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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Cross-correlation of CMB Polarization Lensing with High-$z$ Submillimeter $Herschel$-ATLAS Galaxies

Herein we report a 4.8σ measurement of the cross-correlation signal between the cosmic microwave background (CMB) lensing convergence reconstructed from measurements of the CMB polarization made by the P OLARBEAR experiment and the infrared-selected galaxies of the $Herschel$-ATLAS survey. This is the first measurement of its kind. We infer a best-fit galaxy bias of $b$ = 5.76 ± 1.25, corresponding to a host halo mass of log 10 (M h /M ⊙ ). =13.5$^{+0.2}_{-0.3}$ at an effective redshift of $z$ ~ 2 from the cross-correlation power spectrum. Residual uncertainties in the redshift distribution of the submillimeter galaxies are subdominant with respect to the statistical precision. We perform a suite of systematic tests, finding that instrumental and astrophysical contaminations are small compared to the statistical error. This cross-correlation measurement only relies on CMB polarization information that, differently from CMB temperature maps, is less contaminated by galactic and extragalactic foregrounds, providing a clearer view of the projected matter distribution. This result demonstrates the feasibility and robustness of this approach for future high-sensitivity CMB polarization experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

Relationship between voids and interlaminar shear strength of polymer matrix composites

The effect of voids on the interlaminar shear strength of a polyimide matrix composite system is described. The AS4 graphite/PMR-15 composite was chosen for study because this system can be readily processed by using the standard specified cure cycle to produce void-free composites and because preliminary work in this study had shown that the processing parameters of this resin matrix system can be altered to produce cured composites of varying void contents. Thirty-eight 12-ply unidirectional composite panels were fabricated for this study. A significant range of void contents (0 to 10 percent) was produced. The panels were mapped, ultrasonically inspected, and sectioned into interlaminar shear, flexure, and fiber content specimens. The density of each specimen was measured and interlaminar shear and flexure strength measurements were then made. The fiber content was measured last. The results of these tests were evaluated by using ultrasonic results, photomicrographs, statistical methods, theoretical relationships derived by other investigators, and comparison of the test data with the Integrated Composite Analyzer (ICAN) computer program developed at the Lewis Research Center for predicting composite ply properties. The testing is described in as much detail as possible in order to help others make realistic comparisons.

Bowles, Kenneth J.↗
NASA NTRS1991

Relationship between voids and interlaminar shear strength of polymer matrix composites

The effect of voids on the interlaminar shear strength of a polyimide matrix composite system is described. The AS4 graphite/PMR-15 composite was chosen for study because this system can be readily processed by using the standard specified cure cycle to produce void-free composites and because preliminary work in this study had shown that the processing parameters of this resin matrix system can be altered to produce cured composites of varying void contents. Thirty-eight 12-ply unidirectional composite panels were fabricated for this study. A significant range of void contents (0 to 10 percent) was produced. The panels were mapped, ultrasonically inspected, and sectioned into interlaminar shear, flexure, and fiber content specimens. The density of each specimen was measured and interlaminar shear and flexure strength measurements were then made. The fiber content was measured last. The results of these tests were evaluated by using ultrasonic results, photomicrographs, statistical methods, theoretical relationships derived by other investigations, and comparison of the test data with the Integrated Composite Analyzer (ICAN) computer program developed at the Lewis Research Center for predicting composite ply properties. The testing is described in as much detail as possible in order to help others make realistic comparisons.

Bowles, Kenneth J.↗
NASA NTRS2010

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

Kirschbaum, Dalia B.↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2020

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
NASA NTRS1979

Performance characteristics and statistics of a laser initiated microdetonator

The test results of 320 units of the laser initiated microdetonator are summarized. The commercially fabricated units used a lead styphnate/lead azide/HMX (1 mg/17.5 mg/13.5 mg) explosive train design contained in a miniature aluminum can and completed with a glass-metal seal window. The test parameters were the high and low laser energy, high and low temperature, laser pulse duration, laser wavelength and nuclear radiation (5 x 10 to the 6th rads of 1 MeV gamma ray). The performance parameters were the laser energy for ignition and the actuation response time. Included also is a description of the development of a flexible, continuously advanced train of explosive devices by using the units, miniature optics and fiber optics.

