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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

On Possible States of the Crystal Structure Preceding to a Phase Transition in Zn{sub 1 –} {sub x}V{sub x}Se (0.01 ≤ x ≤ 0.10) Crystals

The systematic new formations observed in the reciprocal lattice of the cubic structural modification of a II–VI compound are characterized using a detailed neutron diffraction study of bulk semiconducting ZnSe crystals with an increased vanadium content. Direct evidence that the additional sites k = (1/3 1/3 1/3) 2π/a (k is the wave vector and a is cubic unit cell parameter) observed by neutron scattering in the crystals, in the case when they belong to mutually penetrated rotated sublattices, contain a superstructure contribution formed by short-wave deformation, is obtained for the first time. This structure state is determined as a pretransition to the concentration fcc–hcp phase transformation, and the basis functions that allow one to analyze atomic displacements, the correlation between which create distortion-type superstructures, are indicated for the transition through one-arm channel, considering the transitions by the star of wave vector k{sub 5} of the fcc lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Solvers for $\mathcal{O} (N)$ Electronic Structure in the Strong Scaling Limit

Here we present a hybrid OpenMP/Charm\tt++ framework for solving the $\mathcal{O} (N)$ self-consistent-field eigenvalue problem with parallelism in the strong scaling regime, $P\gg{N}$, where $P$ is the number of cores, and $N$ is a measure of system size, i.e., the number of matrix rows/columns, basis functions, atoms, molecules, etc. This result is achieved with a nested approach to spectral projection and the sparse approximate matrix multiply [Bock and Challacombe, SIAM J. Sci. Comput., 35 (2013), pp. C72--C98], and involves a recursive, task-parallel algorithm, often employed by generalized $N$-Body solvers, to occlusion and culling of negligible products in the case of matrices with decay. Lastly, employing classic technologies associated with generalized $N$-Body solvers, including overdecomposition, recursive task parallelism, orderings that preserve locality, and persistence-based load balancing, we obtain scaling beyond hundreds of cores per molecule for small water clusters ([H${}_2$O]${}_N$, $N \in \{ 30, 90, 150 \}$, $P/N \approx \{ 819, 273, 164 \}$) and find support for an increasingly strong scalability with increasing system size $N$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Nonlocal and Mixed-Locality Multiscale Finite Element Methods

In many applications the resolution of small-scale heterogeneities remains a significant hurdle to robust and reliable predictive simulations. In particular, while material variability at the mesoscale plays a fundamental role in processes such as material failure, the resolution required to capture mechanisms at this scale is often computationally intractable. Multiscale methods aim to overcome this difficulty through judicious choice of a subscale problem and a robust manner of passing information between scales. One promising approach is the multiscale finite element method, which increases the fidelity of macroscale simulations by solving lower-scale problems that produce enriched multiscale basis functions. Here, in this study, we present the first work toward application of the multiscale finite element method to the nonlocal peridynamic theory of solid mechanics. This is achieved within the context of a discontinuous Galerkin framework that facilitates the description of material discontinuities and does not assume the existence of spatial derivatives. Analysis of the resulting nonlocal multiscale finite element method is achieved using the ambulant Galerkin method, developed here with sufficient generality to allow for application to multiscale finite element methods for both local and nonlocal models that satisfy minimal assumptions. Finally, we conclude with preliminary results on a mixed-locality multiscale finite element method in which a nonlocal model is applied at the fine scale and a local model at the coarse scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control

Herein, we present a new approach to using neural networks to approximate the solutions of variational equations, based on the adaptive construction of a sequence of finite-dimensional sub-spaces whose basis functions are realizations of a sequence of neural networks. Here, the finite-dimensional subspaces are then used to define a standard Galerkin approximation of the variational equation. This approach enjoys a number of advantages, including: the sequential nature of the algorithm offers a systematic approach to enhancing the accuracy of a given approximation; the sequential enhancements provide a useful indicator for the error that can be used as a criterion for terminating the sequential updates; the basic approach is largely oblivious to the nature of the partial differential equation under consideration; and, some basic theoretical results are presented regarding the convergence (or otherwise) of the method which are used to formulate basic guidelines for applying the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Online Adaptive Algorithm for Constraint Energy Minimizing Generalized Multiscale Discontinuous Galerkin Method

Here in this research, we propose an online basis enrichment strategy within the framework of a recently developed constraint energy minimizing generalized multiscale discontinuous Galerkin method. Combining the technique of oversampling, one makes use of the information of the current residuals to adaptively construct basis functions in the online stage to reduce the error of multiscale approximation. A complete analysis of the method is presented, which shows the proposed online enrichment leads to a fast convergence from multiscale approximation to the fine-scale solution. The error reduction can be made sufficiently large by suitably selecting oversampling regions and the number of oversampling layers. Further, the convergence rate of the enrichment algorithm depends on a factor of exponential decay regarding the number of oversampling layers and a user-defined parameter. Numerical results are provided to demonstrate the effectiveness and efficiency of the proposed online adaptive algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Convergence of Weak-SINDy Surrogate Models

