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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Modeling longitudinal INMA(1) with COM–Poisson innovation under non-stationarity: application to medical data

This paper introduces an observation-driven (OD) longitudinal integer-valued moving average model of order 1 (INMA(1)) with COM–Poisson innovations under non-stationary moment conditions. This new longitudinal model provides lot of practical flexibility in terms of modeling a wide range of over-, under-dispersion and any mixed level of dispersion. In this set-up, the model parameters of primary interest consist of the regression and dispersion effects while the serial autocorrelation parameters are treated as nuisance. A robust Generalized Quasi-Likelihood approach is formulated to estimate the different set of parameters. The performance of the estimating algorithm is assessed via Monte Carlo experiments under various combination of the serial and dispersion values and is compared with the existing adaptive generalized method of moments. Application to two medical data: epileptic seizures and polyposis counts are also illustrated.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

A comprehensive comparison between satellite-measured skin and multichannel sea surface temperature

Three algorithms for computing sea-surface temperature (SST) from AVHRR channel-4 and -5 brightness temperatures were compared using 1-week blocks of global-area-coverage (GAC) data archived at NCAR. These are the multichannel SST (MCSST), the cross-product SST (CPSST), and the AVHRR-only satellite-measured ocean-surface skin temperature (SMSST). Maps of each SST product were produced and were compared for various time periods. The difference between the algorithms range from +1.6 to -1.2 K, resulting from the different forms of both the algorithms and the ground truth from which the algorithms were derived. It is concluded that, for the study of relative SST patterns, any of the algorithms can be used. However, for applications requiring accurate absolute temperature measurements, the differences are significant and the derivation of the algorithm must be considered when an algorithm is selected.

Wick, Gary A.↗
DOE OSTI2018

A multigrid waveform relaxation method for solving the poroelasticity equations

In this work, a multigrid waveform relaxation method is proposed for solving a collocated finite difference discretization of the linear Biot’s model. This gives rise to the first space–time multigrid solver for poroelasticity equations in the literature. The waveform relaxation iteration is based on a point-wise Vanka smoother that couples the pressure variable at a grid-point with the displacements around it. A semi-algebraic mode analysis is proposed to theoretically analyze the convergence of the multigrid waveform relaxation algorithm. This analysis is novel since it combines the semi-algebraic analysis, suitable for parabolic problems, with the non-standard analysis for overlapping smoothers. The practical utility of the method is illustrated through several numerical experiments in one and two dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Safeguards-Informed Hybrid Imagery Dataset [Poster]

Deep Learning computer vision models require many thousands of properly labelled images for training, which is especially challenging for safeguards and nonproliferation, given that safeguards-relevant images are typically rare due to the sensitivity and limited availability of the technologies. Creating relevant images through real-world staging is costly and limiting in scope. Expert-labeling is expensive, time consuming, and error prone. We aim to develop a data set of both realworld and synthetic images that are relevant to the nuclear safeguards domain that can be used to support multiple data science research questions. In the process of developing this data, we aim to develop a novel workflow to validate synthetic images using machine learning explainability methods, testing among multiple computer vision algorithms, and iterative synthetic data rendering. We will deliver one million images – both real-world and synthetically rendered – of two types uranium storage and transportation containers with labelled ground truth and associated adversarial examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

On Computation of Koopman Operator from Sparse Data

In this paper, we propose a novel approach to compute the Koopman operator from sparse time series data. In recent years there has a considerable interest in operator theoretic methods for data-driven analysis of dynamical systems and existing techniques for the approximation of the Koopman operator require rich enough data-sets. However, in many applications, the data set may not be rich enough to approximate the operators to acceptable limits. In this paper, using ideas from robust optimization, we propose an algorithm to compute the Koopman operator from sparse data. In particular, we enrich the sparse data set with artificial data points and use robust optimization techniques to obtain the transfer operator. We illustrate the efficiency of our proposed approach in three different dynamical systems, namely, a linear system, a nonlinear system and a dynamical system governed by a partial differential equation.

Sinha, Subhrajit↗
NASA NTRS1979

Density models for the upper atmosphere

Three contemporary atmospheric density models which are candidates for meeting the current orbit computation requirements are compared. The models considered are the analytic Jacchia-Roberts model, the modified Harris-Priester model, and the USSR Cosmos satellite derived density model. All of these models can be said to be quasi-dynamic representations of the atmospheric density. The time dependent variations in the model density profiles are determined by both the evaluation of explicit continuous functions of time and by the input of time varying parameters to algorithms. The computation characteristics of each of the models are compared and a modification to the modified Harris-Priester model is proposed which improves its ability to represent the diurnal variation in the atmospheric density.

