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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CP decomposition for tensors via alternating least squares with QR decomposition

The CP tensor decomposition is used in applications such as machine learning and signal processing to discover latent low-rank structure in multidimensional data. Computing a CP decomposition via an alternating least squares (ALS) method reduces the problem to several linear least squares problems. The standard way to solve these linear least squares subproblems is to use the normal equations, which inherit special tensor structure that can be exploited for computational efficiency. However, the normal equations are sensitive to numerical ill-conditioning, which can compromise the results of the decomposition. In this paper, we develop versions of the CP-ALS algorithm using the QR decomposition and the singular value decomposition, which are more numerically stable than the normal equations, to solve the linear least squares problems. Our algorithms utilize the tensor structure of the CP-ALS subproblems efficiently, have the same complexity as the standard CP-ALS algorithm when the input is dense and the rank is small, and are shown via examples to produce more stable results when ill-conditioning is present. Our MATLAB implementation achieves the same running time as the standard algorithm for small ranks, and we show that the new methods can obtain lower approximation error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Error estimates to smooth solutions of semi-discrete discontinuous Galerkin methods with quadrature rules for scalar conservation laws

Here, we focus on error estimates to smooth solutions of semi-discrete discontinuous Galerkin (DG) methods with quadrature rules for scalar conservation laws. The main techniques we use are energy estimate and Taylor expansion first introduced by Zhang and Shu in (Zhang and Shu, SIAM J Num Anal 42 (2004), 641–666). We show that, with p K (piecewise polynomials of degree k) finite elements in 1D problems, if the quadrature over elements is exact for polynomials of degree (2k) error estimates of O(h k+1/2 ) are obtained for general monotone fluxes, and optimal estimates of O(h k+1 ) are obtained for upwind fluxes. For multidimensional problems, if in addition quadrature over edges is exact for polynomials of degree (2k+1), error estimates of O(h k ) are obtained for general monotone fluxes, and O(h k+1/2 ) are obtained for monotone and sufficiently smooth numerical fluxes. Numerical results validate our analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Viral Dynamic Models During COVID‐19: Are We Ready for the Next Pandemic?

Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A high-order WENO-limited finite-volume algorithm for atmospheric flow using the ADER-differential transform time discretization

A high-order-accurate weighted essentially non-oscillatory (WENO) limited upwind finite-volume scheme is detailed for the compressible, nonhydrostatic, inviscid Euler equations using an arbitrary derivatives (ADER) time-stepping scheme based on differential transforms (DTs). A second-order-accurate alternating Strang dimensional splitting is compared against multidimensional simulation with 2D transport using solid body rotation of various data. The two were found to give nearly identical accuracy in orthogonal, Cartesian coordinates. Orders of convergence are demonstrated at up to ninth-order accuracy with 2D transport. 1D transport is used to confirm that error decreases monotonically with increasing order of accuracy with WENO limiting even for discontinuous data. Further, WENO limiting always decreased the error compared with simulation without limiting in the L 1 norm. A series of standard 2D compressible nonhydrostatic Euler equation test cases were validated against previous results from literature. Finally, it was demonstrated that increasing the order of accuracy led to better resolved features and increased power for kinetic energy at small wavelengths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Materials data analytics for 9% Cr family steel

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron–base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first–order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition–based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p–norm distance measures, matching the alloy classification groups adopted by industry. Lastly, the evolutionary method of propagating an ensemble of competing cluster–based models proved to be a viable option in dealing with scarce, multidimensional data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Gearbox high-speed-stage bearing slip induced by electric excitation in a test facility

The goal of this paper is to investigate the behaviour of the high-speed stage of a wind turbine gearbox during transient grid-loss events triggered at different power levels in a full-scale nacelle dynamometer test. The gear mesh behaviour and the bearing roller slip during these events are characterized by strain measurements placed in the gear teeth roots and embedded in the outer rings of the tapered roller bearing locating pair. From our experiments, we conclude that the high-speed stage experiences a multidimensional loading consisting of bending and torsional deformation during the transient event. This loading propagates throughout the gearbox and results in unfavourable loading conditions at the bearing that have the potential to go beyond the preload during the torque reversals that occur during the grid-loss event. During the torque reversal, roller slip can occur in the bearings. It is observed that the slip behaviour is directly linked to the initial power level of the generator. Electrically induced excitation that typically leads to dynamic torque events can therefore cause mechanical gearbox degradation.

17 WIND ENERGY↗
DOE OSTI2016

Gas Sorption and Storage Properties of Calixarenes

Calixarenes, a class of organic macrocyclic molecules have shown interesting gas sorption properties towards industrially important gases such as carbon di-oxide, hydrogen, methane and acetylene. These macrocycles are involved in weak van der Waals interaction to form multidimensional supramolecular frameworks. The gas-diffusion and subsequent sorption occurs due to a cooperative behavior between neighboring macrocycles. Furthermore, the flexibility at the upper rim functional group also plays a key role in the overall gas uptake of calixarene. In this book chapter, we give a brief account of interaction and diffusion of gases in calixarene and selected derivatives.

