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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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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Dispersion Validation for Flow Involving a Large Structure

The atmospheric dispersion of contaminants in the wake of a large urban structure is a challenging fluid mechanics problem of interest to the scientific and engineering communities. Magnetic Resonance Velocimetry (MRV) is a relatively new technique that leverages diagnostic equipment used primarily by the medical field to make 3D engineering measurements of flow and contaminant dispersal. SIERRA/Fuego, a computational fluid dynamics (CFD) code at Sandia National Labs is employed to make detailed comparisons to the dataset to evaluate the quantitative and qualitative accuracy of the model. The comparison exercise shows good comparison between model and experimental results, with the wake region downstream of the tall building presenting the most significant challenge to the quantitative accuracy of the model. Model uncertainties are assessed through parametric variations. Some observations are made in relation to the future utility of MDV and CFD, and some productive follow-on activities are suggested that can help mature the science of flow modeling and experimental testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

ClusterCAD: a computational platform for type I modular polyketide synthase design

Here, we present ClusterCAD, a web-based toolkit designed to leverage the collinear structure and deterministic logic of type I modular polyketide synthases (PKSs) for synthetic biology applications. The unique organization of these megasynthases, combined with the diversity of their catalytic domain building blocks, has fueled an interest in harnessing the biosynthetic potential of PKSs for the microbial production of both novel natural product analogs and industrially relevant small molecules. However, a limited theoretical understanding of the determinants of PKS fold and function poses a substantial barrier to the design of active variants, and identifying strategies to reliably construct functional PKS chimeras remains an active area of research. In this work, we formalize a paradigm for the design of PKS chimeras and introduce ClusterCAD as a computational platform to streamline and simplify the process of designing experiments to test strategies for engineering PKS variants. ClusterCAD provides chemical structures with stereochemistry for the intermediates generated by each PKS module, as well as sequence- and structure-based search tools that allow users to identify modules based either on amino acid sequence or on the chemical structure of the cognate polyketide intermediate. ClusterCAD can be accessed at https://clustercad.jbei.org and at http://clustercad.igb.uci.edu.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Template-Framework Interactions in Tetraethylammonium-Directed Zeolite Synthesis

Zeolites, having widespread applications in chemical industries, are often synthesized using organic templates. These can be cost-prohibitive, motivating investigations into their role in promoting crystallization. Herein, the relationship between framework structure, chemical composition, synthesis conditions and the conformation of the occluded, economical template tetraethylammonium (TEA + ) has been systematically examined by experimental and computational means. The results show two distinct regimes of occluded conformer tendencies: 1) In frameworks with a large stabilization energy difference, only a single conformer was found (BEA, LTA and MFI). 2) In the frameworks with small stabilization energy differences (AEI, AFI, CHA and MOR), less than the interconversion of TEA + in solution, a heteroatom-dependent (Al, B, Co, Mn, Ti, Zn) distribution of conformers was observed. Our findings demonstrate that host–guest chemistry principles, including electrostatic interactions and coordination chemistry, are as important as ideal pore-filling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Relativistic coupled‐cluster and equation‐of‐motion coupled‐cluster methods

Abstract The development of relativistic coupled‐cluster (CC) and equation‐of‐motion coupled‐cluster (EOM‐CC) methods is reviewed. An emphasis is placed on recent efforts to improve the computational efficiency of CC and EOM‐CC calculations with non‐perturbative treatments of spin‐orbit coupling (SO‐CC and EOM‐CC) by partially recovering spin symmetry in the formulations. Example calculations of electronic ground state as well as valence‐excited and core‐excited states for molecules containing heavy elements are presented to demonstrate the applicability and usefulness of the SO‐CC and EOM‐CC methods. Future directions for the development of the SO‐CC and EOM‐CC methods are also discussed. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Localization landscape theory of disorder in semiconductors. I. Theory and modeling

Here, we present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called localization landscape. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. We show how the various outputs of these landscapes can be directly implemented into a drift-diffusion model of carrier transport and into the calculation of absorption/emission transitions. This creates a new computational model which accounts for disorder localization effects while also capturing two major effects of quantum mechanics, namely, the reduction of barrier height (tunneling effect) and the raising of energy ground states (quantum confinement effect), without having to solve the Schrödinger equation. Finally, this model is applied to several one-dimensional structures such as single quantum wells, ordered and disordered superlattices, or multiquantum wells, where comparisons with exact Schrödinger calculations demonstrate the excellent accuracy of the approximation provided by the landscape theory.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2022

