted in 2.90 ± 0.29 μCi/mm 2 of tritium retained by IG-110U and 1.83 ± 0.31 μCi/mm 2 for ARB during the 300 h FS-2 in-core irradiation. Based on the desorption measurements, the estimated total tritium retention in graphite from the FS-2 samples is consistent with the tritium release measurements from the FS-3 experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Bridging Scales in Bioenergy and Catalysis: A Review of Mesoscale Modeling Applications, Methods, and Future Directions

Between the molecular and reactor scales, which are familiar to the chemical engineering community, lies an intermediate regime, here termed the “mesoscale,” where transport phenomena and reaction kinetics compete on similar time scales. Bioenergy and catalytic processes offer particularly important examples of mesoscale phenomena owing to their multiphase nature and the complex, highly variable porosity characteristic of biomass and many structured catalysts. In this review, we overview applications and methods central to mesoscale modeling as they apply to reaction engineering of biomass conversion and catalytic processing. A brief historical perspective is offered to put recent advances in context. Applications of mesoscale modeling are described, and several specific examples from biomass pyrolysis and catalytic upgrading of bioderived intermediates are highlighted. Methods including reduced order modeling, finite element and finite volume approaches, geometry construction and import, and visualization of simulation results are described; in each category, recent advances, current limitations, and areas for future development are presented. Owing to improved access to high-performance computational resources, advances in algorithm development, and sustained interest in reaction engineering to sustainably meet societal needs, we conclude that a significant upsurge in mesoscale modeling capabilities is on the horizon that will accelerate design, deployment, and optimization of new bioenergy and catalytic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Spin-Dependent Thermoelectric Power of Nanoislands

The Seebeck effect explains the generation of electric voltage as a result of a temperature gradient. Its efficiency, defined as the ratio of the generated electric voltage to the temperature difference, is sensitive to local inhomogeneities that alter the scattering rate and the density of the conduction electrons. Spin-polarized Seebeck tunneling generates a distinct thermovoltage in spin-up and spin-down charge transport channels, which, as a key to spin caloritronics, focuses on transport phenomena related to spin and heat. Here, we report spatially resolved measurement of the spin-dependent thermovoltage in a tunneling junction formed by ferromagnetic Co nanoislands and a Ni tip using spin-dependent scanning tunneling thermovoltage microscopy (SP-STVthM). We resolve the nanoscale thermoelectric powers with respect to spin polarization, nanoisland size, stacking order of Co layers on a Cu substrate, and local sample heterogeneities. The observed thermally generated spin voltages are supported by first-principles and model calculations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Strong Correlation Between Superconductivity and Ferromagnetism in an Fe-Chalcogenide Superconductor

The interplay among topology, superconductivity, and magnetism promises to bring a plethora of exotic and unintuitive behaviors in emergent quantum materials. The family of Fe-chalcogenide superconductors FeTe x Se 1–x are directly relevant in this context due to their intrinsic topological band structure, high-temperature superconductivity, and unconventional pairing symmetry. Despite enormous promise and expectation, the local magnetic properties of FeTe x Se 1–x remain largely unexplored, which prevents a comprehensive understanding of their underlying material properties. Exploiting nitrogen vacancy (NV) centers in diamond, here we report nanoscale quantum sensing and imaging of magnetic flux generated by exfoliated FeTe x Se 1–x flakes, demonstrating strong correlation between superconductivity and ferromagnetism in FeTe x Se 1–x . The coexistence of superconductivity and ferromagnetism in an established topological superconductor opens up new opportunities for exploring exotic spin and charge transport phenomena in quantum materials. The demonstrated coupling between NV centers and FeTe x Se 1–x may also find applications in developing hybrid architectures for next-generation, solid-state-based quantum information technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Effect of Formation Heterogeneity on CO 2 Dissolution in Subsurface Porous Media

Dissolution trapping is one of the most dominant mechanisms for the secure storage of CO 2 injected in porous subsurface formations saturated with brine. This trapping mechanism is enhanced by convective mixing, which occurs due to the gravitational instability between the different fluid layers in the aquifer. The reservoir permeability also plays a crucial role in the dissolution rate and overall fluid flow dynamics during the density-driven convection in porous media. Herein this study investigates the role of complex heterogeneity, i.e., irregular permeability distribution in CO 2 dissolution, using a novel experimental approach to create medium permeability heterogeneity in Hele–Shaw cells. Complex subsurface transport phenomena such as a preferential dissolution path, CO 2 sweep efficiency, changes in finger morphology, and CO 2 concentration distribution are visualized by creating heterogeneous media. Experimental results showed that reservoir permeability heterogeneity causes significant channeling effects and poor sweep efficiency. A scaling relationship between average finger growth rate (Gr) and permeability (k) was obtained as Gr [m s –1 ] = 266.8k [m 2 ] + 1.20 × 10 –6 . Furthermore, the mass of CO 2 dissolved is calculated using the spectrophotometric method to characterize the convective instability. The convective flux was analyzed by comparing the experimental dissolution flux with the theoretical diffusion flux, calculating a maximum Sherwood number of 6.8. The study’s findings improve the current understanding of the CO 2 convection morphology in heterogeneous media, allowing better assessment of long-term CO 2 storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Light-Matter Interaction in Ultrastable Tunneling Nanogaps

