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At least 541 records · Page 30

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DOE OSTI2014

Case depth verification of hardened samples with Barkhausen noise sweeps

An interesting topic of recent Barkhausen noise (BN) method studies is the application of the method to case depth evaluation of hardened components. The utilization of BN method for this purpose is based on the difference in the magnetic properties between the hardened case and the soft core. Thus, the detection of case depth with BN can be achieved. The measurements typically have been carried out by using low magnetizing frequencies which have deeper penetration to the ferromagnetic samples than the conventional BN measurement. However, the penetration depth is limited due to eddy current damping of the signal. We introduce here a newly found sweep measurement concept for the case depth evaluation. In this study sweep measurements were carried out with various magnetizing frequencies and magnetizing voltages to detect the effect of different frequency and voltage and their correspondence to the actual case depth values verified from destructive characterization. Also a BN measurement device that has an implemented sweep analysis option was utilised. The samples were either induction or case-hardened samples and sample geometry contained both rod samples and gear axle samples with different case depth values. Samples were also further characterized with Xray diffraction to study the residual stress state of the surface. The detailed data processing revealed that also other calculated features than the maximum slope division of the 1st derivative of the BN signal could hold the information about the case depth value of the samples. The sweep method was able to arrange the axles into correct order according to the case depth value even though the axles were used.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Study on effect of annealing conditions on structural, magnetic and superconducting properties of MgB{sub 2} bulk samples

Effect of annealing conditions on structural, magnetic and superconducting properties of Magnesium Diboride (MgB{sub 2}) bulk superconductor samples prepared by solid state route method are compared. The samples are made by taking Magnesium and Boron powders in stoichiometric ratio, grounded well and pelletized at pressure of about 10Tonnes. These pellets are annealed in both Argon and vacuum environment separately up to 800°c for two hours. Both the samples show clear superconducting transition at Tc ∼ 38 k. This is further conformed by AC/DC magnetization (M-T), Resistivity [ρ (T, H)] measurements under magnetic field up to 14 Tesla as well. Rietveld refinement of X-ray diffraction of both samples conformed the MgB{sub 2} phase formation with P6/mmm space group symmetry. Scanning Electron Microscopy images of the surface revile more agglomeration of grains in case of Argon annealed samples. This result in more critical current density (J{sub c}) of Argon annealed samples than vacuum annealed one calculated from Bean's critical state model. This high Jc is explained in terms of more inter grain connectivity for Argon annealed sample than vacuum annealed sample.

36 MATERIALS SCIENCE↗

DOE OSTI2016

A whole-path importance-sampling scheme for Feynman path integral calculations of absolute partition functions and free energies

Using Feynman path integrals, a molecular partition function can be written as a double integral with the inner integral involving all closed paths centered at a given molecular configuration, and the outer integral involving all possible molecular configurations. In previous work employing Monte Carlo methods to evaluate such partition functions, we presented schemes for importance sampling and stratification in the molecular configurations that constitute the path centroids, but we relied on free-particle paths for sampling the path integrals. At low temperatures, the path sampling is expensive because the paths can travel far from the centroid configuration. In this paper, we now present a scheme for importance sampling of whole Feynman paths based on harmonic information from an instantaneous normal mode calculation at the centroid configuration, which we refer to as harmonically guided whole-path importance sampling (WPIS). We obtain paths conforming to our chosen importance function by rejection sampling from a distribution of free-particle paths. Sample calculations on CH 4 demonstrate that at a temperature of 200 K, about 99.9% of the free-particle paths can be rejected without integration, and at 300 K, about 98% can be rejected. Finally, we also show that it is typically possible to reduce the overhead associated with the WPIS scheme by sampling the paths using a significantly lower-order path discretization than that which is needed to converge the partition function.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2018

Quantitative comparison of adaptive sampling methods for protein dynamics

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Evaluating the effect of sample type on American alligator ( Alligator mississippiensis ) analyte values in a point-of-care blood analyser