Kinetics-Informed Neural Networks

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. Furthermore, this approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the convergence of statistics in simulations of stationary incompressible turbulent flows

When reporting statistics from simulations of statistically stationary chaotic phenomenon, it is important to verify that the simulations are time-converged. This condition is connected with the statistical error or number of digits with which statistics can be reliably reported. In this work we consider numerical experiments of low Reynolds number incompressible homogeneous and isotropic turbulence as a model problem to investigate statistical convergence over finite simulation times. Specifically, we investigate the time integration requirements that allow meaningful reporting of the statistical error associated with finiteness of the temporal domain. We address two key questions: (1) How long should a simulation be performed in terms of large eddy time, and (2) How should the simulation time be divided among temporal windows over which a quantity of interest is estimated so that its statistical error could be reliably reported? We find that reliable reporting of statistical errors requires simulations on the order of 10 4 large eddy times, which is orders of magnitude longer than typically performed. Additionally, data post-processing should employ windows of at least ten times the large eddy time scale, with the most robust computation of statistical error of the mean requiring window sizes of an additional factor of ten. For practical simulations, we demonstrate that it is possible to estimate the statistical error within a factor of two under a less stringent condition in which a minimum of four windows with size at least ten large eddy times are used. In conclusion, our observations for homogeneous isotropic turbulence are also shown to hold in turbulent channel flow.

42 ENGINEERING↗
DOE OSTI2022

express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Finally, two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating hydrogen isotope transport mechanisms in proton-conducting ceramics with trapping effects using TMAP8

Hydrogen isotopes play an central role in many science and engineering applications such as fuel cells, hydrogen production, and fusion energy. For these applications, hydrogen separation and extraction applications are pivotal aspects of hydrogen transports, where proton-conducting ceramics (PCCs) have shown great potential. In this study, we propose a new model for hydrogen isotope transport in PCC materials, BaZr 0.9 Y 0.1 O 2.95 (BZY) in particular, which captures behavior in both dry and wet environments. The model expands previous efforts and considers diffusion, trapping, and surface reactions (i.e., dissociation and recombination). We then validate and calibrate the model using deuterium transport measurements from experiments in both dry and wet environments. This study highlights the key role of trapping, often neglected, on hydrogen isotope transport in BZY and other PCC materials. It also explains how the commonly observed discrepancy between dry and wet behavior can be attributed to more active surface reactions and saturated traps due to the increased hydrogen presence under the wet environment. These results provide insights to optimize PCC manufacturing and usage as a hydrogen separation and extraction technology in various fields, emphasizing that lowering the trapping can reduce hydrogen isotope retention. These modeling and calibration efforts are performed using the tritium migration analysis program, version 8 (TMAP8), an open-source application designed for hydrogen isotope transport.

36 - MATERIALS SCIENCE↗
DOE OSTI2017

The shifted boundary method for embedded domain computations. Part I: Poisson and Stokes problems

We propose a new finite element method for embedded domain computations, which falls in the category of surrogate/approximate boundary algorithms. The key feature of the proposed approach is the idea of shifting the location where boundary conditions are applied from the true to the surrogate boundary, and to appropriately modify the shifted boundary conditions, enforced weakly, in order to preserve optimal convergence rates of the numerical solution. This process yields a method which, in our view, is simple, efficient, and also robust, since it is not affected by the small-cut-cell problem. Although general in nature, here we apply this new concept to the Poisson and Stokes problems. We present in particular the full analysis of stability and convergence for the case of the Poisson operator, and numerical tests for both the Poisson and Stokes equations, for geometries of progressively higher complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An axial temperature profile curvature criterion for the engineering of convex crystal growth interfaces in Bridgman systems

A unifying idea is presented for the engineering of convex melt-solid interface shapes in Bridgman crystal growth systems. Previous approaches to interface control are discussed with particular attention paid to the idea of a “booster” heater. Proceeding from the idea that a booster heater promotes a converging heat flux geometry and from the energy conservation equation, we show that a convex interface shape will naturally result when the interface is located in regions of the furnace where the axial thermal profile exhibits negative curvature, i.e., where d 2 T/dz 2 < 0. This criterion is effective in explaining prior literature results on interface control and promising for the evaluation of new furnace designs. Here we posit that the negative curvature criterion may be applicable to the characterization of growth systems via temperature measurements in an empty furnace, providing insight about the potential for achieving a convex interface shape, without growing a crystal or conducting simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Temperature-dependent model of helium bubble expansion and bursting in tungsten