Light emission and detection through tunnel junctions have emerged as a promising platform for studying nanoscale light–matter interactions, including electroluminescence and photoassisted transport. However, controlling these interactions in the tunneling regime has been challenging due to complex underlying mechanisms that remain poorly understood. A major obstacle is the difficulty in forming stable junctions that can function reliably over extended periods. In this study, we fabricate ultrastable tunneling junctions consisting of epitaxial indium–tin-oxide, epitaxial lutetium oxide, and gold. With their stable and consistent tunneling currents, we investigate photon-assisted transport phenomena using simple direct-current detection. Our results demonstrate that optical rectification is the primary contributor to the laser-induced current, alongside thermal effects and hot-electron currents. Furthermore, owing to their epitaxial nature and high breakdown threshold, this ultrastable platform holds promise for future real-world applications, including nanoscale light sources and multifunctional photodetectors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations

Advection-dispersion equations (ADEs) are commonly used to describe transport phenomena in porous media. Even though mature discretization-based numerical methods for ADEs exist, some challenges still remain, especially when it comes to solving advection-dominated forward ADEs and diffusion-dominated backward ADEs. The latter problem usually arises in the source identification context and leads to numerically unstable grid-based solutions that require a form of regularization or should be treated as an inverse problem that is computationally more expensive because it requires solving the forward problem multiple times. In this study, we propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled ADE and Darcy flow equations with space-dependent hydraulic conductivity. In this approach, the hydraulic conductivity, hydraulic head, and concentration fields are approximated with deep neural networks (DNNs). We assume that the conductivity field is given by its values on a grid, and we use these values to train the conductivity DNN. The head and concentration DNNs are trained by minimizing the residuals of the flow equation and ADE and using the initial and boundary conditions as additional constraints. The PINN method is applied to one- and two-dimensional forward ADE problems, where its performance for various P\'{e}clet numbers ($Pe$) is compared with the analytical and numerical solutions. We find that the PINN method is accurate with errors of less than 1\% and outperforms some conventional discretization-based methods for $Pe$ larger that 100. Next, we demonstrate that the PINN method remains accurate for the backward ADEs, with the relative errors in most cases staying under 5\% compared to the reference concentration field. Finally, we show that when available, the concentration measurements can be easily incorporated in the PINN method and significantly improve (by more than 50\% in the considered cases) the accuracy of the PINN solution of the backward ADE.

He, Qizhi↗

DOE OSTI2021

Sondheimer oscillations as a probe of non-ohmic flow in WP 2 crystals

As conductors in electronic applications shrink, microscopic conduction processes lead to strong deviations from Ohm’s law. Depending on the length scales of momentum conserving (l MC ) and relaxing (l MR ) electron scattering, and the device size (d), current flows may shift from ohmic to ballistic to hydrodynamic regimes. So far, an in situ methodology to obtain these parameters within a micro/nanodevice is critically lacking. In this context, we exploit Sondheimer oscillations, semi-classical magnetoresistance oscillations due to helical electronic motion, as a method to obtain l MR even when l MR $\gg$ d. We extract l MR from the Sondheimer amplitude in WP 2 , at temperatures up to T ~ 40 K, a range most relevant for hydrodynamic transport phenomena. Our data on μm-sized devices are in excellent agreement with experimental reports of the bulk l MR and confirm that WP 2 can be microfabricated without degradation. These results conclusively establish Sondheimer oscillations as a quantitative probe of l MR in micro-devices.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Cooperative and inhibitory ion transport in functionalized angstrom-scale two-dimensional channels