In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of nonlinear system identification techniques, namely SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a linear system to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

CPU Volume Rendering of Adaptive Mesh Refinement Data

Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. Here, we demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Real-time capable modeling of neutral beam injection on NSTX-U using neural networks

A new model of heating, current drive, torque and other effects of neutral beam injection on NSTX-U that uses neural networks has been developed. The model has been trained and tested on the results of the Monte Carlo code NUBEAM for the database of experimental discharges taken during the first operational campaign of NSTX-U. By projecting flux surface quantities onto empirically derived basis functions, the model is able to efficiently and accurately reproduce the behavior of both scalars, like the total neutron rate and shine through, and profiles, like beam current drive and heating. The model has been tested on the NSTX-U real-time computer, demonstrating a rapid execution time orders of magnitude faster than the Monte Carlo code that is well suited for the iterative calculations needed to interpret experimental results, optimization during scenario development activities, and real-time plasma control applications. Simulation results of a proposed design for a nonlinear observer that embeds the neural network calculations to estimate the poloidal flux profile evolution, as well as effective charge and fast ion diffusivity, are presented.

Neural networks↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

Boyer, Mark↗
DOE OSTI2019

Hyperspectral Data Approximation by Gaussian Sigmoids

Finds LASSO fit coefficient values of hyperspectral data for a Gaussian sigmoid basis function collection using LARS (least angle regression) methodology on a provided vector of sensor wavelength bins.

Lanker, CoryL↗
DOE OSTI2022

DGTile

SAND2022-12898 O DGTile is a lightweight C++17 adaptive mesh library meant to support explicit discontinuous Galerkin applications on high performance computing machines. DGTile uses a block-based adaptive mesh refinement approach, where the underlying mesh data structure is an octree in three dimensions, where each leaf node of the tree represents a Cartesian grid. Over each grid, DGTile provides modal discontinuous Galerkin basis functions to facilitate simulations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Granzow, Brian↗
DOE OSTI2021

Benchmarking performance changes in the simulation of extratropical modes of variability across CMIP generations

In this work, we evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, 5, and 6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the qobserved Northern Annular Mode (NAM), North Atlantic Oscillation (NAO), Pacific North America pattern (PNA), Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a Common Basis Function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and High-top vertical coordinate), however the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the post-dominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious post-dominant season amplitude errors found in many models can be addressed in an atmospheric-only framework making it more tractable to address in the model development process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics

Fecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection and shows promise for treating other medical conditions associated with intestinal dysbioses. However, we lack a sufficient understanding of which microbial populations successfully colonize the recipient gut, and the widely used approaches to study the microbial ecology of FMT experiments fail to provide enough resolution to identify populations that are likely responsible for FMT-derived benefits. Here, we used shotgun metagenomics together with assembly and binning strategies to reconstruct metagenome-assembled genomes (MAGs) from fecal samples of a single FMT donor. We then used metagenomic mapping to track the occurrence and distribution patterns of donor MAGs in two FMT recipients. Our analyses revealed that 22% of the 92 highly complete bacterial MAGs that we identified from the donor successfully colonized and remained abundant in two recipients for at least 8 weeks. Most MAGs with a high colonization rate belonged to the order Bacteroidales. The vast majority of those that lacked evidence of colonization belonged to the order Clostridiales, and colonization success was negatively correlated with the number of genes related to sporulation. Our analysis of 151 publicly available gut metagenomes showed that the donor MAGs that colonized both recipients were prevalent, and the ones that colonized neither were rare across the participants of the Human Microbiome Project. Although our dataset showed a link between taxonomy and the colonization ability of a given MAG, we also identified MAGs that belong to the same taxon with different colonization properties, highlighting the importance of an appropriate level of resolution to explore the functional basis of colonization and to identify targets for cultivation, hypothesis generation, and testing in model systems. Lastly, the analytical strategy adopted in our study can provide genomic insights into bacterial populations that may be critical to the efficacy of FMT due to their success in gut colonization and metabolic properties, and guide cultivation efforts to investigate mechanistic underpinnings of this procedure beyond associations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A multiresolution adaptive wavelet method for nonlinear partial differential equations