Dowd, D. L.↗
DOE OSTI2018

Localized Pisot Matrices and Joint Approximations of Algebraic Numbers

A development of the simplex-module algorithm for expansion of algebraic numbers in multidimensional continued fractions is proposed. To this end, localized Pisot matrices are constructed, whose eigenvalues with moduli less than one are contained in an interval of small length. Such Pisot matrices generate continued fractions whose convergents are arbitrarily close to the best approximations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

A Modified Algorithm and Open-Source Computational Package for the Determination of Infrared Optical Constants Relevant to Astrophysics

Infrared (IR) telescopes, such as Spitzer and SOFIA, have revealed a rich variety of chemical species trapped in interstellar ices. The most fundamental parameters to be derived from observed IR spectra are the identity and abundance of each component. Several compounds have been conclusively or tentatively identified, but the band strengths and optical constants needed to derive accurate abundances for many of these are poorly constrained. We have developed a modified approach to the extraction of the real and imaginary parts of the refractive index (optical constants) of a thin film from a single transmission spectrum measured in the IR spectral range. Our algorithm is similar to those implemented by previous authors, with some major changes that yield results for strong absorptions where previous approaches fail: (1) an adaptive k-correction step size, (2) the use of a root-finding algorithm to obtain a more accurate k-correction at each iteration, and (3) a k-correction step that prevents non-physical results such as negative n-values that prevent convergence in the calculation algorithm. The algorithm is presented and described, with examples to show agreement with some existing results and improvements upon others. New optical-constants calculations for CH3OH, CO2, N2O, and CH4 are presented, and potential implications for the modeling of interstellar and planetary ice data from space telescopes are discussed. With the objective of being open-source and transparent, the full source code in the free Python programming language is made available along with the compiled version and the laboratory data used to produce the results shown.

Perry A. Gerakines↗
DOE OSTI2018

SciDAC – PSI: Plasma Surface Interactions involving Tungsten - A Summary of Project Accomplishments

Gaining physics understanding and predictive capabilities to describe the evolution of plasma facing components (PFC) requires simultaneously addressing complex and diverse physics occurring over a wide range of length and time scales, as well as integrating extensive physical processes across the plasma - surface - bulk materials boundaries. This requires development not only of detailed physics models and computational strategies at each of these scales, but computer science algorithms and methods to strongly couple them in a way that can be robustly validated through comparisons against available data and new experiments. Therefore, the objective of this project is to develop robust, high-fidelity simulation tools capable of predicting the PFC operating lifetime and the PFC impact on plasma contamination, recycling of hydrogenic species, and tritium retention in future magnetic fusion devices, with a focus on tungsten based material systems. Deploying these tools requires the development of a leadershipscale computational code, as well as a host of simulations that span the multiple scales needed to address complex physical and computational issues at the plasma - surface interface and the transition below the surface where neutron damage processes in the bulk material dominate behavior in multiple-component materials systems. Successful development will enable improved prediction of PFC performance needed to ensure magnetic fusion energy development beyond ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Recent advances in 3D SRF cavity-based quantum computing facility at the Fermilab SQMS Center

This talk will describe the most recent advances and progress in building an SRF cavity-based quantum computing facility at the Fermilab SQMS center. Several technical challenges had been successfully overcome to preserve the highest quality factors of SRF cavities in the presence of the sapphire chip holding the transmon qubit inside the cavity. The record values of the attained multicell cavity (2-cell, 9-cell)-qubit systems will be presented, as well as a variety of quantum operations realized with such systems, including the generation of large Fock states, coherent states, entangled mode-mode states etc. The quantum algorithms and computational problems being implemented on this 3D SQMS QPUs will be discussed as well.

Romanenko, Alexander [Fermilab]↗
NASA NTRS1988

Parallel computer methods for eigenvalue extraction

A new numerical algorithm for the solution of large-order eigenproblems typically encountered in linear elastic finite element systems is presented. The architecture of parallel processing is used in the algorithm to achieve increased speed and efficiency of calculations. The algorithm is based on the frontal technique for the solution of linear simultaneous equations and the modified subspace eigenanalysis method for the solution of the eigenproblem. The advantages of this new algorithm in parallel computer architecture are discussed.

