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At least 235 records · Page 13

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DOE OSTI2017

PROVIDING PLANT DATA ANALYTICS THROUGH A SEAMLESS DIGITAL ENVIRONMENT

As technology continues to evolve and become more integrated into a worker’s daily routine in the Nuclear Power industry the need for easy access to data becomes a priority. Not only does the need for data increase but the amount of data collected increases. In most cases the data is collected and stored in various software applications, many of which are legacy systems, which do not offer any other option to access the data except through the application’s user interface. Furthermore the data gets grouped in “silos” according to work function and not necessarily by subject. Hence, in order to access all the information needed for a particular task or analysis one may have to access multiple applications to gather all the data needed. The industry and the research community have identified the need for a digital architecture and more importantly the need for a Seamless Digital Environment. An SDE provides a means to access multiple applications, gather the data points needed, conduct the analysis requested, and present the result to the user with minimal or no effort by the user. In addition, the nuclear utilities have identified the need for research focused on data analytics. The effort should develop and evaluate use cases for data mining and analytics for employing information from plant sensors and database for use in developing improved business analytics. Idaho National Laboratory is leading such effort, which is conducted in close collaboration with vendors, nuclear utilities, Institute of Nuclear Power Operations, and Electric Power Research Institute. The goal of the study is to research potential approaches to building an analytics solution for equipment reliability, on a small scale, focusing on either a single piece of equipment or a single system. The analytics solution will likely consist of a data integration layer, predictive and machine learning layer and the user interface layer that will display the output of the analysis in a straight forward, easy to consume manner. This paper will describe the study and the initial results.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

ANALYTICAL LABORATORY AT IDAHO NATIONAL LABORATORY COUNTING EQUIPMENT STANDARDIZATION AND QUALITY CONTROL IMPROVEMENT

The Analytical Laboratory at Idaho National Laboratory (INL) is a growing program with an emphasis on providing high quality results to customers, and an increasing focus on independent research. The Analytical Laboratory Counting Group has a number of capabilities: six gamma-ray counters, one gamma-ray sample changer, four gas flow proportional counter drawers, an auto-changing gas flow proportional counter, two liquid scintillation counters, and an alpha spectroscopy system. Other capabilities within the Analytical Laboratory include quadrupole inductively coupled plasma mass spectrometry (ICP-MS), high resolution ICP-MS, multi-collector ICP-MS, ICP atomic emission spectrometry (ICP-AES), gas mass spectrometry, thermal ionization mass spectrometry (TIMS), several glove boxes and fume hoods for sample preparation, and six interconnected hot cells for sample receipt and dissolution. In order to position the Analytical Laboratory as a world-class research institution, the Counting Group has recently begun making improvements to its analytical techniques, calibrations, standardization, and quality control procedures. A large part of this effort is to increase the level of documentation of instrument work. One project is to re-calibrate all of the gamma systems with newly purchased standards. One of the changes made in the purchase of the new standards was to lower the total activity so that the dead time of the calibration would more closely match the dead time of typical samples. Similarly, another planned project is to use new standards to re-calibration the gas flow proportional counters. Additional work has focused on testing and improving alpha spectroscopy sample preparation techniques. Plutonium separations have been tested to improve tracer recovery and to verify the separation technique in the presence of typical sample matrices. A final planned area of improvements has focused on liquid scintillation counting. One difficulty the Counting Group has encountered in liquid scintillation counting is an inflated gross alpha count relative to other analysis methods in the presence of high gross beta activity. Ideas include investigating the impact of choice of scintillation cocktail, as well as the effects of sample dilution.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗

DOE OSTI2021

Solid phase sampling device and methods for point-source sampling of polar organic analytes

Sampling devices for sampling an aqueous source (e.g., field testing of ground water) for multiple different analytes are described. Devices include a solid phase extraction component for retention of a wide variety of targeted analytes. Devices include analyte derivatization capability for improved extraction of targeted analytes. Thus, a single device can be utilized to examine a sample source for a wide variety of analytes. Devices also include an isotope dilution capability that can prevent error introduction to the sample analysis and can correct for sample loss and degradation from the point of sampling until analysis as well as correction for incomplete or poor derivatization reactions. The devices can be field-deployable and rechargeable.

