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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (n e0 , T e0 , I p , B t , R 0 , n ∥︀ , Z e f f , V loop , P LHCD ) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and “lateral” problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. In conclusion, the πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Data-driven efficient network and surveillance-based immunization

Given a contact network and coarse-grained diagnostic information such as electronic Healthcare Reimbursement Claims (eHRC) data, can we develop efficient intervention policies from data to control an epidemic? Immunization is an important problem in multiple areas, especially epidemiology and public health. However, most existing studies rely on assuming prior epidemiological models to develop pre-emptive strategies, which may fail to adapt to the change in new epidemiological patterns and the availability of rich data such as eHRC. In practice, disease spread is usually complicated, hence assuming an underlying model may deviate from true spreading patterns, leading to possibly inaccurate interventions. Additionally, the abundance of health care surveillance data (such as eHRC) makes it possible to study data-driven strategies without too many restrictive assumptions. Therefore, such a data-driven intervention approach can help public-health experts take more practical decisions. In this work, we take into account propagation log and contact networks for controlling propagation. Furthermore, different from previous model-based approaches, our solutions are solely data driven in a sense that we develop immunization strategies directly from the network and eHRC without assuming classical epidemiological models. In particular, we formulate the novel and challenging data-driven immunization problem. To solve it, we first propose an efficient sampling approach to align surveillance data with contact networks, then develop an efficient algorithm with the provably approximate guarantee for immunization. Finally, we show the effectiveness and scalability of our methods via extensive experiments on multiple datasets, and conduct case studies on nation-wide real medical surveillance data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Improvements to a class of hybrid methods for radiation transport: Nyström reconstruction and defect correction methods

In this study, two modifications are introduced for improving the accuracy, versatility, and robustness of a class of hybrid methods for radiation transport. In general, such methods are constructed by splitting the radiative flux into collided and uncollided components to which low- and high-resolution angular approximations are applied, respectively. In this work we focus on discrete ordinates discretizations of high and low order. The first modification we introduce changes the way in which the collided component is mapped into the uncollided component at the end of each time step in a simulation. The new mapping is a Nyström-type reconstruction that is applicable to arbitrary discrete ordinates quadratures, is guaranteed to preserve positivity of the solution provided that all ordinate weights are positive, is significantly more accurate than previous methods, and can be readily extended to other discretizations such as moment methods, finite element methods, and diffusion approximations. The second modification leverages integral deferred correction (IDC) to iteratively correct for the splitting error introduced by the inconsistency in angular discretization between the collided and uncollided components, in addition to improving the accuracy of the low-order temporal error that is treated by traditional IDC methods. Numerical tests in one- and two-dimensional geometries are used to demonstrate the increased accuracy and efficiency of the proposed modifications. It is found that the two techniques combined yield methods with solution accuracy and memory requirements comparable to that of monolithic discrete ordinates methods while reducing runtime by as much as a factor of between two and ten, depending on the problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Composition and Band Structure of the Native Oxide Nanolayer on the Ion Beam Treated Surface of the GaAs Wafer

Detailed information on GaAs oxide properties is important for solving the problem of passivating and dielectric layers in the GaAs-based electronics. The elemental and chemical compositions of the native oxide layer grown on the atomically clean surface of an n-GaAs (100) wafer etched by Ar{sup +} ions have been studied by synchrotron-based photoelectron spectroscopy. It has been revealed that the oxide layer is essentially enriched in the Ga{sub 2}O{sub 3} phase which is known to be a quite good dielectric as compared to As{sub 2}O{sub 3}. The gallium to arsenic ratio reaches the value as high as [Ga]/[As] = 1.5 in the course of oxidation. The Ga-enrichment occurs supposedly due to diffusion away of As released in preferential oxidation of Ga atoms. A band diagram was constructed for the native oxide nanolayer on the n-GaAs wafer. It has been shown that this natural nanostructure has features of a p–n heterojunction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven learning of nonlocal physics from high-fidelity synthetic data

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. Here, we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

42 ENGINEERING↗
DOE OSTI2020

Assessment of the Griffin Reactor Multiphysics Application Using the Empire Micro Reactor Design Concept