Continuum-scale models that can reliably predict the behavior of helium in tungsten are of interest to the fusion community due to the projected impact of these materials on fusion reactor operation. In this work, we perform molecular dynamics simulations of spherical helium bubbles of various sizes in tungsten at different temperatures and depths with the goal of determining a mathematical model of the pressure and density at which the bubbles initially expand or burst as a function of depth, size, temperature, and surface orientation. The bubbles are small enough that their loop-punching pressures cannot be accurately predicted with continuum mechanics, and their expansion behavior is important, as it appears to cause many of the features observed on helium-irradiated tungsten surfaces. We vary the temperature, bubble size, bubble depth, and surface orientation in each case, recording the bubble pressure and density that result in bubble expansion. An exponential function with three adjustable parameters is found to fit the results well; the parameters that best fit our results are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Physical properties of zircon and scheelite lutetium orthovanadate: Experiment and first-principles calculation

Pure zircon and scheelite LuVO{sub 4} were prepared by solid state reaction and high-pressure route, respectively. Structure, elastic constants, lattice dynamics and thermodynamics of LuVO{sub 4} polymorphs were studied by experiments and first principles calculation. Calculations here are in good agreement with the experimental results. The phonon dispersions of LuVO{sub 4} polymorphs were studied by the linear response method. The calculated phonon dispersions show that zircon and scheelite LuVO{sub 4} phases are dynamically stable. Raman-active frequencies were measured and assigned to different modes according to the calculations. The internal frequencies shift downward after phase transition from zircon to scheelite. Born effective charge tensors elements for both phases are analyzed. The finite temperature thermodynamic properties of LuVO{sub 4} polymorphs were calculated from the obtained phonon density of states by quasi-harmonic approach. - Graphical abstract: Lutetium orthovanadate polymorphs were synthesized by SSR and HP methods and their physical and chemical properties, including lattice dynamical properties, were determined by DFT calculations and experiments. Display Omitted - Highlights: • Pure zircon and scheelite LuVO{sub 4} polymorphs were synthesized by solid state reaction and high-pressure route. • Chemical and physical properties of LuVO4 polymorphs were studied by experiments and first principles calculation. • Raman-active frequencies were measured and assigned to different modes according to the calculations. • Lattice dynamics of polymorphs were discussed in details.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Consequences of Ca multisite occupation for the conducting properties of BaTiO{sub 3}

In combination with the dielectric modulus formalism and theoretical calculations, a newly developed defect incorporation mode, which is a combination of the standard A- and B-site doping mechanisms, is used to explain the conducting properties in 5 mol% Ca-doped BaTiO{sub 3}. Simulation results for Ca solution energies in the BaTiO{sub 3} lattice show that the new oxygen vacancy inducing mixed mode exhibits low defect energies. A reduction in dc conductivity compared with undoped BaTiO{sub 3} is witnessed for the incorporation of Ca. The conducting properties of 5 mol% Ca-doped BaTiO{sub 3} are analyzed using molecular dynamics and impedance spectroscopy. The ionic conductivity activation energies for each incorporation mode are calculated and good agreement with experimental data for oxygen migration is observed. The likely existence of the proposed defect configuration is also analyzed on the basis of these methods. - Graphical abstract: Oxygen vacancy formation as a result of self-compensation in Ca-doped BaTiO{sub 3}.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Partitioning Quantum Chemistry Simulations with Clifford Circuits

Current quantum computing hardware is restricted by the availability of only few, noisy qubits which limits the investigation of larger, more complex molecules in quantum chemistry calculations on quantum computers in the near term. Here, in this work, we investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver. To this end, we consider naive and physically motivated, classically efficient product ansatz for the parametrized wavefunction adapting the separable-pair ansatz form. We combine it with post-treatment to account for interactions between subsystems originating from this ansatz. The classical treatment is given by another quantum circuit that has support between the enforced subsystems and is folded into the Hamiltonian. To avoid an exponential increase in the number of Hamiltonian terms, the entangling operations are constructed from purely Clifford or near-Clifford circuits. While Clifford circuits can be simulated efficiently classically, they are not universal. In order to account for missing expressibility, near-Clifford circuits with only few, selected non-Clifford gates are employed. The exact circuit structure to achieve this objective is molecule-dependent and is constructed using simulated annealing and genetic algorithms. We demonstrate our approach on a set of molecules of interest and investigate the extent of our methodology’s reach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