Boggess, Andrew J.↗

DOE OSTI2015

Knowledge management information tool Web analytics - 15182

The Knowledge Management Information Tool (KM-IT) is a web-based [01] system developed to maintain and preserve the Department of Energy's (DOE's) D and D knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the active collaboration and support of the DOE's Energy Facility Contractors Group (EFCOG). The KM-IT system is a D and D community driven system tailored to serve the technical issues faced by the D and D workforce across the DOE Complex. The D and D KM-IT is an interactive web-based system operational and available for free access at www.dndkm.org. [02] The D and D KM-IT is currently composed of the following tailored D and D modules: Web Crawler, Hotline, Technology, Document Library (including Hanford's and Savannah River Sites' ALARA Center Reports), Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors, Collaboration Tools, Training, and Mobile Systems. The Web Crawler dynamically searches through the D and D KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, Documents, and Pictures/Videos to the D and D KM-IT repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. D and D KM-IT makes excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. The mobile version of the KM-IT can be accessed from m.dndkm.org [03] and currently has six active modules: Vendor, Technology, Picture/Video Library, Hotline, Lessons Learned and Specialist Directory. Web analytics is a key tool being implemented by the D and D KM-IT project team in an effort to enhance the system to better meet users' needs. The D and D KM-IT project team incorporates web analytics as part of its operational elements to analyze information and key data points on how the system is being used. Web analytics is the measurement, collection, analysis and reporting of internet data for the purposes of understanding and optimizing web usage. The use of web analytics retains or attracts D and D community members to access and share knowledge through virtual collaboration on the KM-IT platform. D and D KM-IT web analytics measures details on the overall usage of the system without tracking personal information or specific user behavior: how many users visited the application, how many of those users were unique visitors, how visitors come to the system (i.e., by following a link to the system or by using the web site URL via bookmark or directly typing the address), what keywords were used to perform searches with the search engine, how long the average user stayed on a given screen or on the entire system and what links were clicked on. This information is used to identify which areas of the system are more popular than the others and provides information that can be used to streamline the application for a better user experience. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Solid-phase sampling device and methods for point-source sampling of polar organic analytes

Boggess, Andrew J.↗

DOE OSTI2017

Determining appropriate data analytics for transformer health monitoring - 107

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer life cycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring. (authors)

42 ENGINEERING↗

DOE OSTI2023

A projection-based analytical Jacobian framework for chemical kinetics applications

A major challenge in simulating complex combustion systems with detailed chemical kinetic models is the cost of integrating the chemical source terms, often done using stiff ODE solvers that require frequent Jacobian evaluations. Using analytically derived Jacobian matrices instead of divided-difference-based numerical Jacobian approximations can significantly reduce the associated computational cost. However, ambiguities arise in the formulation of analytical Jacobians because the chemical state of the system, or state vector, can be expressed in multiple ways, involving variables that are typically not independent from one another. Here, in this work, the consequences of those ambiguities on practical calculations are characterized in detail, and a generalized, projection-based framework is proposed as a mitigation strategy. Performances are assessed in a series of test cases involving a variety of configurations and numerical solution approaches. Results show that with proper treatment, commonly used analytical Jacobian formulations can be considered as equivalent for practical purposes, thereby alleviating concerns that the state vector chosen to express the governing equations and corresponding analytical Jacobian may significantly impact the accuracy of the simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

The “2T” ion-electron semi-analytic shock solution for code-comparison with xRAGE: A report for FY16

This report documents an effort to generate the semi-analytic "2T" ion-electron shock solution developed in the paper by Masser, Wohlbier, and Lowrie, and the initial attempts to understand how to use this solution as a code-verification tool for one of LANL's ASC codes, xRAGE. Most of the work so far has gone into generating the semi-analytic solution. Considerable effort will go into understanding how to write the xRAGE input deck that both matches the boundary conditions imposed by the solution, and also what physics models must be implemented within the semi-analytic solution itself to match the model assumptions inherit within xRAGE. Therefore, most of this report focuses on deriving the equations for the semi-analytic 1D-planar time-independent "2T" ion-electron shock solution, and is written in a style that is intended to provide clear guidance for anyone writing their own solver.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Analytical Laboratory Presentation at Canadian Nuclear Laboratory

The INL Analytical Research Laboratories and the CNL Analytical Laboratories share many of the same capabilities and expertise while also overlapping significantly in mission. Where there are similarities and differences in the types of samples and analytical procedures between the two facilities, each laboratory can learn much from the other, and establish a technical baseline that may be mutually beneficial. An exchange of knowledge and exposure to the international laboratories benefits both organizations, and it is the purpose of the proposed visits to the two facilities to foster understanding and learning between the respective scientists. Topics of common interest include but are not limited to multi-collector ICP-MS; high resolution gas MS; alpha, beta, and gamma spectrometry; and separations chemistry of nuclear materials. By sharing expertise, the INL and CNL Analytical Laboratories will improve their analyses providing more reliable results and capabilities that are competitive with other world-class facilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

NREL's Capabilities in Analytical Sciences Supporting Research and Development (R&D)

NREL researchers perform biomass characterization in dedicated laboratories working with over $5 million in state-of-the-art analytical equipment. A dedicated team of highly skilled analytical chemists have over 100 cumulative years of experience in characterization. They have three specialized laboratories with dedicated sample processing and advanced analytical equipment. Core capabilities include biomass and biochemical characterization and quantification, publicly available laboratory analytical procedures (LAPs), and near-infrared rapid analysis.