In late 2019, INL and ANL agreed to jointly develop the reactor physics code named Griffin based on the integration of the two code suites, MAMMOTH/Rattlesnake (INL) and MC2 - 3/PROTEUS (ANL). Griffin is being developed based on the MOOSE framework and MOOSE quality assurance procedures. This decision was made to be able to allow DOE-NE to efficiently invest funding to this area and to provide effective and timely support for existing and potential users; the latter includes industry and government organizations who are developing various types of advanced reactors in the near and long term. Since MAMMOTH/Rattlesnake has been developed based on the MOOSE framework, the INL/ANL Griffin development team agreed to build Griffin beginning with a merger of MAMMOTH and Rattlesnake into a single code and moving forward by implementing capabilities from the PROTEUS suite into Griffin. Moving forward, both ANL and INL efforts are equally invested in the Griffin project, with management support, to provide an advanced reactor multiphysics tool to assist in reactor design, optimization, and safety analysis. Much work remains in moving Griffin forward to migrate PROTEUS capabilities and to optimize performance to meet user needs. The main objective of this work is to assess the current status of Griffin capabilities in terms of performance and accuracy, to determine priorities for PROTEUS migration, and to identify capabilities and features to improve for supporting the code integration effort. For this assessment, the Empire micro reactor problem that was developed in the ARPA-E MEITNER program was selected as an advance reactor concept of interest to the technical community. The Empire reactor problem was expanded from its original incomplete specification to be a small heat-pipe-cooled micro reactor core with ~113 cm radius and 70 cm in height, composed of 18 fuel assemblies, 12 control drums, and beryllium radial and axial reflectors. In the current model, using 5 cm axial reflectors specified in the original Empire assembly model, more than 10% of neutrons leak axially and through the empty center safety hole, as well as through heat pipe channels in fuel assembly elements that extend through the top reflector region. Several calculation models of the core were defined for systematic assessment, including 2-D and 3-D fuel assemblies and whole cores with cylindrical boundaries. Cross sections were generated using Serpent 2, and meshes were produced using the Argonne mesh tool or the INL neutronics meshing tools combined with CUBIT. Cross sections and meshes were converted to the ISOXML and Exodus formats, respectively, so that Griffin and PROTEUS could use consistent data for solving the reactor problems. With the prepared cross sections and meshes, PROTEUS was run first to ensure that all input data were correctly generated and input options in terms of angle, mesh, and energy group were accurately determined. Comparisons against Serpent 2 solutions were made in terms of eigenvalue and pin power. The same calculations and comparisons were then conducted using Griffin. For the fuel assembly and whole core problems, the PROTEUS eigenvalues agreed well with reference Serpent 2 solutions within 100 and 30 pcm, respectively, and pin power differences relative to Serpent 2 were overall less than 2.2% and RMS 0.8% for the whole core models. This indicated that all input data were properly prepared. Using the same data, Griffin was run selecting the SAAF-CFEM SN solver with Legendre-Gaussian quadrature and NDA and DSA for acceleration. It was found that the SAAF-CFEM solver of Griffin required finer meshes to achieve eigenvalue and pin power solutions in good agreement with Serpent 2, consequently requiring more memory requirement and longer computation time. On the other hand, the SPH-Diffusion 2-D core calculations performed using Griffin were able to recover the exact eigenvalue from the reference Serpent 2 solutions, resulting in a pin-power distribution with an RMS of 0.6% and maximum absolute difference of less than 1.4%. The runtimes for SPH-Diffusion for the 2-D core were less than 3 minutes on 40 cores. During this evolution of this evaluation, many updates were made in Griffin by the Griffin development team of INL (focusing on software updates) and ANL (reviewing and supporting software updates) to complete this assessment. Observations from the code assessment are presented in the conclusion section of this report, followed by a discussion of recommendations for future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Fluid-driven fractures in granular media: Insights from numerical investigations