Akl, Fred↗
DOE OSTI2018

On Two Algorithms of Wavelet Decomposition for Spaces of Linear Splines

The purpose of this paper is to construct new types of wavelets for minimal splines on an irregular grid. The approach applied to construct spline-wavelet decompositions uses approximation relations as an initial structure for constructing the spaces of minimal splines. The advantages of this approach are the possibilities of using irregular grids and sufficiently arbitrary nonpolynomial spline-wavelets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Novel Multiagent Resource Sharing Algorithm for Control of Advanced Energy Systems

This paper implements a novel resource sharing control strategy on a fuel cell–gas turbine hybrid power system at the National Energy Technology Laboratory’s Hybrid Performance Facility (Hyper). In a fuel cell–gas turbine hybrid power system, the simultaneous interaction of the gas turbine and the fuel cell creates a tightly coupled environment characterized by conflicting dynamics. In this paper, a model-free control approach is applied to solve the tightly coupled control problem posed by this challenging environment. Specifically, this control problem is presented as a resource sharing problem that can be solved using a resource sharing algorithm that is defined based on the distribution construction concept. This algorithm creates computational agents and solves the problem through the distribution and redistribution of shared resources defined as blocks. Furthermore, two agents were created; the first agent (agent 1) controls the gas turbine speed by adjusting the electric load, and the second agent (agent 2) controls the cathode mass flow through the fuel cell using the cold-air bypass valve. A parametric study was performed over the course of 15 experimental tests for both agents 1 and 2 through an evaluation of the responses based on setpoint changes. The algorithm was shown to have behavior comparable to a previously implemented multi-input multioutput state-space controller, which was designed through a model-based control approach. The resource sharing algorithm was able to find stable performance during run-time operations without any prior system knowledge identification on the power plant and without creating models used in traditional control strategies.

25 ENERGY STORAGE↗
NASA NTRS1981

Contextual classification of multispectral image data

A general method is presented for exploiting both spatial and spectral information when classifying multispectral image data. This statistical classification algorithm utilizes the tendency of certain ground cover classes to be more likely to occur in some contexts than others. The theoretical model assumes the two-dimensional array of random observations and a 0-1 loss function, a distribution of the p-context array that is spatially invariant, and class-conditional independence for the observations. The problems that prevent the immediate use of this context classifier are the need for a generally applicable method for making adequate estimates of the context distribution and a reduction in the computational intensivity of the classifier. The former problem is being approached by a method that raises the relative frequency value for each class configuration to a power and uses the result as the context distribution estimate. The second is being approached by searching for a less computationally intensive algorithm.

Tilton, J. C.↗
DOE OSTI2018

Modified algorithms for image inpainting in Fourier transform domain

One of the aims of image inpainting is recovering an image some of which Fourier transform coefficients are lost. In this area, the algorithm of iterative coupled transform domain (ICTDI) has been given by Li and Zeng (SIAM J Imaging Sci 9:24–51, 2016). In this paper, we present some modified algorithms of ICTDI and prove their convergence. In fact, we consider the effect of spectrum and phase angle of the Fourier transform, separately. Therefore, in comparison with ICTDI, one more regularization parameter is generated, and hence, we have more degree of freedom, and therefore, in general, we expect a more appropriate solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Hari, Gautam↗
DOE OSTI2025

Computing virtual dark-field X-ray microscopy images of complex discrete dislocation structures from large-scale molecular dynamics simulations

Dark-field X-ray microscopy (DFXM) is a novel diffraction-based imaging technique that non-destructively maps the local deformation from crystalline defects in bulk materials. While studies have demonstrated that DFXM can spatially map 3D defect geometries, it is still challenging to interpret DFXM images of the high-dislocation-density systems relevant to macroscopic crystal plasticity. This work develops a scalable forward model to calculate virtual DFXM images for complex discrete dislocation structure(s) (DDS) obtained from atomistic simulations. Our new DDS-DFXM model integrates a non-singular formulation for calculating the local strain from the DDS and an efficient geometrical optics algorithm for computing the DFXM image from the strain field. We apply the model to complex DDS obtained from a large-scale mol­ecular dynamics simulation of compressive loading on single-crystal silicon. Simulated DFXM images exhibit prominent contrast for dislocation features between the multiple slip systems, demonstrating the potential of DFXM to resolve features from dislocation multiplication. In conclusion, the integrated DDS-DFXM model provides a toolbox for DFXM experimental design and image interpretation in the context of bulk crystal plasticity for a range of measurements across shock plasticity and the broader materials science community.

X-ray imaging↗