Significant success has been achieved in fabricating angstrom-scale artificial solid ionic channels aiming to replicate the biological ion channels (BICs). Besides high selectivity, BICs also exhibit sophisticated ion gating and interplay. However, such behavior and functionality are seldomly recreated in the artificial counterparts due to the insufficient understanding of the molecular origin. Here we report cooperative and inhibitory ion transport in angstrom-scale acetate functionalized MoS 2 two-dimensional channels. For cooperative ion transport, the permeability of K + is doubled in the presence of only 1% Pb 2+ (versus K + by molarity), while the permeability of Pb 2+ is independent of K + . Molecular dynamics simulations reveal complex interplay among K + , Pb 2+ , and the anions in governing the cooperativity, such that Pb 2+ ions capture and slow down the anions via long-range interaction, which leads to the synchronization of anions with K + to transport as ion pairs with reduced interaction with the channel surface. For inhibitory ion transport, divalent Co 2+ (or Ba 2+ ) and Pb 2+ can replace each other in the confined channel and compete for the limited transport cross section. Our work reveals ion transport phenomena in extreme confinement and highlights the potential of manipulating ion interplay in confinement for achieving advanced functionalities.

Functionalized Angstrom↗

DOE OSTI2023

Pseudogap behavior in charge density wave kagome material ScV 6 Sn 6 revealed by magnetotransport measurements

Over the last few years, significant attention has been devoted to studying the kagome materials A V 3 Sb 5 (A = K, Rb, Cs) due to their unconventional superconductivity and charge density wave (CDW) ordering. Recently $ScV_{6}Sn_{6}$ was found to host a CDW below ≈ 90 K, and, like A V 3 Sb 5 , it contains a kagome lattice comprised only of V ions. Here we present a comprehensive magnetotransport study on $ScV_{6}Sn_{6}$. We discovered several anomalous transport phenomena above the CDW ordering temperature, including insulating behavior in interlayer resistivity, a strongly temperature-dependent Hall coefficient, and a violation of Kohler’s rule. All these anomalies can be consistently explained by a progressive decrease in carrier densities with decreasing temperature, suggesting the formation of a pseudogap. Our findings suggest that high-temperature CDW fluctuations play a significant role in determining the normal state electronic properties of $ScV_{6}Sn_{6}$.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Sign-tunable anomalous Hall effect induced by two-dimensional symmetry-protected nodal structures in ferromagnetic perovskite thin films

Magnetism and spin-orbit coupling are two quintessential ingredients underlying topological transport phenomena in itinerant ferromagnets. When spin-polarized bands support nodal points/lines with band degeneracy that can be lifted by spin-orbit coupling, the nodal structures become a source of Berry curvature, leading to a large anomalous Hall effect. However, two-dimensional systems can possess stable nodal structures only when proper crystalline symmetry exists. Here, in this study, we show that two-dimensional spin-polarized band structures of perovskite oxides generally support symmetry-protected nodal lines and points that govern both the sign and the magnitude of the anomalous Hall effect. To demonstrate this, we performed angle-resolved photoemission studies of ultrathin films of SrRuO 3 , a representative metallic ferromagnet with spin-orbit coupling. We show that the sign-changing anomalous Hall effect upon variation in the film thickness, magnetization and chemical potential can be well explained by theoretical models. Our work may facilitate new switchable devices based on ferromagnetic ultrathin films.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Predicting 3D moisture sorption behavior of materials from 1D investigations

Abstract Moisture in materials can be a source of future outgassing and exacerbate unwanted changes in physical and chemical properties. Here, we investigate the effect of sample size and shape on the moisture transport phenomena through a combined experimental and modeling approach. Several different materials varying in size and shape were investigated over a wide range of relative humidities (0–90%) and temperatures ( $$30{-}70^{\,\circ } \hbox {C}$$ 30 - 70 ∘ C ) using gravimetric type dynamic vapor sorption (DVS). A dynamic triple-mode sorption model, developed previously, was employed to describe the experimental results with good success; the model includes absorption, adsorption, pooling (clustering) of species, and molecular diffusion. Here we show that the full triple-mode sorption model is robust enough to predict the dynamic uptake and outgassing of 3-dimensional (3D) samples using parameters derived from quasi-1D samples. This successful demonstration on three different materials (filled polydimethylsiloxane (PDMS), unfilled PDMS, and ceramic inorganic composite) illustrates that the model is robust at describing the scale-independent physics and chemistry of moisture sorption and diffusion materials. This work demonstrates that while sorption mechanisms manifest in testing of all sample sizes, some of these mechanisms were so subtle that they were overlooked in our initial modeling and assessment, illustrating the importance of multi-scale experiments in the development of robust predictive capabilities. Our study also outlines the challenges and viable solutions for global optimization of a multi-parameter model. The ability to quantify moisture sorption and diffusion, independent of scale, using 1D lab-scale experiments enables prediction of long-term bulk materials behavior in real applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