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At least 973 records · Page 54

Diffusion Monte Carlo approaches for studying nuclear quantum effects in fluxional molecules

Abstract Diffusion quantum Monte Carlo (DMC) provides a powerful approach for obtaining the ground state energy and wave function of molecules, ions, and molecular clusters. The approach is uniquely well suited for studies of fluxional molecules, which undergo large amplitude vibrational motions even in their ground state. In contrast to the electronic structure problem, where the wave function must be antisymmetric with respect to exchange of any pair of electrons, the wave function for the ground vibrational state is nodeless. This greatly simplifies the application of DMC for vibrational problems. Because there is not a single potential function that can be used to describe the intramolecular and intermolecular interactions in all molecular systems, most methods that are used to describe nuclear quantum effects rely on a carefully chosen zero‐order description of the molecular vibrations. In contrast, DMC calculations can be performed in Cartesian coordinates, making the DMC algorithm easily transferable between different chemical systems. In this contribution, the theory that underlies DMC will be discussed along with important considerations for performing DMC calculations. Extensions for evaluating vibrationally excited states and molecular properties are also discussed. Insights that can be obtained from DMC calculations are illustrated in the context of the protonated water clusters. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Theoretical and Physical Chemistry > Spectroscopy

Chemistry↗

A novel Mo-W interlayer approach for CVD diamond deposition on steel

Steel is the most widely used material in engineering for its cost/performance ratio and coatings are routinely applied on its surface to further improve its properties. Diamond coated steel parts are an option for many demanding industrial applications through prolonging the lifetime of steel parts, enhancement of tool performance as well as the reduction of wear rates. Direct deposition of diamond on steel using conventional chemical vapour deposition (CVD) processes is known to give poor results due to the preferential formation of amorphous carbon on iron, nickel and other elements as well as stresses induced from the significant difference in the thermal expansion coefficients of those materials. This article reports a novel approach of deposition of nanocrystalline diamond coatings on high-speed steel (M42) substrates using a multi-structured molybdenum (Mo) – tungsten (W) interlayer to form steel/Mo/Mo-W/W/diamond sandwich structures which overcome the adhesion problem related to direct magnetron sputtering deposition of pure tungsten. Surface, interface and tribology properties were evaluated to understand the role of such an interlayer structure. The multi-structured Mo-W interlayer has been proven to improve the adhesion between diamond films and steel substrates by acting as an effective diffusion barrier during the CVD diamond deposition.

36 MATERIALS SCIENCE↗

A Domain Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients

We developed a domain decomposition model reduction method for linear steady- state convection-diffusion equations with random coefficients. Of particular interest to this effort are the diffusion equation with random diffusivity and the convection-dominated transport equation with random velocity. We investigated two types of random fields, i.e., colored noises and discrete white noises, both of which can lead to high-dimensional parametric dependence in practice. The motivation is to exploit domain decomposition to reduce the parametric dimension of local problems in subdomains, such that an entire parametric map can be approximated with a small number of expensive partial differential equation (PDE) simulations. The new method combines domain decomposition with model reduction and sparse polynomial approximation, so as to simultaneously handle the high dimensionality and irregular behavior of the PDEs under consideration. Here, the advantages of our method lie in three aspects: (i) online-offline decomposition, i.e., the online cost is independent of the size of the triangle mesh; (ii) sparse approximation of operators involving nonaffine high-dimensional random fields; (iii) an effective strategy to capture irregular behaviors, e.g., sharp transitions of the PDE solutions. Two numerical examples are provided to demonstrate the advantageous performance of our method.

97 MATHEMATICS AND COMPUTING↗

Unlocking Failure Mechanisms and Improvement of Practical Li-S Pouch Cells Through in Operando Pressure Study

For Lithium-sulfur (Li-S) battery commercialization, the study at a pouch cell level is essential, as some problems ignored or deemed minimal at the smaller level could have a greater effect on the performance of the larger pouch cell. Herein, the failure mechanisms of Li-S pouch cells are deeply investigated via in operando pressure analysis. It is found that highly porous structures of cathodes/separators and slow electrolyte diffusion through cathodes/separators can both lead to poor initial wetting. Additionally, Li-metal anode dominates the thickness variation of the whole pouch cell, which is verified by in situ measured pressure variation. Consequently, a real-time approach that combined normalized pressure with dP/dV analysis is proposed and validated to diagnose the morphology evolution of Li-metal anode. Moreover, applied pressure and porosity/tortuosity ratio of the cathode are both identified as independent factors that influence anode performance. In addition to stabilizing anodes, high pressure is proven to improve the cathode connectivity and avoid cathode cracking over cycling, which improves the possibility of developing cathodes with high sulfur mass loading. This work provides insights into Li-S pouch cell design (e.g., cathode and separator) and highlights pathways to improve cell capacity and cycling performance with applied and monitored pressure

