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DOE OSTI2021

A sharp interface Lagrangian-Eulerian method for rigid-body fluid-structure interaction

Herein this paper introduces a sharp interface method to simulate fluid-structure interaction (FSI) involving rigid bodies immersed in viscous incompressible fluids. The capabilities of this methodology are benchmarked using a range of test cases and demonstrated using large-scale models of biomedical FSI. The numerical approach developed herein, which we refer to as an immersed Lagrangian-Eulerian (ILE) method, integrates aspects of partitioned and immersed FSI formulations by solving separate momentum equations for the fluid and solid subdomains, as in a partitioned formulation, while also using non-conforming discretizations of the dynamic fluid and structure regions, as in an immersed formulation. A simple Dirichlet-Neumann coupling scheme is used, in which the motion of the immersed solid is driven by fluid traction forces evaluated along the fluid-structure interface, and the motion of the fluid along that interface is constrained to match the solid velocity and thereby satisfy the no-slip condition. To develop a practical numerical method, we adopt a penalty approach that approximately imposes the no-slip condition along the fluid-structure interface. In the coupling strategy, a separate discretization of the fluid-structure interface is tethered to the volumetric solid mesh via stiff spring-like penalty forces. Our fluid-structure coupling scheme relies on an immersed interface method (IIM) for discrete geometries, which enables the accurate determination of both velocities and stresses along complex internal interfaces. Numerical methods for FSI can suffer from instabilities related to the added mass effect, but computational tests indicate that the methodology introduced here remains stable for selected test cases across a broad range of solid-fluid mass density ratios, including extremely small, nearly equal, equal, and large density ratios. Biomedical FSI demonstration cases include results obtained using this method to simulate the dynamics of a bileaflet mechanical heart valve in a pulse duplicator, and to model transport of blood clots in a patient-averaged anatomical model of the inferior vena cava.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

High pressure ammonia/methanol oxidation up to 100 atm

Here, high pressure ammonia/methanol oxidation and NOx formations were investigated using a recently developed supercritical pressure jet-stirred reactor (SP-JSR) at 20 and 100 atm with temperatures between 550 and 950 K and equivalence ratios of 0.138 and 1.15. The experimental results show that NH 3 oxidation at high pressure is significantly accelerated by the active OH radicals produced from CH 3 OH oxidation. Furthermore, the kinetic interactions between NH 3 and CH 3 OH are governed mainly by the reactions CH 3 OH + NH 2 = CH 2 OH + NH 3 , CH 3 OH + NH 2 = CH 3 O + NH 3 , and CH 2 O + NH 2 = HCO + NH 3 . A HP-Mech model for high-pressure NH 3 /CH 3 OH oxidation was developed in this study. It consists of the most recent NH 3 and CH 3 OH models including some new reactions and updated rate constants from the literature as well as NH 3 -CH 3 OH interactions where rate constants of CH 3 OH + NH 2 = CH 2 OH + NH 3 , CH 3 OH + NH 2 = CH 3 O + NH 3 , NH 2 + CH 2 O = NH 3 + HCO, and NH 2 + CH 2 O = NH 2 CHO + H were theoretically calculated in this study. Our model with these updates improves the prediction for the measured N 2 O/NO x temperature dependence at 100 atm. In addition, the reaction pathway and sensitivity analysis show that N 2 O/NO x /HONO interactions with HO 2 are very important, especially for a fuel-lean mixture at 100 atm. The HONO mole fraction for the fuel-lean mixture at 100 atm was then measured by off-axis integrated cavity output spectroscopy (ICOS) at wavenumber of 6638.26 cm -1 . The experimental data show a significant HONO formation at intermediate temperature that is strongly underpredicted by numerical simulation at 100 atm. Therefore, the HONO related reactions with notable uncertainty at high pressure such as NO + OH (+M) = HONO (+M) and H 2 NO + NO 2 = HONO + HNO need deeper exploration in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Vibrational spectroscopy of uranium tetrafluoride hydrates

