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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 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2017

A near-stationary subspace for ridge approximation

Response surfaces are common surrogates for expensive computer simulations in engineering analysis. However, the cost of fitting an accurate response surface increases exponentially as the number of model inputs increases, which leaves response surface construction intractable for high-dimensional, nonlinear models. Here we describe ridge approximation for fitting response surfaces in several variables. A ridge function is constant along several directions in its domain, so fitting occurs on the coordinates of a low-dimensional subspace of the input space. We review essential theory for ridge approximation – e.g., the best mean-squared approximation and an optimal low-dimensional subspace – and we prove that the gradient-based active subspace is near-stationary for the least-squares problem that defines an optimal subspace. Motivated by the theory, we propose a computational heuristic that uses an estimated active subspace as an initial guess for a ridge approximation fitting problem. We show a simple example where the heuristic fails, which reveals a type of function for which the proposed approach is inappropriate. We then propose a simple alternating heuristic for fitting a ridge function, and we demonstrate the effectiveness of the active subspace initial guess applied to an airfoil model of drag as a function of its 18 shape parameters.

42 ENGINEERING↗

DOE OSTI2015

3D RISM theory with fast reciprocal-space electrostatics

The calculation of electrostatic solute-solvent interactions in 3D RISM (“three-dimensional reference interaction site model”) integral equation theory is recast in a form that allows for a computational treatment analogous to the “particle-mesh Ewald” formalism as used for molecular simulations. In addition, relations that connect 3D RISM correlation functions and interaction potentials with thermodynamic quantities such as the chemical potential and average solute-solvent interaction energy are reformulated in a way that calculations of expensive real-space electrostatic terms on the 3D grid are completely avoided. These methodical enhancements allow for both, a significant speedup particularly for large solute systems and a smoother convergence of predicted thermodynamic quantities with respect to box size, as illustrated for several benchmark systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Design Paradigm for Modular Multilevel Converter-Based Generator Rectifier Systems

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

42 ENGINEERING↗

DOE OSTI2014

Assessing the standard Molybdenum projector augmented wave VASP potentials

Density Functional Theory (DFT) based Equation of State (EOS) construction is a prominent part of Sandia’s capabilities to support engineering sciences. This capability is based on augmenting experimental data with information gained from computational investigations, especially in those parts of the phase space where experimental data is hard, dangerous, or expensive to obtain. A key part of the success of the Sandia approach is the fundamental science work supporting the computational capability. Not only does this work enhance the capability to perform highly accurate calculations but it also provides crucial insight into the limitations of the computational tools, providing high confidence in the results even where results cannot be, or have not yet been, validated by experimental data. This report concerns the key ingredient of projector augmented-wave (PAW) potentials for use in pseudo-potential computational codes. Using the tools discussed in SAND2012-7389 we assess the standard Vienna Ab-initio Simulation Package (VASP) PAWs for Molybdenum.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Design of a reading test for low vision image warping

NASA and the University of Houston College of Optometry are examining the efficacy of image warping as a possible prosthesis for at least two forms of low vision - maculopathy and retinitis pigmentosa. Before incurring the expense of reducing the concept to practice, one would wish to have confidence that a worthwhile improvement in visual function would result. NASA's Programmable Remapper (PR) can warp an input image onto arbitrary geometric coordinate systems at full video rate, and it has recently been upgraded to accept computer-generated video text. We have integrated the Remapper with an SRI eye tracker to simulate visual malfunction in normal observers. A reading performance test has been developed to determine if the proposed warpings yield an increase in visual function; i.e., reading speed. We will describe the preliminary experimental results of this reading test with a simulated central field defect with and without remapped images.

Loshin, David S.↗

DOE OSTI2013

Determination of Optimal Operating Schemes for a Multi-Reservoir System Under Environmental Constraints

In strati ed reservoirs, both dam tailwater discharge and thermal plant intake water quality and temperature can be highly dependent on structure depth. A two- dimensional laterally-averaged model allows for better prediction of water quality over time at specfi c depths. Because high- fidelity models are typically too computationally expensive for direct inclusion within optimization algorithms, water quality is incorporated using one dimensional models are simple flow requirements. Water quality predictions can be incorporated within the optimization process through using surrogate modeling methods, in this application artificial neural network (ANN) models. ANNs are flexible machine learning tools for function approximation composed of a structure of neurons assembled within a multi-layer architecture. They are capable of handling large amounts of training data and modeling nonlinear dynamic systems, making ANNs a well-suited method for this application. This report illustrates the development of ANN models to emulate the hydrodynamic and water quality modeling capabilities of the high- fidelity, two-dimensional CE-QUAL-W2 (W2) model, as well as a linked riverine reservoir system optimization process which accounts for energy generation, water balance and hydraulics, and compliance point water quality. A process for hourly hydropower generation planning is demonstrated on a pair of reservoirs linked in series. The two reservoirs are U.S. Army Corps of Engineers projects with hydropower capabilities on the Cumberland River near Nashville, Tennessee, USA.