25 ENERGY STORAGE↗

Development of a Performance Portable Non-Equilibrium Plasma Fluid Solver on Adaptive Grids

This presentation will describe the numerical techniques, programming paradigms, verification, and performance of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures. Our plasma fluid model solves the conservation equations for self-consistent electrostatic Poisson, electron and heavy species transport, and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive mesh management library, AMReX (Zhang et al., JOSS, 4 (37) 1370, 2019), and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We utilize a non-subcycled second order semi-implicit time-stepping method where all adaptive mesh refinement (AMR) levels are advanced with the same time step. The composite multi-level multigrid solver from within AMReX is used for each of the governing equations that are cast into a Helmholtz equation form. We have also developed a python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files as input. Our custom parser reads the yaml file and provides C++ files with transport and production rate functions that can be executed on both host (CPU) and device (GPU). We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on low-pressure capacitive and high-pressure streamer discharges. Our initial performance studies indicate 10X speed-up using 20 NVIDIA GPUs versus 200 CPUs for an atmospheric streamer discharge problem solved on a 512 x 1024 x 512 grid.

graphics processing units↗

A transformed path integral approach for solution of the Fokker–Planck equation

A novel path integral (PI) based method for solution of the Fokker–Planck equation is presented. The proposed method, termed the transformed path integral (TPI) method, utilizes a new formulation for the underlying short-time propagator to perform the evolution of the probability density function (PDF) in a transformed computational domain where a more accurate representation of the PDF can be ensured. The new formulation, based on a dynamic transformation of the original state space with the statistics of the PDF as parameters, preserves the non-negativity of the PDF and incorporates short-time properties of the underlying stochastic process. New update equations for the state PDF in a transformed space and the parameters of the transformation (including mean and covariance) that better accommodate nonlinearities in drift and non-Gaussian behavior in distributions are proposed (based on properties of the SDE). Owing to the choice of transformation considered, the proposed method maps a fixed grid in transformed space to a dynamically adaptive grid in the original state space. The TPI method, in contrast to conventional methods such as Monte Carlo simulations and fixed grid approaches, is able to better represent the distributions (especially the tail information) and better address challenges in processes with large diffusion, large drift and large concentration of PDF. Additionally, in the proposed TPI method, error bounds on the probability in the computational domain can be obtained using the Chebyshev's inequality. The benefits of the TPI method over conventional methods are illustrated through simulations of linear and nonlinear drift processes in one-dimensional and multidimensional state spaces. The effects of spatial and temporal grid resolutions as well as that of the diffusion coefficient on the error in the PDF are also characterized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Is the surface oxygen exchange rate linked to bulk ion diffusivity in mixed conducting Ruddlesden–Popper phases?

There is a possible link between oxygen surface exchange rate and bulk oxygen anion diffusivity in mixed ionic and electronic conducting oxides; it is a topic of great interest and debate. While a large body of experimental evidence and theoretical analyses support a link, observed differences between bulk and surface composition of these materials are hard to reconcile with this observation. This is further compounded by potential problems with simultaneous measurement of both parameters. Here we utilize separate techniques, in situ neutron diffraction and pulsed isotopic surface exchange, to examine bulk ion mobility and surface oxygen exchange rates of three Ruddlesden-Popper phases, general form A n-1 A 2 'BnO 3n+1 , A n-1 A 2 'BnX 3n+1 ; LaSrCo 0.5 Fe 0.5 O 4-δ (n = 1), La 0.3 Sr 2.7 CoFeO 7-δ (n = 2) and LaSr 3 Co 1.5 Fe 1.5 O 10-δ (n = 3). These measurements are complemented by surface composition determination via high sensitivity-low energy ion scattering. We observe a correlation between bulk ion mobility and surface exchange rate between materials. The surface exchange rates vary by more than one order of magnitude with high anion mobility in the bulk of an oxygen vacancy-rich n = 2 Ruddlesden-Popper material correlating with rapid oxygen exchange. Furthermore this is in contrast with the similar surface exchange rates which we may expect due to similar surface compositions across all three samples. This paper conclude that experimental limitations lead to inherent convolution of surface and bulk rates, and that surface exchange steps are not likely to be rate limiting in oxygen incorporation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving a mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.

