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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Advanced Characterisation of Aerosol Size Properties from Measurements of Spectral Optical Depth Using the GRASP Algorithm

This study evaluates the potential of using aerosol optical depth (τ (sub a)) measurements to characterise the microphysical and optical properties of atmospheric aerosols. With this aim, we used the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties) code for numerical testing of six different aerosol models with different aerosol loads. The direct numerical simulations (self-consistency tests) indicate that the GRASP-AOD retrieval provides modal aerosol optical depths (fine and coarse) to within 0.01 of the input values. The retrieval of the fine-mode radius, width and volume concentration are stable and precise if the real part of the refractive index is known. The coarse-mode properties are less accurate, but they are significantly improved when additional a priori information is available. The tests with random simulated errors show that the uncertainty in the bimodal log-normal size distribution parameters increases as the aerosol load decreases. Similarly, the reduction in the spectral range diminishes the stability of the retrieved parameters. In addition to these numerical studies, we used optical depth observations at eight AERONET locations to validate our results with the standard AERONET inversion products. We found that bimodal log-normal size distributions serve as useful input assumptions, especially when the measurements have inadequate spectral coverage and/or limited accuracy, such as moon photometry. Comparisons of the mode median radii between GRASP-AOD and AERONET indicate average differences of 0.013µm for the fine mode and typical values of 0.2–0.3µm for the coarse mode. The dominant mode (i.e. fine or coarse) indicates a 10 % difference in mode radii between the GRASP-AOD and AERONET inversions, and the average of the difference in volume concentration is around 17 % for both modes. The retrieved values of the fine-mode τ(sub a)(500) using GRASP-AOD are generally between those values obtained by the standard AERONET inversion and the values obtained by the AERONET spectral deconvolution algorithm (SDA), with differences typically lower than 0.02 between GRASP-AOD and both algorithms. Finally, we present some examples of application of GRASP-AOD inversion using moon photometry and the airborne PLASMA sun photometer during the ChArMEx summer 2013 campaign in the western Mediterranean.

Torres, Benjamin↗
DOE OSTI2020

Temperature estimation in a spatially inhomogeneous flame by diode laser absorption spectroscopy

A two-temperature (2T) model is proposed for estimating the temperature of a spatially inhomogeneous hot zone using single-beam diode laser absorption spectrometry. The proposed algorithm is based on fitting experimental absorption spectra by a linear combination of two database-simulated single-temperature spectra with different temperatures. The model efficiency is experimentally demonstrated when determining the temperature in different flame zones of a Wolfgard – Parker slot burner. The proposed 2T algorithm is used to find the maximum and minimum temperatures in different flame sections, which are compared with local temperatures in the same flame zones measured by the method of coherent anti-Stokes Raman scattering (CARS). It is shown that the maximum temperatures determined by the proposed 2T algorithm are in good agreement with CARS data in flame zones where high-temperature regions prevail. A good agreement between the minimum temperatures obtained by these two methods is also observed for predominantly cold zones. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2007

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Comiso, Josefino C.↗
NASA NTRS1977

Resampling study

The author has identified the following significant results. The nearest neighbor and cubic convolution resampling algorithms were applied to a variety of images extracted from LANDSAT MSS data. A comparison of the results demonstrated that (1) cubic convolution can cause spreading of small features and can introduce noticeable overshoot (ringing) into the data; (2) cubic convolution attenuates the high spatial frequencies compared to the original and nearest neighbor resampled data; and (3) cubic convolution generally produces photographic products of superior visual quality. The effects of the resampling algorithms on multispectral classification were not conclusively determined due to the small number of images tested.

Ferneyhough, D. G.↗
NASA NTRS1987

Numerical analysis of discrete fractional integrodifferential structural dampers

This paper develops solution algorithms enabling the handling of the dynamic response of nonlinear structures contained discretely attached dampers modeled by fractional integrodifferential operators of the Grunwald-Liouville-Riemann type. The development consists of two levels of formulation, namely: (1) numerical approximations of fractional operators and, (2) the establishment of global level implicit schemes enabling the solution to nonlinear structural formulations. To generalize the overall results, error estimates are derived for the fractional operator approximation algorithm. These enable an ongoing optimization of solution efficiency for a given error tolerance. To benchmark the scheme, the results of several numerical experiments are presented. These illustrate the numerical characteristics of the overall formulation.