13 HYDRO ENERGY↗

DOE OSTI2017

Why wait? Let's start computing while data is still on the wire

In this era of Big Data, computing useful information from data is becoming increasingly complicated, particularly due to the ever increasing volumes of data that need to travel over the network to data centers to be stored and processed, all highly expensive operations in the long haul. In this paper we suggest that we can do computing and analysis of data "on the wire," i.e., while data is still in transit. The nature of these computations include analysis, visualization, pattern recognition, and prediction, or forecasting, on the streaming data. We follow a service function chaining architecture to implement this, assuming that the data packets arrive within a single network administrative domain. As a demonstration of this new computing paradigm, we present three examples. Firstly, we demonstrate pattern recognition and data visualization on streaming forex data, which can be used for lucrative trading in the forex market. In our second example, we analyze and learn user buying patterns from clickstream data streaming from multiple websites. Finally, we monitor solar sensors for a zero reading while the packets are still on their way to the data center, to schedule any maintenance and requisite repairs with no time delay.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗

DOE OSTI2021

Davis Computational Spectroscopy Workflow—From Structure to Spectra

Here, we describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ~100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

Alternative communication network designs for an operational Plato 4 CAI system

The cost of alternative communications networks for the dissemination of PLATO IV computer-aided instruction (CAI) was studied. Four communication techniques are compared: leased telephone lines, satellite communication, UHF TV, and low-power microwave radio. For each network design, costs per student contact hour are computed. These costs are derived as functions of student population density, a parameter which can be calculated from census data for one potential market for CAI, the public primary and secondary schools. Calculating costs in this way allows one to determine which of the four communications alternatives can serve this market least expensively for any given area in the U.S. The analysis indicates that radio distribution techniques are cost optimum over a wide range of conditions.

Mobley, R. E., Jr.↗

DOE OSTI2024

Distinguishing homolytic vs heterolytic bond dissociation of phenylsulfonium cations with localized active space methods

Modeling chemical reactions with quantum chemical methods is challenging when the electronic structure varies significantly throughout the reaction and when electronic excited states are involved. Multireference methods, such as complete active space self-consistent field (CASSCF), can handle these multiconfigurational situations. However, even if the size of the needed active space is affordable, in many cases, the active space does not change consistently from reactant to product, causing discontinuities in the potential energy surface. The localized active space SCF (LASSCF) is a cheaper alternative to CASSCF for strongly correlated systems with weakly correlated fragments. The method is used for the first time to study a chemical reaction, namely the bond dissociation of a mono-, di-, and triphenylsulfonium cation. LASSCF calculations generate smooth potential energy scans more easily than the corresponding, more computationally expensive CASSCF calculations while predicting similar bond dissociation energies. Furthermore, our calculations suggest a homolytic bond cleavage for di- and triphenylsulfonium and a heterolytic pathway for monophenylsulfonium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Improved reference system for the corrected rigid spheres equation of state model

The Corrected Rigid Spheres (CRIS) EOS model, developed from fluid perturbation theory using a hard sphere reference system, has been successfully used to calculate the EOS of many materials, including gases and metals. The radial distribution function (RDF) plays a pivotal role in choosing the sphere diameter, through a variational principle, as well as the thermodynamic response. Despite its success, the CRIS model has some shortcomings in that it predicts too large a temperature for liquid-vapor critical points, can break down at large compression, and is computationally expensive. In this paper, we first demonstrate that an improved analytic representation of the hard sphere RDF does not alleviate these issues. Relaxing the strict adherence of the RDF to hard spheres allows an accurate fit to the isotherms and vapor dome of the Lennard-Jones fluid using an arbitrary reference system. Second order correction are eliminated, limiting the breakdown at large compression and significantly reducing the computation cost. The transferrability of the new model to real systems is demonstrated on argon, with an improved vapor dome compared to the original CRIS model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel Directed Targeted Gene Characterization in Sorghum (Final Technical Report)

Understanding which genes control important crop traits could help scientists develop better bioenergy and food crops more efficiently. However, plant genomes contain tens of thousands of genes, and testing each one individually is expensive and time-consuming. This project developed computational tools to predict which genes are most likely to matter, allowing researchers to focus their efforts where they will have the greatest impact. This project developed and validated integrated approaches combining machine learning, quantitative genetics, and crop growth modeling to improve the efficiency of functional gene characterization in sorghum (Sorghum bicolor), a critical bioenergy and food security crop. The research addressed a fundamental challenge in plant biology: the majority of genes in plant genomes lack experimentally validated functions, making it difficult to prioritize which genes to study using resource-intensive reverse genetics approaches.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1994

Parallel implicit unstructured grid Euler solvers

A mesh-vertex finite volume scheme for solving the Euler equations on triangular unstructured meshes is implemented on a multiple-instruction/multiple-data stream parallel computer. An explicit four-stage Runge-Kutta scheme is used to solve two-dimensional flow problems. A family of implicit schemes is also developed to solve these problems, where the linear system that arises at each time step is solved by a preconditioned GMRES algorithm. Two partitioning strategies are employed: one that partitions triangles and the other that partitions vertices. The choice of the preconditioner in a distributed memory setting is discussed. All of the methods are compared both in terms of elapsed times and convergence rates. It is shown that the implicit schemes offer adequate parallelism at the expense of minimal sequential overhead. The use of a global coarse grid to further minimize this overhead is also investigated. The schemes are implemented on a distributed memory parallel computer, the Intel iPSC/860.

TRT-THEORETICAL↗

DOE OSTI2022

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [32], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Lastly, support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Analysis and Entropy Stability of the Line-Based Discontinuous Galerkin Method

We develop a discretely entropy-stable line-based discontinuous Galerkin method for hyperbolic conservation laws based on a flux differencing technique. By using standard entropy-stable and entropy-conservative numerical flux functions, this method guarantees that the discrete integral of the entropy is non-increasing. This nonlinear entropy stability property is important for the robustness of the method, in particular when applied to problems with discontinuous solutions or when the mesh is under-resolved. This line-based method is significantly less computationally expensive than a standard DG method. Numerical results are shown demonstrating the effectiveness of the method on a variety of test cases, including Burgers’ equation and the Euler equations, in one, two, and three spatial dimensions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A reactive molecular dynamics model for uranium/hydrogen containing systems

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Multilevel algorithms for nonlinear optimization

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Alexandrov, Natalia↗