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Results for “Process Sample Analysis”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

pnnl/memgaze

MemGaze is a memory analysis toolset that combines high-resolution trace analysis and low overhead measurement, both with respect to time and space. It provides high-resolution by collecting world-level memory access traces, where the highest resolution supported is back-to-back sequences. It achieves low-overhead in space and time by leveraging sampling and various methods of hardware support for collecting traces. Its post-mortem trace processing provides multiresolution analysis for locations vs. operations; accesses vs. spatio-temporal reuse, and reuse (distance, rate, volume) vs. access patterns.

Central, PNNL Developer↗
DOE OSTI2016

Analysis Of Condensate Samples In Support Of The Antifoam Degradation Study

The degradation of Antifoam 747 to form flammable decomposition products has resulted in declaration of a Potential Inadequacy in the Safety Analysis (PISA) for the Defense Waste Processing Facility (DWPF). Savannah River National Laboratory (SRNL) testing with simulants showed that hexamethyldisiloxane (HMDSO), trimethylsilanol (TMS), and 1-propanal are formed in the offgas from the decomposition of the antifoam. A total of ten DWPF condensate samples from Batch 735 and 736 were analyzed by SRNL for three degradation products and additional analytes. All of the samples were analyzed to determine the concentrations of HMDSO, TMS, and propanal. The results of the organic analysis found concentrations for propanal and HMDSO near or below the detection limits for the analysis. The TMS concentrations ranged from below detection to 11 mg/L. The samples from Batch 736 were also analyzed for formate and oxalate anions, total organic carbon, and aluminum, iron, manganese, and silicon. Most of the samples contained low levels of formate and therefore low levels of organic carbon. These two values for each sample show reasonable agreement in most cases. Low levels of all the metals (Al, Fe, Mn, and Si) were present in most of the samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

ANALYSIS OF CONDENSATE SAMPLES IN SUPPORT OF THE ANTIFOAM DEGRADATION STUDY

The degradation of Antifoam 747 to form flammable decomposition products has resulted in declaration of a Potential Inadequacy in the Safety Analysis (PISA) for the Defense Waste Processing Facility (DWPF). Savannah River National Laboratory (SRNL) testing with simulants showed that hexamethyldisiloxane (HMDSO), trimethylsilanol (TMS), and 1-propanal are formed in the offgas from the decomposition of the antifoam. A total of ten DWPF condensate samples from Batch 735 and 736 were analyzed by SRNL for three degradation products and additional analytes. All of the samples were analyzed to determine the concentrations of HMDSO, TMS, and propanal. The results of the organic analysis found concentrations for propanal and HMDSO near or below the detection limits for the analysis. The TMS concentrations ranged from below detection to 11 mg/L. The samples from Batch 736 were also analyzed for formate and oxalate anions, total organic carbon, and aluminum, iron, manganese, and silicon. Most of the samples contained low levels of formate and therefore low levels of organic carbon. These two values for each sample show reasonable agreement in most cases. Low levels of all the metals (Al, Fe, Mn, and Si) were present in most of the samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Scaling up to address data science challenges

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Characterization of the March 2017 Tank 15 Waste Removal Slurry Sample (Combination of Slurry Samples HTF-15-17-28 and HTF-15-17-29)

Two March 2017 Tank 15 slurry samples (HTF-15-17-28 and HTF-15-17-29) were collected during the second bulk waste removal campaign and submitted to SRNL for characterization. At SRNL, the two samples were combined and then characterized by a series of physical, elemental, radiological, and ionic analysis methods. Sludge settling as a function of time was also quantified. The characterization results reported in this document are consistent with expectations based upon waste type, process knowledge, comparisons between alternate analysis techniques, and comparisons with the characterization results obtained for the November 2016 Tank 15 slurry sample (the sample collected during the first bulk waste removal campaign).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

hpMCA: A Python-based Graphical Program for Energy Dispersive X-Ray Diffraction Data Collection and Analysis