Uranium tetrafluoride (UF 4 ) is an important intermediate in the production of UF6 and nuclear fuel. Historical characterization of UF 4 with Raman spectroscopy was plagued with ambiguity until the first accurate Raman spectrum was published by our group in 2016. Although generally considered to be relatively stable, UF 4 can hydrolyze to form numerous UF 4 hydrates that may play a role in future uranium waste forms. In contrast to anhydrous UF 4 , the hydrates, with their OH stretch and HOH bending modes, can be spectroscopically characterized by the type and degree of water bonding in the crystal lattice, which can yield additional information about their crystal structure. Herein, vibrational spectroscopy (Raman and infrared) was used to characterize three different UF 4 hydrates: UF 4 (H 2 O) 0.33 , U 3 F 12 (H 2 O), and UF 4 (H 2 O) 2.5 . Spectra show the different hydrates vary in the number of observed bands, full-width half-maximum of the bands, and band intensity. These differences are due to varying interactions between the OH stretch and HOH bending modes with UF 4 and the polymeric UF 4 structure in the crystal lattice. These vibrational data, in combination with spectral fitting and crystallographic structures measured with powder X-ray diffraction and single crystal X-ray diffraction, provide unique details on the location of water molecules in the crystal lattice of hydrated UF 4 , and provide an interesting contrast to the vibrational spectra of anhydrous UF 4 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Detailed Characterization of an Annealed Reduced Graphene Oxide Catalyst for Selective Peroxide Formation Activity

Recently, electrochemical hydrogen peroxide (H 2 O 2 ) generation from oxygen molecules has been extensively studied. Thus far, the best peroxide activity under alkaline conditions has been reported at the surface of a mild reduced graphene oxide annealed at 600 °C (mrGO-600). However, the detailed material information, such as chemical functionality and structural morphology, is unknown, which results in ambiguous debates on its catalytic active sites. Therefore, to solve this problem, we intensively characterize the structure of mrGO-600 to clarify the origin of its catalytic activity. Various characterizations, including X-ray photoelectron spectroscopy, Raman spectroscopy, infrared spectroscopy, near-edge X-ray absorption fine spectroscopy, and high-resolution transmittance electron microscopy coupled with in situ infrared spectroelecrochemistry, reveal that the annealing process generates not only various hole edge defects that are related to the ring ether group but also numerous point defects that result in a small-sized disconnected graphitic carbon region. These defects are believed to form a unique atomic level configuration in mrGO-600, which enables it to facilitate high peroxide-generated activity from oxygen molecules in an alkaline electrolyte.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *

Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Single-particle dynamics of microbunching

The mechanism of steady-state microbunching (SSMB) has been proposed [D. F. Ratner and A. W. Chao, Phys. Rev. Lett. 105, 154801 (2010)] to generate high-power coherent radiation at a high repetition rate or in continuous-wave mode using electron storage rings. In this paper, the related single-particle dynamics are theoretically and numerically studied, and important results are presented. The investigated effects are longitudinal quantum radiation excitation, nonlinear momentum compaction, and linear and nonlinear coupling of the transverse and longitudinal motion. Although this analysis is oriented toward SSMB, some of the analyzed effects are also crucial in cases such as coherent harmonic generation, bunch slicing, bunch compression, free-electron laser beam transport lines, and quasi-isochronous rings, which involve precise longitudinal phase-space manipulations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

PETSc/TAO Users Manual V.3.21

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations (PDEs) and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for implementing large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and optimizers that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started ). The library is organized hierarchically, enabling users to employ the abstraction level most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

PETSc/TAO Users Manual Revision 3.22

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Nuclear Criticality Safety Margin and Handbook Data: Concepts and Applications [Slides]

At the end of this briefing, personnel should be able to: • Define concepts associated with criticality safety margin • Provide and understand practical applications that illustrate safety margin concepts • Describe how safety margin is addressed in criticality safety evaluations • Improve documentation and communication of safety margin • Connect these concepts to related statements in the ANS-8 Standards • Find numerous applications for handbook data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