analysis↗

DOE OSTI2023

Advanced Analytical Methodologies Enable Feedstock Screening and Correlation to Pyrolysis Yields and Catalytic Upgrading

Pyrolysis of lignocellulosic biomass can serve as a powerful pathway for the generation of renewable fuels and industrial products. However, procurement of some biomass may be costly, especially when there is competition for alternative uses. Forestry and agricultural byproducts may serve as valuable pyrolysis feedstocks due to their high availability and low cost. Although waste products may fill demand for affordable pyrolysis feedstocks, they are often high in ash and extractives which contribute to unfavorable changes in pyrolysis vapor composition, as well as end products. The work presented here investigates the correlations between various pine anatomical fractions to understand their effect on pyrolysis oil composition for forest residues. Analytical pyrolysis coupled to direct analysis by molecular beam mass spectrometry (MBMS) provides real-time monitoring of pyrolysis vapor composition, even for compounds that are not amenable to gas chromatography. Statistical analysis of MBMS data from the fast pyrolysis (FP) of clean pine, cambium, twigs and branches, needles, bark, and a forest residue samples revealed that bark and needles made the greatest differences in the vapor composition of forest residues during pyrolysis and were enriched in resin acids, furfural derivatives, and ions that have been previously observed from catalysis corresponding to ash content. These results were also compared to catalytic fast pyrolysis (CFP) using a platinum on titania catalyst, and correlations between FP and CFP products were observed for the anatomical fractions. The forest residues and corresponding anatomical fractions from a stand of 23-year-old pine will be converted by fast pyrolysis in a bench-scale 2-inch fluidized-bed reactor, and statistical correlations between the bench-scale products and analytical-scale pyrolysis vapors will be shown. This work highlights the power of advanced analytical methodologies in determining the contributions of vapor components to catalytic pyrolysis outcomes for mixed feedstocks and demonstrates the use of analytical methodologies to better screen feedstocks prior to pyrolysis.

analytical↗

DOE OSTI2021

Unlocking the secret of lignin-enzyme interactions: Recent advances in developing state-of-the-art analytical techniques

Bioconversion of renewable lignocellulosics to produce liquid fuels and chemicals is one of the most effective ways to solve the problem of fossil resource shortage, energy security, and environmental challenges. Among the many biorefinery pathways, hydrolysis of lignocellulosics to fermentable monosaccharides by cellulase is arguably the most critical step of lignocellulose bioconversion. In the process of enzymatic hydrolysis, the direct physical contact between enzymes and cellulose is an essential prerequisite for the hydrolysis to occur. However, lignin is considered one of the most recalcitrant factors hindering the accessibility of cellulose by binding to cellulase unproductively, which reduces the saccharification rate and yield of sugars. This results in high costs for the saccharification of carbohydrates. The various interactions between enzymes and lignin have been explored from different perspectives in literature, and a basic lignin inhibition mechanism has been proposed. However, the exact interaction between lignin and enzyme as well as the recently reported promotion of some types of lignin on enzymatic hydrolysis is still unclear at the molecular level. Multiple analytical techniques have been developed, and fully unlocking the secret of lignin-enzyme interactions would require a continuous improvement of the currently available analytical techniques. This review summarizes the current commonly used advanced research analytical techniques for investigating the interaction between lignin and enzyme, including quartz crystal microbalance with dissipation (QCM-D), surface plasmon resonance (SPR), attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), nuclear magnetic resonance (NMR) spectroscopy, fluorescence spectroscopy (FLS), and molecular dynamics (MD) simulations. Interdisciplinary integration of these analytical methods is pursued to provide new insight into the interactions between lignin and enzymes. Finally, this review will serve as a resource for future research seeking to develop new methodologies for a better understanding of the basic mechanism of lignin-enzyme binding during the critical hydrolysis process.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

A semi-analytical method for simulating matrix diffusion in numerical transport models

We report a semi-analytical approximation for transient matrix diffusion is developed for use in numerical contaminant transport simulators. This method is an adaptation and extension of the heat conduction method of Vinsome and Westerveld (1980) used to simulate heat losses during thermally enhanced oil recovery. The semi-analytical method is used in place of discretization of the low permeability materials, and it represents the concentration profile in the low permeability materials with a fitting function that is adjusted in each element at each time-step. The resulting matrix diffusion fluxes are added to the numerical model as linear concentration-dependent source/sink terms. Since only the high permeability zones need to be discretized, the numerical formulation is extremely efficient compared to traditional approaches that require discretization of both the high and low permeability zones. The semi-analytical method compares favorably with the analytical solution for transient one-dimensional diffusion with first order decay, with a two-layer aquifer/aquitard solution, with the solution for transport in a fracture with matrix diffusion and decay, and with a fully numerical solution for transport in a thin sand zone bounded by clay with variable decay rates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Connecting the dots: Semi-analytical and random walk numerical solutions of the diffusion–reaction equation with stochastic initial conditions

