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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Application of the vortex-lattice technique to the analysis of thin wings with vortex separation and thick multi-element wings

Two techniques for extending the range of applicability of the basic vortex-lattice method are discussed. The first improves the computation of aerodynamic forces on thin, low-aspect-ratio wings of arbitrary planforms at subsonic Mach numbers by including the effects of leading-edge and tip vortex separation, characteristic of this type wing, through use of the well-known suction-analogy method of E. C. Polhamus. Comparisons with experimental data for a variety of planforms are presented. The second consists of the use of the vortex-lattice method to predict pressure distributions over thick multi-element wings (wings with leading- and trailing-edge devices). A method of laying out the lattice is described which gives accurate pressures on the top and part of the bottom surface of the wing. Limited comparisons between the result predicted by this method, the conventional lattice arrangement method, experimental data, and 2-D potential flow analysis techniques are presented.

Smith, C. W.↗
NASA NTRS1981

Comments on settling chamber design for quiet, blowdown wind tunnels

Transfer of an existing continous circuit supersonic wind tunnel to Langley and its operation there as a blowdown tunnel is planned. Flow disturbance requirements in the supply section and methods for reducing the high level broad band acoustic disturbances present in typical blowdown tunnels are reviewed. Based on recent data and the analysis of two blowdown facilities at Langley, methods for reducing the total turbulence levels in the settling chamber, including both acoustic and vorticity modes, to less than one percent are recommended. The pertinent design details of the damping screens and honeycomb and the recommended minimum pressure drop across the porous components providing the required two orders of magnitude attenuation of acoustic noise levels are given. A suggestion for the support structure of these high pressure drop porous components is offered.

Beckwith, I. E.↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Characterization of ceramic powders by an X-ray measuring method

X-ray line broadening analysis gives quantitative data on structural changes of ceramic powders after different processing steps. Various Al2O3 powders were investigated and the following points are discussed on the basis of these results: X-ray line broadening analysis, structural changes during grinding, structural changes during annealing, influence of structural properties on sintering behavior and application of line broadening analysis to quality control of powders.

Ziegler, B.↗
NASA NTRS1991

Doppler indices of gas phase formation in hypobaric environments: Time-intensity analysis

A semi-quantitative method to analyze decompression data is described. It possesses the advantage that it allows a graded response to decompression rather than the dichotomous response generally employed. A generalized critical volume (C-V), or stoichiometric time-dependent equilibrium model is examined that relates the constant of the equation P sub i equals m P sub f plus b to variable tissue supersaturation and gas washout terms. The effects of the tissue ratio on gas phase formation indicate that a decreased ratio yields fewer individuals with Doppler detectable gas bubbles, but those individuals still present with Spencer Grade 3 or 4. This might indicate a local collapse of tissue saturation. The individuals with Grade 3 or 4 could be at risk for type 2 decompression sickness by transpulmonic arterialization. The primary regulator of the problems of decompression sickness is the reduction of local supersaturation, presumably governed by the presence and number of gas micronuclei. It is postulated that a reduction in these nuclei will favor a low incidence of decompression sickness in microgravity secondary to hypokinesia and adynamia.

Powell, Michael R.↗
DOE OSTI2016

New ORNL Method Could Unleash Solar Power Potential

Measurement and data analysis techniques developed at the Department of Energy’s Oak Ridge National Laboratory could provide new insight into performance-robbing flaws in crystalline structures, ultimately improving the performance of solar cells.

14 SOLAR ENERGY↗
DOE OSTI2016

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Inventory Uncertainty Quantification using TENDL Covariance Data in Fispact-II

The new inventory code Fispact-II provides predictions of inventory, radiological quantities and their uncertainties using nuclear data covariance information. Central to the method is a novel fast pathways search algorithm using directed graphs. The pathways output provides (1) an aid to identifying important reactions, (2) fast estimates of uncertainties, (3) reduced models that retain important nuclides and reactions for use in the code's Monte Carlo sensitivity analysis module. Described are the methods that are being implemented for improving uncertainty predictions, quantification and propagation using the covariance data that the recent nuclear data libraries contain. In the TENDL library, above the upper energy of the resolved resonance range, a Monte Carlo method in which the covariance data come from uncertainties of the nuclear model calculations is used. The nuclear data files are read directly by FISPACT-II without any further intermediate processing. Variance and covariance data are processed and used by FISPACT-II to compute uncertainties in collapsed cross sections, and these are in turn used to predict uncertainties in inventories and all derived radiological data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data

In this report, we present preliminary research into nonparametric clustering methods for multi-source imagery data and quantifying the performance of these models. In many domain areas, data sets do not necessarily follow well-defined and well-known probability distributions, such as the normal, gamma, and exponential. This is especially true when combining data from multiple sources describing a common set of objects (which we call multimodal analysis), where the data in each source can follow different distributions and need to be analyzed in conjunction with one another. This necessitates nonparametric density estimation methods, which allow the data to better dictate the distribution of the data. One prominent example of multimodal analysis is multimodal image analysis, when we analyze multiple images taken using different radar systems of the same scene of interest. We develop uncertainty analysis methods, which are inherent in the use of probabilistic models but often not taken advance of, to assess the performance of probabilistic clustering methods used for analyzing multimodal images. This added information helps assess model performance and how much trust decision-makers should have in the obtained analysis results. The developed methods illustrate some ways in which uncertainty can inform decisions that arise when designing and using machine learning models.