Yang, L. C.↗
DOE OSTI2017

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1994

Is a data set distributed as a power law? A test, with application to gamma-ray burst brightnesses

We present a method to determine whether an observed sample of data is drawn from a parent distribution that is pure power law. The method starts from a class of statistics which have zero expectation value under the null hypothesis, H(sub 0), that the distribution is a pure power law: F(x) varies as x(exp -alpha). We study one simple member of the class, named the `bending statistic' B, in detail. It is most effective for detection a type of deviation from a power law where the power-law slope varies slowly and monotonically as a function of x. Our estimator of B has a distribution under H(sub 0) that depends only on the size of the sample, not on the parameters of the parent population, and is approximated well by a normal distribution even for modest sample sizes. The bending statistic can therefore be used to test a set of numbers is drawn from any power-law parent population. Since many measurable quantities in astrophysics have distriibutions that are approximately power laws, and since deviations from the ideal power law often provide interesting information about the object of study (e.g., a `bend' or `break' in a luminosity function, a line in an X- or gamma-ray spectrum), we believe that a test of this type will be useful in many different contexts. In the present paper, we apply our test to various subsamples of gamma-ray burst brightness from the first-year Burst and Transient Source Experiment (BATSE) catalog and show that we can only marginally detect the expected steepening of the log (N (greater than C(sub max))) - log (C(sub max)) distribution.

Wijers, Ralph A. M. J.↗
DOE OSTI2018

Quickest Detection for Changes in Maximal kNN Coherence of Random Matrices

This paper addresses the problem of quickest detection of a change in the maximal coherence between columns of a n p random matrix based on a sequence of matrix observations having a single unknown change point. The random matrix is assumed to have identically distributed rows and the maximal coherence is defined as the largest of the ( 2 p) correlation coefficients associated with any row. Likewise the κ nearest neighbor (kNN) coherence is defined as the κ-th largest of these correlation coefficients. The forms of the preand postchange distributions of the observed matrices are assumed to belong to the family of elliptically contoured densities with sparse dispersion matrices but are otherwise unknown. A nonparametric stopping rule is proposed that is based on the maximal kNN sample coherence between columns of each observed random matrix. This is a summary statistic that is related to a test of existence of a hub vertex in a sample correlation graph having degree at least κ. Performance bounds on the delay and false alarm performance of the proposed stopping rule are obtained in the purely high-dimensional regime where p → ∞ and n is fixed. When the prechange dispersion matrix is diagonal it is shown that, among all functions of the proposed summary statistic, the proposed stopping rule is asymptotically optimal under a minimax quickest change detection model as the stopping threshold approaches infinity. Furthermore, the theory developed also applies to sequential hypothesis testing and fixed sample size tests.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Visibility of Hypovascularized Liver Tumors during Intra-Arterial Therapy Using Split-Bolus Single-Phase Cone Beam CT