97 MATHEMATICS AND COMPUTING↗

Pececillo

Pececillo is a proxy-app for the open source Truchas metal processing code (LA-CC-15-097). It implements many of the physics models used in Truchas: free-surface, incompressible Navier-Stokes fluid dynamics (e.g., water waves); heat transport, material phase change, view factor thermal radiation; species advection-diffusion; quasi-static, elastic/plastic solid mechanics with contact; electomagnetics (Maxwell's equations). The models are simplified versions that retain the fundamental computational complexity of the Truchas models while omitting many non-essential features and modeling capabilities. The purpose is to expose Truchas algorithms in a greatly simplified context where computer science problems related to parallel performance on advanced architectures can be more easily investigated. While Pececillo is capable of performing simulations representative of typical Truchas metal casting, welding, and additive manufacturing simulations, it lacks many of the modeling capabilites needed for real applications.

Jibben, Zechariah↗

Performance Improvements for the Griffin Transport Solvers

Griffin is a Multiphysics Object-Oriented Simulation Environment based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. Griffin includes a variety of deterministic radiation transport solvers for fixed source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. A code assessment performed in FY-20 identified two significant issues with the transport solvers in Griffin: first, the primary heterogeneous SN (discrete ordinates) transport solver based on continuous finite element methods required significant mesh refinement and higher memory usage compared to solvers based on the method of characteristic for equivalent accuracy. Second, the homogeneous PN (spherical harmonics expansion) transport solver did not adequately support polynomial refinement, which is a feature usually required for problems with spatial homogenization and pronounced streaming, typical in fast or gas-cooled reactor systems. To address the first issue, the development effort focused on the more promising discontinuous finite element method (DFEM)-based SN transport solver in Griffin. The addition of an asynchronous parallel transport sweeper and coarse mesh finite difference (CMFD) acceleration have rendered a superior heterogeneous SN transport capability for multiphysics problems that requires far less computing resources in terms of both CPU time and memory usage. This is demonstrated with typical thermal- and fast-spectrum reactor benchmark problems, including 2D Transient Reactor Test, 3D Advanced Burner Test Reactor (ABTR), and 2D and 3D Empire microreactor. For the second issue, the development effort focused on a new transport solver based on the hybrid finite element PN method (HFEM-PN), equivalent to the variational nodal method, as well as a new diffusion solver based on HFEM-Diffusion. This solver is intended for homogenized domains with multiphysics coupling (i.e., supports mesh displacement, seamless temperature feedback, etc.). Initial calculations with the HFEM-Diffusion implementation show very good parallel efficiency for the residual evaluations with the 2D ABTR benchmark. A future development effort will be centered on further improvements to the CMFD, HFEM-PN, and DFEM diffusion solvers to ensure Griffin meets performance and software quality assurance requirements for advanced reactor design and analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Pececillo

Pececillo is a proxy-app for the open source Truchas metal processing code (LA-CC-15-097). It implements many of the physics models used in Truchas: free-surface, incompressible Navier-Stokes fluid dynamics (e.g., water waves); heat transport, material phase change, view factor thermal radiation; species advection-diffusion; quasi-static, elastic/plastic solid mechanics with contact; electomagnetics (Maxwell's equations). The models are simplified versions that retain the fundamental computational complexity of the Truchas models while omitting many non-essential features and modeling capabilities. The purpose is to expose Truchas algorithms in a greatly simplified context where computer science problems related to parallel performance on advanced architectures can be more easily investigated. While Pececillo is capable of performing simulations representative of typical Truchas metal casting, welding, and additive manufacturing simulations, it lacks many of the modeling capabilites needed for real applications.