The assessment of wildlife health has been enhanced by the ability of point-of-care (POC) blood analysers to provide biochemical analyses of non-domesticated animals in the field. However, environmental limitations (e.g. temperature, atmospheric humidity and rain) and lack of reference values may inhibit researchers from using such a device with certain wildlife species. Evaluating the use of alternative sample types, such as plasma, in a POC device may afford researchers the opportunity to delay sample analysis and the ability to use banked samples. In this study, we examined fresh whole blood, fresh plasma and frozen plasma (sample type) pH, partial pressure of carbon dioxide (PCO 2 ), bicarbonate (HCO 3 ₋), total carbon dioxide (TCO 2 ), base excess (BE), partial pressure of oxygen (PO 2 ), oxygen saturation (sO 2 ) and lactate concentrations in 23 juvenile American alligators (Alligator mississippiensis) using an i-STAT CG4+ cartridge. Our results indicate that sample type had no effect on lactate concentration values (F 2,65 = 0.37, P = 0.963), suggesting that the i-STAT analyser can be used reliably to quantify lactate concentrations in fresh and frozen plasma samples. In contrast, the other seven blood parameters measured by the CG4+ cartridge were significantly affected by sample type. In conclusion, we were able to collect blood samples from all alligators within 2 min of capture to establish preliminary reference ranges for juvenile alligators based on values obtained using fresh whole blood.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). We explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.

Purohit, Sumit↗

DOE OSTI2023

In Vitro Thrombogenicity Testing of Biomaterials in a Dynamic Flow Loop: Effects of Length and Quantity of Test Samples

Abstract The results of in vitro dynamic thrombogenicity testing of biomaterials and medical devices can be significantly impacted by test conditions. To develop and standardize a robust dynamic in vitro thrombogenicity tool, the key test parameters need to be appropriately evaluated and optimized. We used a flow loop test system previously developed in our laboratory to investigate the effects of sample length and the number of samples per test loop on the thrombogenicity results. Porcine blood heparinized to a donor-specific target concentration was recirculated at room temperature through polyvinyl chloride (PVC) tubing loops containing test materials for 1 h at 200 mL/min. Four test materials (polytetrafluoroethylene (PTFE), latex, PVC, and silicone) with various thrombotic potentials in two sample lengths (12 and 18 cm) were examined. For the 12-cm long materials, two different test configurations (one and two samples per loop) were compared. Thrombogenicity was assessed through percent thrombus surface coverage, thrombus weight, and platelet count reduction in the blood. The test system was able to effectively differentiate the thrombogenicity profile of the materials (latex > silicone > PVC ≥ PTFE) at all test configurations. Increasing test sample length by 50% did not significantly impact the test results as both 12 and 18 cm sample lengths were shown to equally differentiate thrombotic potentials between the materials. The addition of a second test sample to each loop did not increase the test sensitivity and may produce confounding results, and thus a single test sample per loop is recommended.

Engineering↗

DOE OSTI2013

Ultra-trace determination of Strontium-90 in environmental soil samples from Qatar by collision/reaction cell-inductively coupled plasma mass spectrometry (CRC-ICP-MS/MS)

Because of the very low level of {sup 90}Sr in the environmental soil samples and its determination by beta counting may take several weeks, we developed a procedure for ultra-trace determination of {sup 90}Sr using collision reaction cell-inductively coupled plasma tandem mass spectrometry (CRC-ICP-MS/MS, Agilent 8800). Soil samples were dried at 105 deg. C and then heated in a furnace at 550 deg. C to remove any organics present. 500 g of each soil samples were aliquoted into 2000 ml glass beakers. Each Soils samples were soaked in 2 ppm Sr solution carrier to allow determination of chemical yield. The solid to liquid ratio was 1:1. Finally the soil samples were dried at 105 deg. C. Five hundred milliliters concentrated nitric acid and 250 ml hydrochloric acid volumes were added on 500 g soil samples. The samples were digested on hot plate at 80 deg. C to prevent spraying with continuous manual mixing. The leachate solution was separated. The solids were rinsed with 500 ml deionized water, warmed on a hot plate and the leachate plus previous leachate were filtered and the total volume was reduced to 500 ml by evaporation. Final leachate volume was transferred to a centrifuge tubes. The centrifuge tubes were centrifuged at 3,500 rpm for 10 min. The leachate was transferred to a 1 L beaker and heated on a hot plate to evaporate the leachate to dryness. The reside was re-dissolved in 100 ml of 2% HNO{sub 3} and reduced by evaporation to 10 mL. The solution was measured directly by CRC-ICP-MS/MS by setting the first quadruple analyzer to m/z 90 and introducing oxygen gas into the reaction cell for elimination isobar interference from zirconium-90. The method was validated by measurements of standard reference materials and applied on environmental soil samples. The overall time requirement for the measurement of strontium-90 by CRC-ICP-MS/MS is 2 days, significantly shorter than any radioanalytical protocol currently available. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗

DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗

DOE OSTI2022

Leaf structural and chemical traits, and BNL field campaign sample details, San Lorenzo, Panama, 2020

This data package includes leaf traits, canopy traits and sample details for leaves from 71 species sampled from the San Lorenzo forest canopy crane site, Panama (PA-SLZ) during the BNL field campaign in January to March 2020. Each leaf sample is described with species, phenological stage and location within vertical canopy profiles. Leaf area index (LAI) and height is presented for each canopy profile location. Leaf mass per area (LMA), leaf water content (LWC) and leaf carbon and nitrogen content are included for a subset of the samples. This data package includes sample details, processed data for leaf traits and LAI (*.csv), LAI raw data (compressed as *.zip) and digital camera images (*.jpg, compressed as *.zip) of the leaf samples. Metadata files include data descriptions (_dd.csv) for tabular data, a list of all species sampled during the campaign (*.csv) and a detailed description of the field campaign protocol and methods (*.pdf). See related datasets for leaf gas exchange, leaf water potential and leaf spectral measurements made on the samples described here.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A Surrogate-based Adaptive Sampling Approach for History Matching and Uncertainty Quantification

A critical procedure in reservoir simulations is history matching (or data assimilation in a broader sense), which calibrates model parameters such that the simulation results are consistent with field measurements, and hence improves the credibility of the predictions given by the simulations. Often there exist non-unique combinations of parameter values that all yield the simulation results matching the measurements. For such ill-posed history matching problems, Bayesian theorem provides a theoretical foundation to represent different solutions and to quantify the uncertainty with the posterior PDF. Lacking an analytical solution in most situations, the posterior PDF may be characterized with a sample of realizations, each representing a possible scenario. A novel sampling algorithm is presented here for the Bayesian solutions to history matching problems. We aim to deal with two commonly encountered issues: 1) as a result of the nonlinear input-output relationship in a reservoir model, the posterior distribution could be in a complex form, such as multimodal, which violates the Gaussian assumption required by most of the commonly used data assimilation approaches; 2) a typical sampling method requires intensive model evaluations and hence may cause unaffordable computational cost. In the developed algorithm, we use a Gaussian mixture model as the proposal distribution in the sampling process, which is simple but also flexible to approximate non-Gaussian distributions and is particularly efficient when the posterior is multimodal. Also, a Gaussian process is utilized as a surrogate model to speed up the sampling process. Furthermore, an iterative scheme of adaptive surrogate refinement and re-sampling ensures sampling accuracy while keeping the computational cost at a minimum level. The developed approach is demonstrated with an illustrative example and shows its capability in handling the above-mentioned issues. Multimodal posterior of the history matching problem is captured and are used to give a reliable production prediction with uncertainty quantification. The new algorithm reveals a great improvement in terms of computational efficiency comparing previously studied approaches for the sample problem.

Li, Weixuan↗

DOE OSTI2018

Portable Automation of Static Chamber Sample Collection for Quantifying Soil Gas Flux

Quantification of soil gas flux using the static chamber method is labor intensive. The number of chambers that can be sampled is limited by the spacing between chambers and the availability of trained research technicians. An automated system for collecting gas samples from chambers in the field would eliminate the need for personnel to return to the chamber during a flux measurement period and would allow a single technician to sample multiple chambers simultaneously. This study describes Chamber Automated Sampling Equipment (FluxCASE) to collect and store chamber headspace gas samples at assigned time points for the measurement of soil gas flux. The FluxCASE design and operation is described, and the accuracy and precision of the FluxCASE system is evaluated. In laboratory measurements of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) concentrations of a standardized gas mixture, coefficients of variation associated with automated and manual sample collection were comparable, indicating no loss of precision. In the field, soil gas fluxes measured from FluxCASEs were in agreement with manual sampling for both N2O and CO2. Slopes of regression equations were 1.01 for CO2 and 0.97 for N2O. The 95% confidence limits of the slopes of the regression lines included the value of one, indicating no bias. Additionally, an expense analysis found a cost recovery ranging from 0.6 to 2.2 yr. Implementing the FluxCASE system is an alternative to improve the efficiency of the static chamber method for measuring soil gas flux while maintaining the accuracy and precision of manual sampling.