Energy-dispersive X-ray diffraction (EDXD) at synchrotron beamlines is commonly used for the study of material properties under high pressure and/or high temperature. Experimenters typically rely on the availability of robust data collection and analysis at a beamline, but this has become increasingly difficult, especially with the introduction of multi-element detectors that generate complex, multi-dimensional data sets. These data sets have energy resolution, and they can also be resolved in relation to sample position, diffraction angle, or different external stimuli. We report a new Python-based graphical program, hpMCA, for EDXD data collection and analysis that streamlines the experimental process for the beamline users. The program features a user-friendly interface, capability for online viewing and analyzing data from multi-element energy-dispersive detectors, and includes features useful for working with samples under high pressure and/or high temperature, such as crystal phase identification, real-time unit cell lattice refinement, and pressure determination based on an equation of state.

data analysis software↗
DOE OSTI2024

FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data

The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

High-Resolution Characterization of UMo Alloy Microstructure

This report highlights the capabilities and procedure for high-resolution characterization of UMo fuels in PNNL. Uranium-molybdenum (UMo) fuel processing steps, from casting to forming final fuel, directly affect the microstructure of the fuel, which in turn dictates the in-reactor performance of the fuel under irradiation. In order to understand the influence of processing on UMo microstructure, microstructure characterization techniques are necessary. Higher-resolution characterization techniques like transmission electron microscopy (TEM) and atom probe tomography (APT) are needed to interrogate the details of the microstructure. The findings from TEM and APT are also directly beneficial for developing predictive multiscale modeling tools that can predict the microstructure as a function of process parameters. This report provides background on focused-ion-beam–based TEM and APT sample preparation, TEM and APT analysis procedures, and the unique information achievable through such advanced characterization capabilities for UMo fuels, from a fuel fabrication capability viewpoint.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analysis of Variance of Functional Data (F-ANOVA) [Slides]

Goals: What is Analysis of Variance (ANOVA); Extending Analysis of Variance to Function Data (F-ANOVA); The role of stochastic processes in F-ANOVA; One and Two Sample Problems for F-ANOVA; One-Way F-ANOVA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tetrafluoroboric Acid Digestion for Accurate Determination of Rare Earth Elements in Coal and Fly Ash by ICP-MS Analysis

Coal and coal-related fly ash often contain rare earth elements (REEs) that have the potential to be utilized as valuable mineral resources. Accurately determining the REE content in coal and fly ash is crucial for resource evaluation. The conventional approach involves using hydrofluoric acid (HF) to dissolve silicates and release REEs, which, however, prolongs the digestion process due to the additional step of complexing fluoride ions (F−) with boric acid (H3BO3). Determining the correct amount of H3BO3 for neutralization can be challenging, and in some instances, the binding of fluoride ions with certain lanthanides (Lns) hampers the accurate determination of all 14 naturally occurring rare earth elements in a single digestion batch by inductively coupled plasma mass spectrometry (ICP-MS). In this study, we present an alternative method that achieves the accurate determination of all 14 naturally occurring REEs using tetrafluoroboric acid (HBF4) followed by ICP-MS analysis. This approach eliminates the need for an F− complexing step. We tested this method on certified REE reference materials, including NIST 1632e (coal) and NIST 1633c (fly ash), as well as the REE geological reference material USGS AGV-1 (andesite). Our results demonstrated excellent recovery rates (relative standard deviation, RSD < ±10%), with a correlation coefficient (r2) exceeding 0.99. Using this method, we investigated the concentrations of all 14 REEs in coal and fly ash samples collected from various locations in the southwestern USA. This improved digestion technique streamlines the analysis process and enhances the accuracy of REE determination, facilitating a more comprehensive evaluation of REE-rich coal and fly ash deposits for resource exploration.