Carlson, Neil↗

Biochemical Process Modeling and Simulation (BPMS)

The Biochemical Process Modeling and Simulation project aims to reduce the cost and time of research by applying theory, modeling, and simulation to the most relevant bottlenecks in the biochemical process. We use molecular modeling, quantum mechanics, metabolic modeling, fluid dynamics, and reaction-diffusion methods in close collaboration with pretreatment, hydrolysis, upgrading, and TEA. The project's outcomes are increased yields and efficiency of the biochemical process, added value to products, and reduced price of fuels by specifically targeting catalytic efficiency, reactor design, enzyme efficiency, and microbial design. We work closely with experimental projects to identify problems and iterate with experiments to find and refine solutions. By working with experimentalists, we decide on problems that can be solved with simulation that could otherwise not be solved or would take too long with experiment alone to reach BETO's targets. Over the years, we have produced solutions that have resulted in determining the most likely fatty-acid derivative for passive transport out of bacteria that upgrade biomass, and we have also designed enzyme mutations for enhanced lignin upgrading. Metabolic models have been developed to tune the activity of 2,3 butanediol production for the 2030 target. A computational method to deliver understanding of how complex omics data can be interpreted in the metabolic pathways of organisms used in the Agile Biofoundry. We have found methods to overcome specific barriers and continue to develop those methods. Our reactor studies have guided the design of both the microbes and reactors for aerobic and micro-aerobic production at all scales and have been instrumental in improving the accuracy of techno-economic analysis models. This project is essential in the process of selecting the final processes for 2030 SAF production targets. More specifically, recently, we have: 1) Predicted the strength of the basic structural interactions in commodity plastics to provide guidance for plastics upcycling strategies. 2) Developed computational tool to improve the characterization of lignin-derived compounds 3) Developed new methodologies to enable Machine Learning-based Directed Evolution for protein engineering. 4) Developed Machine Learning methods to predict protein promiscuity and mutations to further improve microbial and enzymatic driven processes and demonstrated the utility of ML approaches to engineering proteins from sparse experimental datasets. 5) Developed new methods to enable high-fidelity simulation of aerobic fermentation at industrial scale and resolving mismatch of time scales through subcycling/operator splitting 7) Identified the difficulty in preventing local high-oxygen conditions in industrial bubble columns, which leads to less-desirable acetoin production, suggesting future research directions in alternative reactor configurations (e.g loop reactors, shallow-channel reactors).

BIOMASS FUELS↗

Region-of-interest diffuse optical tomography system

Diffuse optical tomography (DOT) using near-infrared light is a promising tool for non-invasive imaging of deep tissue. This technique is capable of quantitative reconstruction of absorption (μ{sub a}) and scattering coefficient (μ{sub s}) inhomogeneities in the tissue. The rationale for reconstructing the optical property map is that the absorption coefficient variation provides diagnostic information about metabolic and disease states of the tissue. The aim of DOT is to reconstruct the internal tissue cross section with good spatial resolution and contrast from noisy measurements non-invasively. We develop a region-of-interest scanning system based on DOT principles. Modulated light is injected into the phantom/tissue through one of the four light emitting diode sources. The light traversing through the tissue gets partially absorbed and scattered multiple times. The intensity and phase of the exiting light are measured using a set of photodetectors. The light transport through a tissue is diffusive in nature and is modeled using radiative transfer equation. However, a simplified model based on diffusion equation (DE) can be used if the system satisfies following conditions: (a) the optical parameter of the inhomogeneity is close to the optical property of the background, and (b) μ{sub s} of the medium is much greater than μ{sub a} (μ{sub s} > > μ{sub a}). The light transport through a highly scattering tissue satisfies both of these conditions. A discrete version of DE based on finite element method is used for solving the inverse problem. The depth of probing light inside the tissue depends on the wavelength of light, absorption, and scattering coefficients of the medium and the separation between the source and detector locations. Extensive simulation studies have been carried out and the results are validated using two sets of experimental measurements. The utility of the system can be further improved by using multiple wavelength light sources. In such a scheme, the spectroscopic variation of absorption coefficient in the tissue can be used to arrive at the oxygenation changes in the tissue.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A mechanistic modeling framework for gas-phase adsorption kinetics and fixed-bed transport

Adsorption is a complex physicochemical process involving interparticle transport, interphase mass-transfer, intraparticle diffusion, and surface reactions. Although the exact description of the adsorption process will inevitably vary from system to system, it will always be governed by those primary mechanisms. Thus, by devising a model framework that can inherently include those mechanisms, it would be possible to create a modeling platform on which many different adsorption problems could be solved numerically. In order to accomplish this task, a generalized 1-D conservation law model was created to include the necessary mechanisms of adsorption on several different geometrical domains. Specific model applications for adsorption were developed under that framework and validated using experimental data available in literature or obtained in this work. This modeling platform makes it easier to model various adsorption problems and develop new adsorption models because of the common treatment of the mathematics governing the physical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Application of Bound-to-Bound Data Collaboration approach for development and uncertainty quantification of a reduced char combustion model