PETSc/TAO Users Manual Revision 3.23

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PETSc/TAO Users Manual Revision 3.24

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

PETSc/TAO Users Manual Revision 3.25

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Current-Phase Relations of Superconducting PtSi Constriction Josephson Junctions

Here, we study the current-phase relations of superconductor–constriction–superconductor Josephson junctions made from platinum silicide thin films by fabricating dc-superconducting quantum interference devices incorporating pairs of junctions and measuring their magnetic-field-dependent electrical transport. By comparing the supercurrent interference patterns with numerical Ginzburg–Landau simulations, we extract the current-phase relations of individual junctions and quantify the degree of nonlinearity. These measurements show that while the constrictions themselves sustain substantial nonlinearity, the effect is moderated by the kinetic inductance of the device leads, which is an important consideration for practical superconducting circuit applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

A Lagrangian dual method for two-stage robust optimization with binary uncertainties

This report presents a new exact method to calculate worst-case parameter realizations in two-stage robust optimization problems with categorical or binary-valued uncertain data. Traditional exact algorithms for these problems, notably Benders decomposition and column-and-constraint generation, compute worst-case parameter realizations by solving mixed-integer bilinear optimization subproblems. However, their numerical solution can be computationally expensive not only due to their resulting large size after reformulating the bilinear terms, but also because decision-independent bounds on their variables are typically unknown. We propose an alternative Lagrangian dual method that circumvents these difficulties and is readily integrated in either algorithm. We specialize the method to problems where the binary parameters switch on or off constraints as these are commonly encountered in applications, and discuss extensions to problems that lack relatively complete recourse and to those with integer recourse. Numerical experiments provide evidence of significant computational improvements over existing methods.

42 ENGINEERING↗

DOE OSTI2023

Spatial Intermittency of Particle Distribution in Relativistic Plasma Turbulence

Abstract Relativistic magnetically dominated turbulence is an efficient engine for particle acceleration in a collisionless plasma. Ultrarelativistic particles accelerated by interactions with turbulent fluctuations form nonthermal power-law distribution functions in the momentum (or energy) space, f ( γ ) d γ ∝ γ − α d γ , where γ is the Lorenz factor. We argue that in addition to exhibiting non-Gaussian distributions over energies, particles energized by relativistic turbulence also become highly intermittent in space. Based on particle-in-cell numerical simulations and phenomenological modeling, we propose that the bulk plasma density has lognormal statistics, while the density of the accelerated particles, n , has a power-law distribution function, P ( n ) dn ∝ n − β dn . We argue that the scaling exponents are related as β ≈ α + 1, which is broadly consistent with numerical simulations. Non-space-filling, intermittent distributions of plasma density and energy fluctuations may have implications for plasma heating and for radiation produced by relativistic turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Application of linear prolongation to coarse mesh finite difference acceleration in CASMO5

The Coarse-Mesh Finite Difference (CMFD) method has been used for over a decade to accelerate the convergence of the Method of Characteristics (MOC) solution to the two- dimensional particle transport equation in CASMO5. Numerical testing, along with widespread use in production-level calculations, have shown that the current CMFD implementation provides stability and robustness for a wide range of realistic reactor physics problems. However, the recent development of linear prolongation has attracted attention from the community as a way to further improve the performance and stability of CMFD. Two interpolation methods for linear prolongation are presented in this work and implemented into a test version of CASMO5. The performance of the proposed interpolations, referred to as the bilinear and linear directional schemes, is evaluated in terms of runtime relative to the default constant or uniform scaling update. Numerical results indicate that the use of linear prolongation can reduce the transport solver runtime on average by approximately 10% when tested with two hundred randomly selected cases. The new directional linear interpolation, combined with default constant boundary updates, is found to provide the highest reduction in runtime for the cases analyzed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