58 GEOSCIENCES↗
DOE OSTI2014

Effects of outgassing of loader chamber walls on hydriding of thin films for commercial applications

An important aspect of understanding industrial processing is to know the characteristics of the materials used in such processes. A study was performed to determine the effects of hydriding chamber material on the degree of hydriding for the commercial production of thin film hydride targets for various research universities, commercial companies, and government national laboratories. The goal was to increase the degree of hydriding of various thin film hydrides and to study the vacuum environment during air-exposure hydriding. For this purpose, dynamic residual gas analysis during deuterium gas hydride processing was utilized with erbium thin films, employing a special set-up for direct dynamic hydride gas sampling during processing at elevated temperature and full loading gas pressure. Complete process data for (1) a copper–(1.83 wt. %)beryllium wet hydrogen fired passivated (600 °C–1 h) externally heated pipe hydriding chamber are reported. Dynamic residual gas analysis comparisons during hydriding are presented for hydriding chambers made from (2) alumina (99.8 wt. %), (3) copper (with an interior aluminum coating ∼10 k Å thick, and (4) for a stainless-steel air-fired passivated (900 °C–1 h) chamber. Dynamic data with deuterium gas in the chamber at the hydriding temperature (450 °C) showed the presence and growth of water vapor (D{sub 2}O) and related mixed ion species(H{sub 2}O{sup +}, HDO{sup +}, D{sub 2}O{sup +}, and OD{sup +}) from hydrogen isotope exchange reactions during the 1 h process time. Peaks at mass-to-charge ratios (i.e., m/e) of 12(C{sup +}), 16(CD{sub 2}{sup +}), 17(CHD{sub 2}{sup +}), and 18(CD{sub 3}{sup +}, OD{sup +}) increased for approximately the first half hour of a 1 h hydriding process and then approach steady state. Mass-to-charge peaks at 19(HDO{sup +}) and 20(D{sub 2}O{sup +}) continue to increase throughout the process cycle. Using the m/e = 20 (D{sub 2}O{sup +}) peak intensity from chamber (1)–Cu(1.83 wt. %)Be as a standard, the peak intensity from chamber (4)—stainless-steel (air-fired) was 7.1× higher, indicating that the surface of stainless-steel had a larger concentration of reactive oxygen and/or water than hydrogen. The (D{sub 2}O{sup +}) peak intensity from chamber (3)—Cu (interior Al coating) was 1.55× larger and chamber (2)—alumina(99.8%) was 1.33× higher than Cu(1.83 wt. %)Be. Thus copper–(1.83 wt. %)beryllium was the best hydriding chamber material studied followed closely by the alumina (99.8 wt. %) chamber. Gas take-up by Er occluder targets processed in Cu(1.83 wt. %)Be hydriding chambers (i.e., gas/metal atomic ratios) correlate with the dynamic RGA data.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Advances in Geologic Disposal System Modeling and Application to Crystalline Rock

The Used Fuel Disposition Campaign (UFDC) of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (OFCT) is conducting research and development (R&D) on geologic disposal of used nuclear fuel (UNF) and high-level nuclear waste (HLW). Two of the high priorities for UFDC disposal R&D are design concept development and disposal system modeling (DOE 2011). These priorities are directly addressed in the UFDC Generic Disposal Systems Analysis (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media (e.g., salt, granite, clay, and deep borehole disposal). This report describes specific GDSA activities in fiscal year 2016 (FY 2016) toward the development of the enhanced disposal system modeling and analysis capability for geologic disposal of nuclear waste. The GDSA framework employs the PFLOTRAN thermal-hydrologic-chemical multi-physics code and the Dakota uncertainty sampling and propagation code. Each code is designed for massively-parallel processing in a high-performance computing (HPC) environment. Multi-physics representations in PFLOTRAN are used to simulate various coupled processes including heat flow, fluid flow, waste dissolution, radionuclide release, radionuclide decay and ingrowth, precipitation and dissolution of secondary phases, and radionuclide transport through engineered barriers and natural geologic barriers to the biosphere. Dakota is used to generate sets of representative realizations and to analyze parameter sensitivity.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