Patil, Rahul S.↗
DOE OSTI2018

Adaptive Sparse Grid Construction in a Context of Local Anisotropy and Multiple Hierarchical Parents

We consider general strategy for hierarchical multidimensional interpolation based on sparse grids, where the interpolation nodes and locally supported basis functions are constructed from tensors of a one dimensional hierarchical rule. We consider four different hierarchies that are tailored towards general functions, high or low order polynomial approximation, or functions that satisfy homogeneous boundary conditions. The main advantage of the locally supported basis is the ability to choose a set of functions based on the observed behavior of the target function. We present an alternative to the classical surplus refinement techniques, where we exploit local anisotropy and refine using functions with not strictly decreasing support. The more flexible refinement strategy improves stability and reduces the total number of expensive simulations, resulting in significant computational saving. We demonstrate the advantages of the different hierarchies and refinement techniques by application to series of simple functions as well as a system of ordinary differential equations given by the Kermack-McKendrick SIR model.

Stoyanov, Miroslav K.↗
DOE OSTI2019

Ultrafast Spectroscopy of Hydrogenase Enzyme Models

Hydrogenase enzymes are nature’s solution to using molecular hydrogen as an energy source. While the air-sensitivity of these biological catalysts make them impractical for industrial hydrogen generation, synthetic mimics show promise in acting as effective substitutes. Hydrogenase model compounds also enable detailed investigation of the ultrafast dynamics and chemical reaction mechanisms using ultrafast infrared spectroscopy. This chapter reviews the progress in applying ultrafast transient infrared absorption and multidimensional spectroscopy to a range of small molecule hydrogenase model compounds, as well as several macromolecular and larger constructs. The rich vibrational structure and straightforward chemical modularity of these diiron compounds represent an exciting class of molecules that are, at the same time, excellent model systems for fundamental chemical dynamics, and practical molecular catalytic components with direct application to next-generation hydrogen-based energy strategies.

Eckert, Peter↗
DOE OSTI2016

The Early Exercise Premium Representation for American Options on Multiply Assets

In the paper we consider the problem of valuation of American options written on dividend-paying assets whose price dynamics follow the classical multidimensional Black and Scholes model. We provide a general early exercise premium representation formula for options with payoff functions which are convex or satisfy mild regularity assumptions. Examples include index options, spread options, call on max options, put on min options, multiply strike options and power-product options. In the proof of the formula we exploit close connections between the optimal stopping problems associated with valuation of American options, obstacle problems and reflected backward stochastic differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multivariate network traffic analysis using clustered patterns

Traffic analysis is a core element in network operations and management for various purposes including change detection, traffic prediction, and anomaly detection. In this paper, we introduce a new approach to online traffic analysis based on a pattern-based representation for high-level summarization of the traffic measurement data. Unlike the past online analysis techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the observed patterns from previous time windows enabling intuitive analysis. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility. Our extensive experimental results with public traces and collected monitoring measurements from ESnet traffic traces show that our pattern-based approach is effective for multivariate analysis of online network traffic with visual and quantitative tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Application of Information Technologies at the Final Stage of the Lifecycle of Reactor Facilities

The information technologies used at the final lifecycle stage of reactor facilities are reviewed for the purpose of increasing the safety and efficiency of their decommissioning. Databases on decommissioning and radwaste management, computer-aided design systems, multidimensional models, interactive simulation models, knowledge control systems, and communication systems are examined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

The Absence of the Selfaveraging Property of the Entanglement Entropy of Disordered Free Fermions in One Dimension

We consider the macroscopic system of free lattice fermions in one dimension assuming that the one-body Hamiltonian of the system is the one dimensional discrete Schrödinger operator with independent identically distributed random potential. We show analytically and numerically that the variance of the entanglement entropy of the segment [−M,M] of the system is bounded away from zero as M→∞. This manifests the absence of the selfaveraging property of the entanglement entropy in our model, meaning that in the one-dimensional case the complete description of the entanglement entropy is provided by its whole probability distribution. This also may be contrasted the case of dimension two or more, where the variance of the entanglement entropy per unit surface area vanishes as M→∞ [8], thereby guaranteeing the representativity of its mean for large M in the multidimensional case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Low-Dimensional and Multi-Dimensional Pendulums in Nonconservative Fields. Part 1

In this review, we discuss new cases of integrable systems on the tangent bundles of finitedimensional spheres. Such systems appear in the dynamics of multidimensional rigid bodies in nonconservative fields. These problems are described by systems with variable dissipation with zero mean. We found several new cases of integrability of equations of motion in terms of transcendental functions (in the sense of the classification of singularities) that can be expressed as finite combinations of elementary functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