We investigate the mechanisms of opening-mode fracture initiation in granular media. The study is based on a simulation of grain-scale fluid-grain interactions through a coupled numerical approach in which the discrete element method is used to solve for the mechanics of a solid granular medium, and computational fluid dynamics is used to model fluid flow and drag forces. In this work, we present benchmark problems with analytical solutions and validate this numerical model against experiments on a viscous-drag-driven cavity in the literature. Additional simulation results show fracture initiation mechanisms in a random granular packing subjected to constant boundary stresses and to fluid injection with a localized source. The dimensionless variable $F_s/F_{sk}$ (ratio of seepage force $F_s$ and skeletal force $F_{sk}$) incorporates the impacts of physical properties and injection parameters including fluid viscosity, injection velocity, grain size, and effective stresses, and it has been used as a criterion separating regimes of fluid invasion and drag-driven fracture opening. Our simulation results show that $F_s/F_{sk}$ in combination with $τ_1$ (ratio of diffusion time from hydromechanical coupling and injection time) serves as a prediction of fracture opening within granular packing. We suggest a simple criterion ($F_s/F_{sk}$ > 1 or $τ_1$ > 0.17) that is valid for various types of granular media and injection conditions to determine if fracture opening will occur. Among other applications, this study is useful to predict the initiation and propagation of fractures in natural sediments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Atomic Mixing of Initially Separated Mixtures

We consider the atomic mixing of two materials, initially separated in space, at fixed temperature– pressure conditions [T, p], and zero mass-mean velocity. Each material is composed of a specified set of atomic species whose density corresponds to the specieswise real Equation of State (EOS) $\tilde{&#961}$ i [T, p]. Because there are no spatial gradients in the mean [T, p] state, the only mechanism for mixing is a specieswise nonequilibrium thermal velocity distribution. The recipe for nonequilibrium species mass fluxes is taken directly from Hirschfelder, Curtiss, and Bird, who express the diffusion velocities in terms of gradients in the molar concentrations and pairwise coefficients of diffusion. EOS data from the SESAME library is used for all species. Data for the specieswise self diffusion coefficients is adopted from the Orbital Free Molecular Dynamics (OFMD) simulations of Ticknor et al.; the so-called Darken approximation is used to express the mutual diffusion coefficients of a mixture in the manner used by White et al. A one-dimensional spatial domain is used; with the left half occupied by one multispecies material and the right half occupied by the other, and with zero–gradient boundary conditions at the ends of the domain. Hence the total mass in the domain is a constant. At time t = 0 the materials are separated. Accordingly, spatial gradients at the separation point are infinite and the species fluxes are also infinite. As t → $\textbf{∞}$ the spatial gradients approach zero and a uniform set of species concentrations is obtained; at this point the system is said to be at “thermodynamic equilibrium.” Analytic solution of the problem is not possible in general due to the nonlinear concentration dependence of the species fluxes. Hence we utilize the numerical solution apparatus in Mathematica for obtaining accurate time-dependent solutions for the mixing of various initial species masses for conditions of interest in laser-driven Inertial Confinement Fusion. Concentration profiles and the speed of propagation of the mixing fronts are discussed in light of the nonlinearity of the problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Simulation of Wingtip Vortex Flows with Reynolds-Averaged Navier–Stokes and Scale-Resolving Simulation Methods

The simulation of wingtip vortex flows is reported on with Reynolds-averaged Navier–Stokes (RANS) equations and scale-resolving simulation (SRS) models. These range from turbulent viscosity and Reynolds-stress model (RSM) RANS closures, to hybrid and bridging SRS methods. The aim of the study was threefold: assess the relevance of replicating the experimental flow conditions, evaluate the numerical requisites of each mathematical model, and determine their modeling accuracy in prediction of a representative wingtip vortex flow. The selected problem is the flow around a NACA 0012 wing at 10 deg of angle of attack and Reynolds number of 4.60×10 6 . The results confirm the relevance of reproducing the experimental flow conditions, in particular at the inlet boundary where the flow is not uniform. For this reason, the evaluation of modeling errors using the available measurements requires the specification of the experimental inlet conditions. On the other hand, the quantification of the modeling error indicates that solely RANS–RSM can achieve an accurate representation of the flow dynamics. Whereas the well-recognized limitations of turbulent viscosity RANS closures to deal with solid-body rotation lead to the overprediction of turbulence and consequent rapid diffusion of the wingtip vortex, the predictive use of SRS methods may reveal excessively complex and demanding.