Davis, Morgan P.↗

DOE OSTI2012

Analytical Results of MOX Colemanite Concrete Samples Poured August 29, 2012

The Mixed Oxide Fuel Fabrication Facility (MFFF) will use colemanite bearing concrete neutron absorber panels credited with attenuating neutron flux in the criticality design analyses and shielding operators from radiation. The Savannah River National Laboratory is tasked with measuring the total density, partial hydrogen density, and partial boron density of the colemanite concrete. Samples poured 8/29/12 were received on 9/20/2012 and analyzed. The average total density of each of the samples measured by the ASTM method C 642 was within the lower bound of 1.88 g/cm 3 . The average partial hydrogen density of samples 8.6.1, 8.7.1, and 8.5.3 as measured using method ASTM E 1311 met the lower bound of 6.04E-02 g/cm 3 . The average measured partial boron density of each sample met the lower bound of 1.65E-01 g/cm 3 measured by the ASTM C 1301 method. The average partial hydrogen density of samples 8.5.1, 8.6.3, and 8.7.3 did not meet the lower bound. The samples, as received, were not wrapped in a moist towel as previous samples and appeared to be somewhat drier. This may explain the lower hydrogen partial density with respect to previous samples.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Analytical Results of MOX Colemanite Concrete Samples Poured August 29, 2012

36 MATERIALS SCIENCE↗

DOE OSTI2013

Rheology and TIC/TOC Results of ORNL Tank Samples

The Savannah River National Laboratory (SRNL)) was requested by Oak Ridge National Laboratory (ORNL) to perform total inorganic carbon (TIC), total organic carbon (TOC), and rheological measurements for several Oak Ridge tank samples. As received slurry samples were diluted and submitted to SRNL-Analytical for TIC and TOC analyses. Settled solids yield stress (also known as settled shear strength) of the as received settled sludge samples were determined using the vane method and these measurements were obtained 24 hours after the samples were allowed to settled undisturbed. Rheological or flow properties (Bingham Plastic viscosity and Bingham Plastic yield stress) were determined from flow curves of the homogenized or well mixed samples. Other targeted total suspended solids (TSS) concentrations samples were also analyzed for flow properties and these samples were obtained by diluting the as-received sample with de-ionized (DI) water.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Analysis of Tank 35H Samples in Support of Salt Batch Planning

Savannah River Remediation obtained three samples from different heights within Tank 35H. The samples were analyzed by Savannah River National Laboratory to support Salt Batch planning. The results from the analysis indicate the compositions of the three samples show increasing concentrations for most analytes going from HTF-35-15-17 through HTF-35-15-19 corresponding to successively deeper sampling locations. The data indicate some stratification within the tank. The plutonium and Sr-90 concentrations measured in the filtered samples were slightly lower than in the decanted (unfiltered) samples. The difference in the results for the filtered and unfiltered samples likely lies within the expected uncertainty for the measurement, but may indicate that filtration removed a small amount of suspended material from the samples.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

SNS Sample Activation Calculator Flux Recommendations and Validation

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) uses the Sample Activation Calculator (SAC) to calculate the activation of a sample after the sample has been exposed to the neutron beam in one of the SNS beamlines. The SAC webpage takes user inputs (choice of beamline, the mass, composition and area of the sample, irradiation time, decay time, etc.) and calculates the activation for the sample. In recent years, the SAC has been incorporated into the user proposal and sample handling process, and instrument teams and users have noticed discrepancies in the predicted activation of their samples. The Neutronics Analysis Team validated SAC by performing measurements on select beamlines and confirmed the discrepancies seen by the instrument teams and users. The conclusions were that the discrepancies were a result of a combination of faulty neutron flux spectra for the instruments, improper inputs supplied by SAC (1.12), and a mishandling of cross section data in the Sample Activation Program for Easy Use (SAPEU) (1.1.2). This report focuses on the conclusion that the SAPEU (1.1.2) beamline neutron flux spectra have errors and are a significant contributor to the activation discrepancies. The results of the analysis of the SAPEU (1.1.2) flux spectra for all beamlines will be discussed in detail. The recommendations for the implementation of improved neutron flux spectra in SAPEU (1.1.3) are also discussed.

43 PARTICLE ACCELERATORS↗