Purpose: To validate a split-bolus contrast injection protocol for single-phase CBCT in terms of detectability of hypovascular liver tumors compared to digital subtraction angiography (DSA). Materials and Methods: In this retrospective, single-center study, 20 consecutive patients with in total 77 hypovascularized tumors referred for intra-arterial therapy received a split-bolus single-phase CBCT. Two readers rated the visibility of the target tumors scheduled for embolization in CBCT and DSA compared to the pre-interventional multiphasic CT or MRI used as reference on a 3-point scoring system (1 = optimal, 3 = not visible) and catheter-associated artifacts (1 = none, 3 = extended). SNR, CNR and contrast values were derived from 37 target tumors in CBCT and MRI. Statistical analysis included the kappa test to determine interrater reliability, the Friedman’s test for the inter-modality comparison evaluating tumor visibility in DSA and CBCT as well as for quantitative assessment. Post hoc analysis included the Wilcoxon signed-rank test. p values < 0.05 were considered significant. Results: Ninety percentage of target tumors were rated as visible in CBCT and 37.5% in DSA (p < 0.001). 70.1% of pre-interventionally detected hypovascularized tumors were depicted with CBCT and 31.2% by DSA (p < 0.001). 7.8% of known tumors were outside the FOV. Quantitative assessment showed higher image contrasts in CBCT (1.91 ± 7.01) compared to hepatobiliary-phase MRI (0.29 ± 0.14, p = 0.003) and to portal-venous (p.v.) MRI (0.31 ± 0.13, p < 0.001), but higher CNR for MRI (1.18 ± 0.80; 13.92 ± 15.82; 13.79 ± 6.65). Conclusion: In conclusion, the split-bolus single-phase CBCT detects significantly more hypovascularized liver tumors compared to DSA performed through the proper hepatic artery with high image contrasts. Level of Evidence: Level III, diagnostic study.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Evolution of damage and failure in an additively manufactured 316L SS structure: experimental reinvestigation of the third Sandia fracture challenge

The third Sandia Fracture Challenge (SFC3) was a benchmark problem for comparing experimental and simulated ductile deformation and failure in an additively manufactured (AM) 316L stainless steel structure. One surprising observation from the SFC3 was the Challenge-geometry specimens had low variability in global load versus displacement behavior, attributed to the large stress-concentrating geometric features dominating the global behavior, rather than the AM voids that tend to significantly influence geometries with uniform cross-sections. This current study reinvestigates the damage and failure evolution of the Challenge-geometry specimens, utilizing interrupted tensile testing with micro-computed tomography (micro-CT) scans to monitor AM void and crack growth from a virgin state through complete failure. This study did not find a correlation between global load versus displacement behavior and AM void attributes, such as void volume, location, quantity, and relative size, which incidentally corroborates the observation from the SFC3. However, this study does show that the voids affect the local behavior of damage and failure. Surface defects (i.e. large voids located on the surface, far exceeding the nominal surface roughness) that were near the primary stress concentration affected the location of crack initiation in some cases, but they did not noticeably affect the global response. The fracture surfaces were a combination of classic ductile dimples and crack deviation from a more direct path favoring intersection with AM voids. Even though the AM voids promoted crack deviation, pre-test micro-CT scan statistics of the voids did not allow for conclusive predictions of preferred crack paths. This study is a first step towards investigating the importance of voids on the ductile failure of AM structures with stress concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Speleogenesis, geometry, and topology of caves: A quantitative study of 3D karst conduits

Karst systems are hierarchically spatially organized three-dimensional (3D) networks of conduits behaving as drains for groundwater flow. Recently, geostatistical approaches proposed to generate karst networks from data and parameters stemming from analogous observed karst features. Other studies have qualitatively highlighted relationships between speleogenetic processes and cave patterns. However, few studies have been performed to quantitatively define these relationships. This paper reports a quantitative study of cave geometries and topologies that takes the underlying speleogenetic processes into account. In order to study the spatial organization of caves, a 3D numerical database was built from 26 caves, corresponding to 621 km of cumulative cave passages representative of the variety of karst network patterns. The database includes 3D speleological surveys for which the speleogenetic context is known, allowing the polygenic karst networks to be divided into 48 monogenic cave samples and classified into four cave patterns: vadose branchwork (VB), water-table cave (WTC), looping cave (LC), and angular maze (AM). Eight morphometric cave descriptors were calculated, four geometrical parameters (width-height ratio, tortuosity, curvature, and vertical index) and four topological ones (degree of node connectivity, α and γ graph indices, and ramification index) respectively. The results were validated by statistical analyses (Kruskal-Wallis test and PCA). The VB patterns are clearly distinct from AM ones and from a third group including WTC and LC. A quantitative database of cave morphology characteristics is provided, depending on their speleogenetic processes. These characteristics can be used to constrain and/or validate 3D geostatistical simulations. This study shows how important it is to relate the geometry and connectivity of cave networks to recharge and flow processes. Conversely, the approach developed here provides proxies to estimate the evolution of the vadose zone to epiphreatic and phreatic zones in limestones from the quantitative analysis of existing cave patterns.