42 ENGINEERING↗
DOE OSTI2024

Reversible Disorder-to-Order Transition Induced by Aqueous Lithiation in Vanadate Electrode Materials

Vanadium-based oxides are intriguing electrode materials in aqueous electrochemical systems owing to their low cost and high theoretical capacity for alkali storage, especially lithium (Li) ions. However, a sequence of phase transformations and irreversible structure distortion upon Li-ion intercalation causes structural instability and has been a lingering problem for vanadium oxide electrodes. Here, in this work, we investigate lithium vanadate (Li–V 3 O 8 ) for aqueous Li-ion intercalation and deintercalation processes. Unlike its crystalline V 2 O 5 polymorph, Li–V 3 O 8 retains monophasic lithiation, which is attributed to its disordered crystalline nature and large interplanar distance. Importantly, we show a unique and reversible sequence of disorder-to-order structural transition induced by the extent of lithiation, which indicates sequential interlayer and intralayer lithiation process, and vice versa in delithiation process, supported by electrokinetic analysis, in situ X-ray diffraction (XRD), and Debye scattering simulations. The absence of distortive phase transitions and multilithiation pathways facilitates Li-ion diffusion across the vanadate electrode materials to improve storage capacity. This work opens a new dimension for vanadium-based disordered oxides, accelerating the development of low-cost, aqueous electrochemical systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Modeling the effect of temperature gradient on moisture and ionic transport in concrete

Most reinforced concrete structures are under the influence of environmental conditions, such as temperature fluctuations, moisture and chloride variations simultaneously. Thus, the moisture transfer in concrete is driven by the moisture gradient as well as the temperature gradient. Similarly, the ionic transfer in concrete is driven by the concentration gradient of the ion as well as the temperature gradient. This paper developed an analytical solution that can be used for the moisture or the chloride transport in concrete considering the effect of temperature gradient. The effect of mass transfer on the thermal conduction was not considered, so the problem is simplified as a one-way coupled problem. The analytical model was validated by using two CASE studies, in which realistic material parameters of concrete were used, the parameters were obtained from experiments in the literature. The comparisons between analytical results and experimental results showed good agreement. It indicated that the present model can successfully characterize the effect of temperature gradient on moisture transfer and the effect of temperature gradient on chloride transport in concrete.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Photocatalytic inactivation of Vibrio fischeri using Fe2O3-TiO2-based nanoparticles

Highlights: • A marine bacterium (Vibrio fisheri) was examined as biofilm forming microorganism. • The inactivation of bacteria was performed in saline and nutrient growth media. • The performance of five TiO{sub 2} based photocatalysts was investigated. • Effective inactivation of bacteria was achieved by visible light driven photocatalysts. Biofouling is a major problem in water membrane processes, especially in seawater reverse osmosis plants. Inactivation of Vibrio fischeri (a well-known marine bacterium forming biofilm) through photocatalysis via visible light was investigated in this work using active Fe{sub 2}O{sub 3}-TiO{sub 2} nanoparticles. Five Fe{sub 2}O{sub 3}-TiO{sub 2} photocatalysts with different weight percentage of Fe{sub 2}O{sub 3} (0–5 wt%) were synthesized using an ultrasonic-assisted co-precipitation method. The photocatalysts were characterized by powder X-ray diffraction (XRD), BET surface area, transmission electron Æ (TEM) plus selected area electron diffraction (SAED) patterns, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX) and diffuse-reflectance spectroscopy (DRS). Based on the design of experiments, the synthesized photocatalysts were tested for inactivation of V. fischeri under visible light irradiation at different temperatures (25–35 °C) and different photocatalyst dosage (0.1–2 g/L). The photocatalytic microbial inactivation experiments were performed in artificial seawater appropriate for growth of the marine bacterium. The results revealed that the highest inactivation efficiency of V. fischeri was achieved when 1 g/L of 2.5 wt% Fe{sub 2}O{sub 3}-TiO{sub 2} were used, at 35 °C. Photocatalytic inactivation of microorganisms using visible light-driven Fe{sub 2}O{sub 3}-TiO{sub 2} photocatalysts, could introduce an innovative green method in pretreatment units of reverse osmosis plants to control the membrane biofouling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impact of Solution Chemistry and Particle Anistropy on the Collective Dynamics of Oriented Aggregation