42 ENGINEERING↗
NASA NTRS2002

Development of an Efficient Binaural Simulation for the Analysis of Structural Acoustic Data

Binaural or "virtual acoustic" representation has been proposed as a method of analyzing acoustic and vibroacoustic data. Unfortunately, this binaural representation can require extensive computer power to apply the Head Related Transfer Functions (HRTFs) to a large number of sources, as with a vibrating structure. This work focuses on reducing the number of real-time computations required in this binaural analysis through the use of Singular Value Decomposition (SVD) and Equivalent Source Reduction (ESR). The SVD method reduces the complexity of the HRTF computations by breaking the HRTFs into dominant singular values (and vectors). The ESR method reduces the number of sources to be analyzed in real-time computation by replacing sources on the scale of a structural wavelength with sources on the scale of an acoustic wavelength. It is shown that the effectiveness of the SVD and ESR methods improves as the complexity of the source increases. In addition, preliminary auralization tests have shown that the results from both the SVD and ESR methods are indistinguishable from the results found with the exhaustive method.

Lalime, Aimee L.↗
DOE OSTI2014

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

In the past several years, several international organizations have begun to collect data on human performance in nuclear power plant simulators. The data collected provide a valuable opportunity to improve human reliability analysis (HRA), but these improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this paper, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Federal policy documentation and geothermal water consumption: Policy gaps and needs

With U.S. geothermal power production expected to more than triple by 2040, and the majority of this growth expected to occur in arid and water-constrained areas, it is imperative that decision-makers understand the potential long-term limitations to and tradeoffs of geothermal development due to water availability. To this end, water consumption data, including documentation triggered by the National Environmental Policy Act (NEPA) of 1969, production and injection data, and water permit data, were collected from state and federal environmental policy sources in an effort to determine water consumption across the lifecycle of geothermal power plants. Values extracted from these sources were analyzed to estimate water usage during well drilling; to identify sourcing of water for well drilling, well stimulation, and plant operations; and to estimate operational water usage at the plant level. Nevada data were also compared on a facility-by-facility basis with other publicly available water consumption data, to create a complete picture of water usage and consumption at these facilities. This analysis represents a unique method of capturing project-level water data for geothermal projects; however, a lack of statutory and legal requirements for such data and data quality result in significant data gaps, which are also explored

Schroeder, J. N.↗
DOE OSTI2016

The Regional Test Center Data Transfer System

The Regional Test Centers are a group of several sites around the US for testing photovoltaic systems and components related to photovoltaic systems. The RTCs are managed by Sandia National Laboratories. The data collected by the RTCs must be transmitted to Sandia for storage, analysis, and reporting. This document describes the methods that transfer the data between remote sites and Sandia as well as data movement within Sandia’s network. The methods described are in force as of September, 2016.

14 SOLAR ENERGY↗
DOE OSTI2023

Dimensionality reduction using elastic measures

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. Here, in this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Determining the Optimal Number of Clusters with the Clustergram

Cluster analysis aids research in many different fields, from business to biology to aerospace. It consists of using statistical techniques to group objects in large sets of data into meaningful classes. However, this process of ordering data points presents much uncertainty because it involves several steps, many of which are subject to researcher judgment as well as inconsistencies depending on the specific data type and research goals. These steps include the method used to cluster the data, the variables on which the cluster analysis will be operating, the number of resulting clusters, and parts of the interpretation process. In most cases, the number of clusters must be guessed or estimated before employing the clustering method. Many remedies have been proposed, but none is unassailable and certainly not for all data types. Thus, the aim of current research for better techniques of determining the number of clusters is generally confined to demonstrating that the new technique excels other methods in performance for several disparate data types. Our research makes use of a new cluster-number-determination technique based on the clustergram: a graph that shows how the number of objects in the cluster and the cluster mean (the ordinate) change with the number of clusters (the abscissa). We use the features of the clustergram to make the best determination of the cluster-number.

Fluegemann, Joseph K.↗
DOE OSTI2016

A NEW METHOD FOR FINDING POINT SOURCES IN HIGH-ENERGY NEUTRINO DATA

The IceCube collaboration has reported the first detection of high-energy astrophysical neutrinos, including ∼50 high-energy starting events, but no individual sources have been identified. It is therefore important to develop the most sensitive and efficient possible algorithms to identify the point sources of these neutrinos. The most popular current method works by exploring a dense grid of possible directions to individual sources, and identifying the single direction with the maximum probability of having produced multiple detected neutrinos. This method has numerous strengths, but it is computationally intensive and because it focuses on the single best location for a point source, additional point sources are not included in the evidence. We propose a new maximum likelihood method that uses the angular separations between all pairs of neutrinos in the data. Unlike existing autocorrelation methods for this type of analysis, which also use angular separations between neutrino pairs, our method incorporates information about the point-spread function and can identify individual point sources. We find that if the angular resolution is a few degrees or better, then this approach reduces both false positive and false negative errors compared to the current method, and is also more computationally efficient up to, potentially, hundreds of thousands of detected neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Light Water Reactor Sustainability Program Industry Application External Hazard Analyses Problem Statement

Risk-Informed Margin Management Industry Application on External Events. More specifically, combined events, seismically induced external flooding analyses for a generic nuclear power plant with a generic site soil, and generic power plant system and structure. The focus of this report is to define the problem above, set up the analysis, describe the methods to be used, tools to be applied to each problem, and data analysis and validation associated with the above.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1984

Towards the objective analysis of clouds from satellite imagery data

It is suspected that clouds play a major role in climate dynamics. However, conclusive studies regarding the effects related to the cloud cover appear difficult because there is a lack of objective data. The present investigation is concerned with an objective scheme for deriving clouds and their properties from satellite imagery data for the oceans. The objective analysis makes use of the spatial coherence method for retrieving cloud cover from satellite imagery data. This method has advantages over other techniques often applied to imagery data. It is not necessary that clouds fill completely the observing instrument's field-of-view, and a priori or satellite derived knowledge of the cloud radiative properties is not needed.

Coakley, J. A., Jr.↗