We study a system with bimolecular irreversible kinetic reaction A+B→∅ where the underlying transport of reactants is governed by diffusion, and the local reaction term is given by the law of mass action. We consider the case where the initial concentrations are given in terms of an average and a white noise perturbation. Our goal is to solve the diffusion–reaction equation which governs the system, and we tackle it with both analytical and numerical approaches. To obtain an analytical solution, we develop the equations of moments and solve them approximately. To obtain a numerical solution, we develop a grid-less Monte Carlo particle tracking approach, where diffusion is modeled by a random walk of the particles, and reaction is modeled by annihilation of particles. The probability of annihilation is derived analytically from the particles' co-location probability. We rigorously derive the relationship between the initial number of particles in the system and the amplitude of white noise represented by that number. This enables us to compare the particle simulations and the approximate analytical solution and offer an explanation of the late time discrepancies. - Graphical abstract:.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

The evaluation of an analytical protocol for the determination of substances in waste for hazard classification

Highlights: • Knowledge of wastes in substances will be necessary to assess HP1–HP15 hazard properties. • A new analytical protocol is proposed for this and tested by two service laboratories on 32 samples. • Sixty-three percentage of the samples have a satisfactory analytical balance between 90% and 110%. • Eighty-four percentage of the samples were classified identically (Seveso Directive) for their hazardousness by the two laboratories. • The method, in progress, is being normalized in France and is be proposed to CEN. - Abstract: The classification of waste as hazardous could soon be assessed in Europe using largely the hazard properties of its constituents, according to the the Classification, Labelling and Packaging (CLP) regulation. Comprehensive knowledge of the component constituents of a given waste will therefore be necessary. An analytical protocol for determining waste composition is proposed, which includes using inductively coupled plasma (ICP) screening methods to identify major elements and gas chromatography/mass spectrometry (GC–MS) screening techniques to measure organic compounds. The method includes a gross or indicator measure of ‘pools’ of higher molecular weight organic substances that are taken to be less bioactive and less hazardous, and of unresolved ‘mass’ during the chromatography of volatile and semi-volatile compounds. The concentration of some elements and specific compounds that are linked to specific hazard properties and are subject to specific regulation (examples include: heavy metals, chromium(VI), cyanides, organo-halogens, and PCBs) are determined by classical quantitative analysis. To check the consistency of the analysis, the sum of the concentrations (including unresolved ‘pools’) should give a mass balance between 90% and 110%. Thirty-two laboratory samples comprising different industrial wastes (liquids and solids) were tested by two routine service laboratories, to give circa 7000 parameter results. Despite discrepancies in some parameters, a satisfactory sum of estimated or measured concentrations (analytical balance) of 90% was reached for 20 samples (63% of the overall total) during this first test exercise, with identified reasons for most of the unsatisfactory results. Regular use of this protocol (which is now included in the French legislation) has enabled service laboratories to reach a 90% mass balance for nearly all the solid samples tested, and most of liquid samples (difficulties were caused in some samples from polymers in solution and vegetable oil). The protocol is submitted to French and European normalization bodies (AFNOR and CEN) and further improvements are awaited.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

How machine learning can extend electroanalytical measurements beyond analytical interpretation

Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb–Wagner test on a magnesium spinel intercalation host. In conclusion, as compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Molecular properties of excited electronic state: Formalism, implementation, and applications of analytical second energy derivatives within the framework of the time-dependent density functional theory/molecular mechanics

This work extends our previous works [J. Liu and W. Z. Liang, J. Chem. Phys. 135, 014113 (2011); J. Liu and W. Z. Liang, J. Chem. Phys. 135, 184111 (2011)] on analytical excited-state energy Hessian within the framework of time-dependent density functional theory (TDDFT) to couple with molecular mechanics (MM). The formalism, implementation, and applications of analytical first and second energy derivatives of TDDFT/MM excited state with respect to the nuclear and electric perturbations are presented. Their performances are demonstrated by the calculations of adiabatic excitation energies, and excited-state geometries, harmonic vibrational frequencies, and infrared intensities for a number of benchmark systems. The consistent results with the full quantum mechanical method and other hybrid theoretical methods indicate the reliability of the current numerical implementation of developed algorithms. The computational accuracy and efficiency of the current analytical approach are also checked and the computational efficient strategies are suggested to speed up the calculations of complex systems with many MM degrees of freedom. Finally, we apply the current analytical approach in TDDFT/MM to a realistic system, a red fluorescent protein chromophore together with part of its nearby protein matrix. The calculated results indicate that the rearrangement of the hydrogen bond interactions between the chromophore and the protein matrix is responsible for the large Stokes shift.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