Although oriented aggregation of nanoparticles is a widely recognized mechanism of crystal growth, the impact of many fundamental parameters, such as crystallographically distinct interfacial structures, solution composition, and particle-shape-specific hydrodynamic characteristics, on the governing mechanisms and assembly kinetics are largely unexplored. In this context, we investigated the structure and dynamics of boehmite aggregation as a function of solution pH and ionic strength. Cryogenic transmission electron microscopy shows that boehmite platelets assemble by oriented attachment on (010) planes. The coagulation rate constants obtained from dynamic light scattering during the early stages of aggregation are found to span seven orders of magnitude and cross both the reaction-limited and diffusion-limited regimes. Combining a simple scaling with calculations for stability ratio and rotational/translational diffusivities of irregular particle shapes, the effects of orientation for irregular-shaped particles on the early stages of aggregation is understood via angular dependence of van der Waals, electrostatic, and hydrodynamic interactions. Using Monte Carlo simulations, we found that a simple geometric parameter, namely the contact area between two attaching platelets, provides physical insight into the unique structural characteristics of the larger aggregates of the irregular-shaped particles, explaining the unusually high fractal dimensions measured in the diffusion-limited aggregation regime.

oriented aggregation, boehmite, attachment, nanopl↗
DOE OSTI2024

Spinbox: tools for many-body quantum systems in a Monte Carlo context

Spinbox is a piece of software that facilitates quantum mechanical calculations relevant to Monte Carlo simulation of atomic nuclei. At the front lines of research on the nuclear many-body problem are a large number of supercomputer-scale simulation codes. These codes produce valuable results but can be hard to understand, especially for those without intimate knowledge of the relevant theoretical methods. Thus, tools that fill pedagogical roles are extremely valuable. Spinbox makes it easy for one to replicate and analyze the computational processes relevant to a Quantum Monte Carlo (QMC) simulation that may be difficult to understand/debug/analyze due to the scale of the corresponding simulation software. Spinbox is written in Python using other state-of-the-art Python modules for numerical calculations. While a number of Python libraries exist that are suited to general quantum many-body calculations, the motivation of Spinbox is quite particular. In Diffusion Monte Carlo methods (DMC, GFMC, AFDMC), the central calculation is the imaginary-time propagation of individual samples of the many-body wavefunction. Although quantum wavefunctions generally must be described by a probability distribution over a basis, DMC imbues particles (within one sample) with classical spatial coordinates. This method is unusual, so other Python packages are typically not set up to do this easily. Furthermore, the software has built-in options for nuclear systems assuming isospin symmetry, which can be set up with other libraries but is a nontrivial process to do so. Features: - numerical representation of samples of the many-body wavefunctions, including tensor-product states (used in AFDMC) - numerical representation of many-body operators, including tensor-product operators: general, spin, imaginary-time propagation, etc. - the correct associated arithmetic and algebra, implemented as class methods - classes for representing realistic nuclear two- and three-body Hamiltonians (e.g. Argonne V18, Illinois NNN) - large-scale parallel integration over random variables, crucial for the AFDMC method My goal is to make this package open source so that anyone may use it and contribute to it, particularly other researchers doing AFDMC calculations

Fox, Jordan↗
DOE OSTI2018

Iron- and aluminium-induced depletion of molybdenum in acidic environments impedes the nitrogen cycle: Mo depletion by Fe/Al precipitation at ORR

Anthropogenic nitrate contamination is a serious problem in many natural environments. Nitrate removal by microbial action is dependent on the metal molybdenum (Mo), which is required by nitrate reductase for denitrification and dissimilatory nitrate reduction to ammonium. The soluble form of Mo, molybdate (MoO 4 2- ), is incorporated into and adsorbed by iron (Fe) and aluminium (Al) (oxy) hydroxide minerals. In this paper, we used Oak Ridge Reservation (ORR) as a model nitrate-contaminated acidic environment to investigate whether the formation of Fe- and Al-precipitates could impede microbial nitrate removal by depleting Mo. We demonstrate that Fe and Al mineral formation that occurs as the pH of acidic synthetic groundwater is increased, decreases soluble Mo to low picomolar concentrations, a process proposed to mimic environmental diffusion of acidic contaminated groundwater. Analysis of ORR sediments revealed recalcitrant Mo in the contaminated core that co-occurred with Fe and Al, consistent with Mo scavenging by Fe/Al precipitates. Nitrate removal by ORR isolate Pseudomonas fluorescens N2A2 is virtually abolished by Fe/Al precipitate-induced Mo depletion. Lastly, the depletion of naturally occurring Mo in nitrate- and Fe/Al-contaminated acidic environments like ORR or acid mine drainage sites has the potential to impede microbial-based nitrate reduction thereby extending the duration of nitrate in the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Prandtl number effects on extreme mixing events in forced stratified turbulence