Padovan, Joseph↗
DOE OSTI2023

ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs

Over the last decade merge trees have been proven to support a plethora of visualization and analysis tasks since they effectively abstract complex datasets. Here, this paper describes the ExTreeM-Algorithm: A scalable algorithm for the computation of merge trees via extremum graphs. The core idea of ExTreeM is to first derive the extremum graph G of an input scalar field f defined on a cell complex K, and subsequently compute the unaugmented merge tree of f on G instead of K; which are equivalent. Any merge tree algorithm can be carried out significantly faster on G, since K in general contains substantially more cells than G. To further speed up computation, ExTreeM includes a tailored procedure to derive merge trees of extremum graphs. The computation of the fully augmented merge tree, i.e., a merge tree domain segmentation of K, can then be performed in an optional post-processing step. All steps of ExTreeM consist of procedures with high parallel efficiency, and we provide a formal proof of its correctness. Our experiments, performed on publicly available datasets, report a speedup of up to one order of magnitude over the state-of-the-art algorithms included in the TTK and VTK-m software libraries, while also requiring significantly less memory and exhibiting excellent scaling behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. 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.

SciDAC↗
NASA NTRS1988

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗
NASA NTRS1989

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗
NASA NTRSDate not supplied

Active Thermography Based on Tensor Rank Decomposition

Principal Component Thermography applies Singular Value Decomposition (SVD) to post-process data that are derived from active thermographic inspections. SVD provides useful compression of the data and allows for better understanding of substructure and indications of potential damage. In the standard approach, SVD is applied to a certain reshaping of a three-dimensional data stack into a two-dimensional array. This work applies the CANDECOMP-PARAFAC (CP) tensor rank decomposition directly to the three-dimensional data to avoid the initial reshaping step in order to begin to develop an inspection method that can more accurately detect defects in non-homogeneous and anisotropic materials. Tests against simulated data that compare the CP decomposition method with traditional Principal Component Thermography based on SVD are described. Finally, the method of Proper Generalized Decomposition (PGD) is used to derive the CP decomposition, and its performance against other algorithms is also discussed.

Thermography↗
DOE OSTI2021

Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind

Here, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

17 WIND ENERGY↗
DOE OSTI2022

Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Abstract Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Estimation of electric fields and current from ground-based magnetometer data

Recent advances in numerical algorithms for estimating ionospheric electric fields and currents from groundbased magnetometer data are reviewed and evaluated. Tests of the adequacy of one such algorithm in reproducing large-scale patterns of electrodynamic parameters in the high-latitude ionosphere have yielded generally positive results, at least for some simple cases. Some encouraging advances in producing realistic conductivity models, which are a critical input, are pointed out. When the algorithms are applied to extensive data sets, such as the ones from meridian chain magnetometer networks during the IMS, together with refined conductivity models, unique information on instantaneous electric field and current patterns can be obtained. Examples of electric potentials, ionospheric currents, field-aligned currents, and Joule heating distributions derived from ground magnetic data are presented. Possible directions for future improvements are also pointed out.

Kamide, Y.↗
NASA NTRS1994

Spacecraft attitude determination accuracy from mission experience

This paper summarizes a compilation of attitude determination accuracies attained by a number of satellites supported by the Goddard Space Flight Center Flight Dynamics Facility. The compilation is designed to assist future mission planners in choosing and placing attitude hardware and selecting the attitude determination algorithms needed to achieve given accuracy requirements. The major goal of the compilation is to indicate realistic accuracies achievable using a given sensor complement based on mission experience. It is expected that the use of actual spacecraft experience will make the study especially useful for mission design. A general description of factors influencing spacecraft attitude accuracy is presented. These factors include determination algorithms, inertial reference unit characteristics, and error sources that can affect measurement accuracy. Possible techniques for mitigating errors are also included. Brief mission descriptions are presented with the attitude accuracies attained, grouped by the sensor pairs used in attitude determination. The accuracies for inactive missions represent a compendium of missions report results, and those for active missions represent measurements of attitude residuals. Both three-axis and spin stabilized missions are included. Special emphasis is given to high-accuracy sensor pairs, such as two fixed-head star trackers (FHST's) and fine Sun sensor plus FHST. Brief descriptions of sensor design and mode of operation are included. Also included are brief mission descriptions and plots summarizing the attitude accuracy attained using various sensor complements.