The development of efficient industrial oxy-coal boilers can be significantly aided by Computational Fluid Dynamics (CFD) tools, as far as fidelity in modeling coal combustion is also complemented by feasible computational costs. Reduced and predictive models are the most suitable for this application scale. Reduced models feature predictivity when they are validated against a broad range of experiments and targeted by Uncertainty Quantification (UQ) procedures. This work proposes a numerical procedure that uses Bound-to-Bound Data Collaboration (B2B-DC) to derive a reduced char combustion model describing transport phenomena and reactions between char carbon and O 2 , CO 2 and H 2 O, in both conventional and oxy-conditions. The approach determines the consistency between a numerical model and an experimental dataset. The latter is made up of the experiments carried out in an optically accessible laminar entrained flow reactor, operated by Sandia National Laboratories. The procedure follows five steps towards predictive modeling capability, namely: quantification of the uncertainty in the experiments, via instrument verification and modeling; development of a physics model and continuous improvement of its fidelity, via model-form uncertainty; identification of the uncertain and most sensitive parameters and of their prior bounds; sampling of the initial uncertain parameter space and training of a surrogate model; validation of the physics model via inference from the data. The last step, also known as inverse problem, is performed by applying the Bound-to-Bound Data Collaboration approach. A char combustion model is found consistent with the experimental data and its validity stands for conventional and oxy-combustion conditions. It accounts for heterogeneous reactions at the particle surface, mass transport of species in the particle boundary layer, pore diffusion and surface area changes. The consistent reduced model overcomes the differences in mass transport and kinetics observed in the experimental campaign. In conclusion, a reduction of the initial degree of uncertainty in both model and experiments is achieved.

42 ENGINEERING↗

Effect of ball milling conditions on microstructure and lithium storage properties of LiNi0.5Mn1.5O4 as cathode for lithium-ion batteries

Highlights: • Precursors of LNM respectively synthesized by dry and wet ball-milling. • Under wet ball-milling raw materials form slurry and mix well in microscale. • LNM-w12 sample presents proper stoichiometric composition and the highest D{sub Li}{sup +}. • At 0.1 C rate LNM-w12 delivers 133 mAh g{sup −1} at 100 cycle close to theoretical value. • LNM-w12 delivers 97 mAh g{sup −1} at 10 C rate and remains 78% after 1000 cycles. - Abstract: In this work, the precursors of LiMn{sub 1.5}Ni{sub 0.5}O{sub 4} (LNM) are respectively synthesized by dry and wet ball-milling. Under wet ball-milling, the problem of sticky residue in dry ball-milling is effectively resolved, the three raw materials form homogeneous slurry and mix well in micrometers level. LNM-w12 sample synthesized by using precursor added 12 mL milling medium gains the atomic composition closed to the theoretical value, ordered crystal structure and the highest lithium diffusion coefficient. At 0.1 C rate, LNM-w12 delivers the capacity of 136 mAh g{sup −1} at the first cycle, and 133 mAh g{sup −1} at the 100th cycle. Moreover, LNM-w12 delivers the initial capacity of 97 mAh g{sup −1} at 10 C rate, and remains 78% after 1000 cycles. The great rate capability of LNM-w12 can be attributed to the well-formed crystal structure, ordered and stable surface structure, which is conducive to the diffusion of lithium ions and prevents the dissolution of the material at high voltage.

25 ENERGY STORAGE↗

Bound-preserving finite element approximations of the Keller–Segel equations

We report this paper aims to develop numerical approximations of the Keller–Segel equations that mimic at the discrete level the lower bounds and the energy law of the continuous problem. We solve these equations for two unknowns: the organism (or cell) density, which is a positive variable, and the chemoattractant density, which is a non-negative variable. We propose two algorithms, which combine a stabilized finite element method and a semi-implicit time integration. The stabilization consists of a nonlinear artificial diffusion that employs a graph-Laplacian operator and a shock detector that localizes local extrema. As a result, both algorithms turn out to be nonlinear and can generate cell and chemoattractant numerical densities fulfilling lower bounds. However, the first algorithm requires a suitable constraint between the space and time discrete parameters, whereas the second one does not. We design the latter to attain a discrete energy law on acute meshes. We report some numerical experiments to validate the theoretical results on blowup and nonblowup phenomena. In the blowup setting, we identify a locking phenomenon that relates the L ∞ (Ω)-norm to the L 1 (Ω)-norm limiting the growth of the singularity when supported on a macroelement.

97 MATHEMATICS AND COMPUTING↗