Applied Probability↗
DOE OSTI2022

Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters

Understanding gas migration in compacted clay materials, e.g., bentonite and claystone, is important for the design and performance assessment of an engineered barrier system of a radioactive waste repository system, as well as many practical applications. Existing field and laboratory data on gas migration processes in low-permeability clay materials demonstrate the complexity of flow and transport processes, including various types of instabilities, caused by nonlinear dynamics of coupled processes of liquid–gas exchange, dilation, fracturing, fracture healing, etc., which cannot be described by classical models of fluid dynamics in porous media. We here show that the complexity of gas migration processes can be explained using a phenomenological concept of nonlinear dynamics and deterministic chaos theory. To do so, we analyzed gas pressure and gas influx (i.e., input) and outflux (i.e., output), recorded during the gas injection experiment in the compact Mx80-D bentonite sample, and calculated a set of the diagnostic parameters of nonlinear dynamics and chaos, such a global embedding dimension, a correlation dimension, an information dimension, and a spectrum of Lyapunov exponents, as well as plotted 2D and 3D pseudo-phase-space strange attractors, based on the univariate influx and outflux time series data. These results indicate the presence of phenomena of low-dimensional deterministic chaotic behavior of gas migration in bentonite. In particular, during the onset of gas influx in the bentonite core, before the breakthrough, the development of gas flow pathways is characterized by the process of chaotic gas diffusion. After the breakthrough, with inlet-to-outlet movement of gas, the prevailing process is chaotic advection. During the final phase of the experiment, with no influx to the sample, the relaxation pattern of gas outflux is resumed back to a process of chaotic diffusion. The types of data analysis and a proposed phenomenological model can be used to establish the basic principles of experimental data-gathering, modeling predictions, and a research design.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Washing Study with SRS Tank 26 Samples FTF-26-19-12 and -13

A recent analysis of Tank 26, one of the feed tanks for the Defense Waste Processing Facility (DWPF) Sludge Batch 10 (SB10), determined a higher than anticipated sulfur concentration in the supernatant and a significant amount of insoluble sulfur, which could negatively impact the DWPF glass waste form. Savannah River Remediation Engineering (SRR-E) requested that the Savannah River National Laboratory (SRNL) perform a washing study with Tank 26 material to determine amount of sulfur removed during washing tests. SRNL decanted supernate from a sample of Tank 26 until it was at 12 wt% insoluble solids. Subsequently, SRNL performed washing experiments from the 8 M sodium initial concentration to targeted endpoints of 4 M, 2 M and 1 M sodium concentration (assuming no sodium dissolution). Results show that over 80% of the sulfur was removed and nearly all the sulfur was in the aqueous phase at the end of washing, demonstrating effective removal of sulfur during washing. Additional washing is likely to decrease the sulfur concentration further since most of the sulfur was in the aqueous phase.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Synthesis and Characterization of Sol–Gel Auto Combustion-Derived Ce/Zr-Substituted Strontium Hexaferrite Nanoparticles

In this work, Ce/Zr-substituted strontium hexaferrite (SrFe{sub 12}O{sub 19}) nanoparticles have been synthesized by the sol–gel auto combustion method and the synthesized samples were annealed at 900 {sup ∘}C for 3 h. The powder XRD pattern revealed that all the diffraction peaks match the hexagonal phase of strontium hexaferrite and the lattice parameters are determined. The VSM study revealed that the values of saturation magnetization, coercivity and remanent magnetization are decreased for the Ce/Zr-substituted strontium hexaferrite sample. Thermal analysis reveals the formation of a hexagonal phase above 670 {sup ∘}C. The specific vibration band of hexagonal ferrite was observed in the range 400–600 cm{sup −1}. The FE-SEM image shows that the particles are spherical shaped and particle size varied from 37 to 44 nm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