58 GEOSCIENCES↗
DOE OSTI2023

A data driven approach for cross-slip modelling in continuum dislocation dynamics

Cross-slip is a thermally activated process by which screw dislocation changes its glide plane to another slip plane sharing the same Burgers vector. The rate at which this process happens is determined by a Boltzmann type expression that is a function of the screw segment length and the stress acting on the dislocation. In continuum dislocation dynamics (CDD), the information regarding the length of the screw dislocation segment and local stress state on dislocations are lost due to the coarse-grained representation of the density. Here, in this work, a data driven approach to characterize the lost information by analyzing the discrete dislocation configurations is proposed to enable cross-slip modeling in the CDD framework in terms of the coarse-grained dislocation density and stress fields. The analysis showed that the screw segment length follows an exponential distribution, and the stress fluctuations, defined as the difference between the stress on the dislocations and the mean field stress in CDD, follows a Lorentzian distribution. A novel approach for cross slip implementation in CDD employing the screw segment length and stress fluctuation statistics was proposed and rigorously tested by comparing the CDD cross-slip rates with discrete dislocation dynamics (DDD) rates. This approach has been applied in conjunction with three cross-slip models used in DDD simulations differing mainly in the functional form of cross slip activation energy. It was found that different cross-slip activation energy formulations yielded different cross-slip rates, yet the effect on mechanical stress-strain response and dislocation density evolution was minimal for the [001] type loading.

42 ENGINEERING↗
DOE OSTI2016

Understanding the bias introduced in quantum dot blinking using change point analysis

Quantum dots are semiconductor crystals that are less than 100 nanometers in size; that is, between 1 billionth and 100 billionths of a meter. They can be composed of one semiconductor material or a core of one semiconductor surrounded by a shell of another. The quantum dots studied in this work consisted of a cadmium selenide core surrounded by a cadmium sulfide shell. When a quantum dot is illuminated with light, some of it can be absorbed and excites the quantum dot to a higher energy state. To relax back down to its resting state, light can be emitted in the form of fluorescence. The nature of the fluorescence that is emitted from a single quantum dot differs from that which is emitted from a bulk sample (usually a solid film or a solution of quantum dots). While in bulk samples the fluorescence over time fluctuates randomly about some average, maintaining a continuous average intensity, single quantum dot fluorescence switches between two or more intensities. This phenomenon is referred to as quantum dot blinking, and its origin is of significant relevance to solar cell, light emitting diode and biological tracking applications. Quantum dot blinking can be studied by exciting an isolated quantum dot with light and recording the ensuing fluorescence using sensitive detectors. This is done over many minutes, resulting in a fluorescence intensity (in counts per second) versus time trajectory. These trajectories must be further analyzed in order to understand the nature of the blinking behavior. Conventionally, a bin and threshold analysis is used, whereby the arrival times of fluorescent photons are binned to reduce the signal noise, and a threshold is used to separate the trajectory into periods of high and low fluorescence. In this study, the effects of bin size and threshold on the blinking kinetics were investigated, and the assumption that there is only one bright and one dark state was tested. To do so, a statistically rigorous method called change point analysis was needed which does not require binning and which can determine the number of intensity levels present. Blinking in 17 quantum dots was studied using both analysis methods. The primary parameter that was compared between the two methods is called a truncation time. Increasing the bin size was found to generally lead to a larger truncation time while increasing the threshold always decreased the truncation time. Using the change point analysis method, 12 out of 17 quantum dots were found to exhibit more than two intensity levels. Lastly, the assumption that only two intensity levels exist was found to significantly affect the blinking statistics: when individual levels were combined into one bright level, the truncation time would increase. The change point analysis method was found to be more suitable for studying multilevel blinking in quantum dots than the conventional bin and threshold method, since the effects of binning and thresholding can be alleviated and the two-level assumption relaxed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