Relatively strongly stratified turbulent flows tend to self-organise into a ‘layered anisotropic stratified turbulence’ (LAST) regime, characterised by relatively deep and well-mixed density ‘layers’ separated by relatively thin ‘interfaces’ of enhanced density gradient. Understanding the associated mixing dynamics is a central problem in geophysical fluid dynamics. It is challenging to study LAST mixing, as it is associated with Reynolds numbers $Re := UL/\nu \gg 1$ and Froude numbers $Fr :=(2{\rm \pi} U)/(L N) \ll 1$ ( $U$ and $L$ being characteristic velocity and length scales, $\nu$ the kinematic viscosity and $N$ the buoyancy frequency). Since a sufficiently large dynamic range (largely) unaffected by stratification and viscosity is required, it is also necessary for the buoyancy Reynolds number $Re_{b} := \epsilon /(\nu N^{2}) \gg 1$ , where $\epsilon$ is the (appropriately volume-averaged) turbulent kinetic energy dissipation rate. This requirement is exacerbated for oceanically relevant flows, as the Prandtl number $Pr := \nu /\kappa = {O}(10)$ in thermally stratified water (where $\kappa$ is the thermal diffusivity), thus leading (potentially) to even finer density field structures. We report here on four forced fully resolved direct numerical simulations of stratified turbulence at various Froude ( $Fr=0.5, 2$ ) and Prandtl ( $Pr=1, 7$ ) numbers forced so that $Re_{b}=50$ , with resolutions up to $30\,240 \times 30\,240 \times 3780$ . We find that, as $Pr$ increases, emergent ‘interfaces’ become finer and their contribution to bulk mixing characteristics decreases at the expense of the small-scale density structures populating the well-mixed ‘layers’. However, extreme mixing events (as quantified by significantly elevated local destruction rates of buoyancy variance $\chi _0$ ) are always preferentially found in the (statically stable) interfaces, irrespective of the value of $Pr$ .

Mechanics↗
DOE OSTI2024

A fully implicit, asymptotic-preserving, semi-Lagrangian algorithm for the time dependent anisotropic heat transport equation

In this paper, we extend the operator-split asymptotic-preserving, semi-Lagrangian algorithm for time dependent anisotropic heat transport equation proposed in Chacón et al. (2014) [18] to use a fully implicit time integration with backward differentiation formulas. The proposed implicit method can deal with arbitrary heat-transport anisotropy ratios $\mathcal{X}$∥ /$ \mathcal{X}$⟂ $\ggg$ 1 (with $\mathcal{X}$∥, $ \mathcal{X}$⟂ the parallel and perpendicular heat diffusivities, respectively) in complicated magnetic field topologies in an accurate and efficient manner. Further, the implicit algorithm is second-order accurate temporally and demonstrates an accurate treatment at boundary layers (e.g., island separatrices), which was not ensured by the operator-split implementation. The condition number of the resulting algebraic system is independent of the anisotropy ratio, and is inverted with preconditioned GMRES. We propose a simple preconditioner that renders the finite-dimensional linear operator compact, resulting in mesh-independent convergence rates for topologically simple magnetic fields, and convergence rates scaling as ~ (NΔt) 1/4 (with N the total mesh size and Δt the timestep) in topologically complex magnetic-field configurations. We demonstrate the accuracy and performance of the approach with test problems of varying complexity, including an analytically tractable boundary-layer problem in a straight magnetic field, and a topologically complex magnetic field featuring magnetic islands with extreme anisotropy ratios $\mathcal{X}$∥ /$ \mathcal{X}$⟂ = 10 10 ) .

97 MATHEMATICS AND COMPUTING↗