We report the multiscale complexity of modern problems in computational science and engineering can prohibit the use of traditional numerical methods in multi-dimensional simulations. Therefore, novel algorithms are required in these situations to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. To meet these challenges, we present a multiresolution wavelet algorithm to solve PDEs with significant data compression and explicit error control. We discretize in space by projecting fields and spatial derivative operators onto wavelet basis functions. We provide error estimates for the wavelet representation of fields and their derivatives. Then, our estimates are used to construct a sparse multiresolution discretization which guarantees the prescribed accuracy. Additionally, we embed a predictor-corrector procedure within the temporal integration to dynamically adapt the computational grid and maintain the accuracy of the solution of the PDE as it evolves. We present examples to highlight the accuracy and adaptivity of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LaplaceInterpolation.jl: A Julia package for fast interpolation on a grid

We implement a linear-time algorithm for interpolation on a regular multidimensional grid in the Julia language. The algorithm is an approximate Laplace interpolation (Press, 1992) when no parameters are given; and when parameters m ∈ Z and ϵ > 0 are set, the interpolant approximates a Matérn kernel, of which radial basis functions and polyharmonic splines are a special case. We implement, in addition, Neumann, Dirichlet (trivial), and average boundary conditions with potentially different aspect ratios in the different dimensions. The interpolant functions in arbitrary dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

EMPHASIS/Nevada UTDEM user guide. Version 2.0.

The Unstructured Time-Domain ElectroMagnetics (UTDEM) portion of the EMPHASIS suite solves Maxwell's equations using finite-element techniques on unstructured meshes. This document provides user-specific information to facilitate the use of the code for applications of interest. UTDEM is a general-purpose code for solving Maxwell's equations on arbitrary, unstructured tetrahedral meshes. The geometries and the meshes thereof are limited only by the patience of the user in meshing and by the available computing resources for the solution. UTDEM solves Maxwell's equations using finite-element method (FEM) techniques on tetrahedral elements using vector, edge-conforming basis functions. EMPHASIS/Nevada Unstructured Time-Domain ElectroMagnetic Particle-In-Cell (UTDEM PIC) is a superset of the capabilities found in UTDEM. It adds the capability to simulate systems in which the effects of free charge are important and need to be treated in a self-consistent manner. This is done by integrating the equations of motion for macroparticles (a macroparticle is an object that represents a large number of real physical particles, all with the same position and momentum) being accelerated by the electromagnetic forces upon the particle (Lorentz force). The motion of these particles results in a current, which is a source for the fields in Maxwell's equations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Development Status of PROTEUS-MOC

The PROTEUS-MOC solver is intended to resolve the outstanding computational challenges that the PROTEUS-SN is limited by and bridge the gap between PROTEUS-SN and existing homogenized structured grid solvers. The primary push behind all of this work is to carry out coupled thermal-mechanical, fluid dynamics, and neutronics physics problems. The PROTEUS-MOC development has taken advantage of the combined experience with PROTEUS-SN and DeCART with the goal of producing a tool which combines the best features of both tools. Taking the DeCART tool as the model, it merges a subgroup cross section interpolation technique with a 2D method of characteristics approach and a 1D SN formulation. DeCART has been specifically demonstrated to be accurate on PWR and VHTR problems, but it has displayed several issues with regard to accuracy on thin axial mesh sizes which are attributable to the 2D1D scheme it is built upon. There are also notable problems with performance and scalability all of which we hope to resolve in PROTEUS-MOC. PROTEUS-MOC is at its core, based upon the success achieved with MOCFE which demonstrated excellent parallelization with respect to angle and good parallelization with respect to space. More recent efforts have indicated parallelization with respect to energy is also feasible. The work completed yielded a novel methodology where the axial basis functions are treated separately from the radial ones, but the geometry is extruded. This eliminates the need to carry out and store 3D ray tracing data and simultaneously allows the coarse axial node sizes typical with DeCART. To rapidly build a prototype, a standard power iteration algorithm was constructed using an unpreconditioned GMRES solution algorithm for solving the flux vector in each fission source iteration. The new PROTEUS-MOC solver was deployed on two test problems: Takeda and a heterogeneous cylindrical heavy water system. The Takeda benchmark problem is a standard homogeneous test problem which was previously found to be too difficult for the 3D MOCFE code to solve. Using the PROTEUS-MOC we were able to show space-angle convergence although full convergence requires further improvements in performance. The second benchmark problem was constructed more for future work than this year, but the results generated are promising indicating that it will be very useful in highlighting the weaknesses and strengths of the PROTEUS-MOC tool. While we are far from a production tool, the results included in this report give us confidence that we are on the right path to constructing an interceding code between the plethora of structured grid homogenized assembly solvers and the supercomputer consuming PROTEUS-SN code. With time, the creation of a suitable preconditioner along with typical optimization should reduce the performance shortcomings that are observable today.

Marin-Lafleche, A.↗