Brasoveanu, D.↗
DOE OSTI2024

Pressurized-Water Reactor Core Design using Multi-Objective Plant Fuel Reload Optimization Platform

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimization of core design. The optimization of the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible options of core configuration, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization (PRLO) platform which supports artificial-intelligence-based reactor core designing is now fully capable of handling realistic problems. The PRLO Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual Environment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multi-objective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

42 ENGINEERING↗
NASA NTRS1995

Robust integration schemes for generalized viscoplasticity with internal-state variables. Part 1: Theoretical developments and applications

This two-part report is concerned with the development of a general framework for the implicit time-stepping integrators for the flow and evolution equations in generalized viscoplastic models. The primary goal is to present a complete theoretical formulation, and to address in detail the algorithmic and numerical analysis aspects involved in its finite element implementation, as well as to critically assess the numerical performance of the developed schemes in a comprehensive set of test cases. On the theoretical side, the general framework is developed on the basis of the unconditionally-stable, backward-Euler difference scheme as a starting point. Its mathematical structure is of sufficient generality to allow a unified treatment of different classes of viscoplastic models with internal variables. In particular, two specific models of this type, which are representative of the present start-of-art in metal viscoplasticity, are considered in applications reported here; i.e., fully associative (GVIPS) and non-associative (NAV) models. The matrix forms developed for both these models are directly applicable for both initially isotropic and anisotropic materials, in general (three-dimensional) situations as well as subspace applications (i.e., plane stress/strain, axisymmetric, generalized plane stress in shells). On the computational side, issues related to efficiency and robustness are emphasized in developing the (local) interative algorithm. In particular, closed-form expressions for residual vectors and (consistent) material tangent stiffness arrays are given explicitly for both GVIPS and NAV models, with their maximum sizes 'optimized' to depend only on the number of independent stress components (but independent of the number of viscoplastic internal state parameters). Significant robustness of the local iterative solution is provided by complementing the basic Newton-Raphson scheme with a line-search strategy for convergence. In the present first part of the report, we focus on the theoretical developments, and discussions of the results of numerical-performance studies using the integration schemes for GVIPS and NAV models.

Saleeb, Atef F.↗
NASA NTRS2011

Actuator Grouping Optimization on Flexible Space Reflectors

With the rapid advances in deployable membrane and mesh antenna technologies, the feasibility of developing large, lightweight reflectors has greatly improved. In order to achieve the required surface accuracy, precision surface control is needed on these lightweight reflectors. For this study, an analytical model is shown which combines a flexible Kapton reflector with Polyvinylidene fluoride (PVDF) actuators for surface control. Surface errors are introduced that are similar to real world scenarios, and a least squares control algorithm is developed for surface control. Experimental results on a 2.4 meter reflector show that while the analytical reflector model is generally correct, due to idiosyncrasies in the reflector it cannot be used for online control. A new method called the En Mass Elimination algorithm is used to determine the optimal grouping of actuators when the number of actuators in the system exceeds the number of power supplies available.

en masse elimination (EME) algorithm↗
NASA NTRS1986

Analyzing Multidimensional Image Data

Six computer programs perform histogram cluster analysis. Histogram Cluster Analysis Procedure (HICAP) developed to perform unsupervised classification of multidimensional image data. Clustering approach used in HICAP based on algorithm which uses multidimensional histogram to perform unsupervised classification of four-dimensional Landsat multispectral-scanner data. HICAP generalizes this procedure to process up to 32-bit data with arbitrary number of dimensions. Also incorporates efficiency improvements so classification requires less computation than original algorithm. Computational savings afforded by HICAP increase with number of dimensions in data. HICAP programs written in FORTRAN 77 for batch or interactive execution.

Wharton, S. W.↗