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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical Laboratory (AL) at the Materials and Fuels Complex (MFC) at Idaho National Laboratory (INL) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RH-TRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization if RH-TRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all AK [define AK] documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Predicting Execution Times for Disk-based and In-Situ Parallel Data Analytics (Final Technical Report)

In recent years, there has been a significant amount of interests in in-situ analytics on simulation programs. For a variety of reasons, it is desirable to be able to predict the execution time of an analytics program. At the same time, frameworks such as MapReduce have become popular for scientific data analytics. This paper focuses on developing performance models for predicting execution time of parallel data analytics, with a special emphasis on in-situ analytics. We take two distinct approach towards performance prediction. We first expand SKOPE (a SKeleton framewOrk for Performance Exploration) with performance models for disk data read, cache performance, and page fault penalty. Second, an analytical performance model is also developed. We have evaluated our performance prediction framework as well as the analytical model on three hardware setups with well-known data mining algorithms implemented in three programming paradigms, MapReduce, MATE (a MapReduce-like parallel system with an alternate API for multi-core environments) and Smart (a MapReduce-like framework for in-situ analytics). Results show that our performance prediction framework along with the incorporated performance models are capable of accurately predicting execution times for parallel scientific analytics on different hardware setups.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

The Finite Analytic Method for steady and unsteady heat transfer problems

A new numerical method called the Finite Analytical Method for solving partial differential equations is introduced. The basic idea of the finite analytic method is the incorporation of the local analytic solution in obtaining the numerical solution of the problem. The finite analytical method first divides the total region of the problem into small subregions in which local analytic solutions are obtained. Then an algebraic equation is derived from the local analytic solution for each subregion relating an interior nodal value at a point P in the subregion to its neighboring nodal values. The assembly of all the local analytic solutions thus provides the finite-analytic numerical solution of the problem. In this paper the finite analytic method is illustrated in solving steady and unsteady heat transfer problems.

Chen, C.-J.↗
NASA NTRS2015

ESIP Earth Sciences Data Analytics (ESDA) Cluster - Work in Progress

The purpose of this poster is to promote a common understanding of the usefulness of, and activities that pertain to, Data Analytics and more broadly, the Data Scientist; Facilitate collaborations to better understand the cross usage of heterogeneous datasets and to provide accommodating data analytics expertise, now and as the needs evolve into the future; Identify gaps that, once filled, will further collaborative activities. Objectives Provide a forum for Academic discussions that provides ESIP members a better understanding of the various aspects of Earth Science Data Analytics Bring in guest speakers to describe external efforts, and further teach us about the broader use of Data Analytics. Perform activities that:- Compile use cases generated from specific community needs to cross analyze heterogeneous data- Compile sources of analytics tools, in particular, to satisfy the needs of the above data users- Examine gaps between needs and sources- Examine gaps between needs and community expertise- Document specific data analytics expertise needed to perform Earth science data analytics Seek graduate data analytics Data Science student internship opportunities.

science data analysis↗
DOE OSTI2015

Mixed Initiative Visual Analytics Using Task-Driven Recommendations

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support tasks involved in discovery and sensemaking, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems, at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with such analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Researchers studying the sensemaking process have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present a candidate set of design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences on user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.

active data↗
DOE OSTI2022

Standard Analytical Methods for Pyrolysis Bio-Oils

There has been significant recent interest in the production of renewable fuels and chemicals from biomass and waste feedstocks. Pyrolysis pathways produce a liquid bio-oil product, which must be processed further, or upgraded, to yield fuel or chemical products. Bio-oils are very complex and often unstable samples, and research and development on upgrading processes needs reliable analytical information. In particular, chemical characterization techniques are needed to quantify both functional groups and individual compounds present in bio-oils. Reliable analytics are also needed to enable the bioenergy industry, as industrial facilities often have different analytical needs and capabilities than research facilities. In this presentation, we will discuss the development of a suite of standard analytical methods for pyrolysis bio-oils. Analytical methods to be discussed include: Determination of Carbon, Hydrogen, Nitrogen, and Oxygen in bio-oils; Accelerated Aging of Fast Pyrolysis Bio-oil using Carbonyl Titration; Determination of Water Content in Bio-oils by Volumetric Karl Fischer Titration; Determination of Carbon Functional Groups; Elemental Analysis of Bio-oils by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) - Na, K, Mg, Ca, S, P, and Fe; Determination of Phenolic Groups in Bio-oils using Revised Folin-Ciocalteu Methods: Single Cuvette and Plate Reader; Corrosivity of Bio-oils: Screening Test using Metal Leaching; Determination of Biogenic Content by 14C Measurement using Liquid Scintillation Counter. These new analytical methods are publicly available as Laboratory Analytical Procedures (https://www.nrel.gov/bioenergy/bio-oil-analysis.html), along with previously developed standard methods: GC-MS, Acid Titration, Carbonyl Titration, and 31P NMR. Additionally, the development of diffusion ordered NMR for characterization of bio-oil molecular weight will be discussed. Collectively, this suite of analytical methods represents the most comprehensive set of standard methods available for pyrolysis bio-oils. These standard methods are commonly used by the bioenergy community, and provide reliable information that enables research, scaleup, and industrial processing of biomass to produce renewable fuels and chemicals.

analytical↗
DOE OSTI2013

Critical node treatment in the analytic function expansion method for Pin Power Reconstruction

Pin Power Reconstruction (PPR) was implemented in PARCS using the eight term analytic function expansion method (AFEN). This method has been demonstrated to be both accurate and efficient. However, similar to all the methods involving analytic functions, such as the analytic node method (ANM) and AFEN for nodal solution, the use of AFEN for PPR also has potential numerical issue with critical nodes. The conventional analytic functions are trigonometric or hyperbolic sine or cosine functions with an angular frequency proportional to buckling. For a critic al node the buckling is zero and the sine functions becomes zero, and the cosine function become unity. In this case, the eight terms of the analytic functions are no longer distinguishable from ea ch other which makes their corresponding coefficients can no longer be determined uniquely. The mode flux distribution of critical node can be linear while the conventional analytic functions can only express a uniform distribution. If there is critical or near critical node in a plane, the reconstructed pin power distribution is often be shown negative or very large values using the conventional method. In this paper, we propose a new method to avoid the numerical problem wit h critical nodes which uses modified trigonometric or hyperbolic sine functions which are the ratio of trigonometric or hyperbolic sine and its angular frequency. If there are no critical or near critical nodes present, the new pin power reconstruction method with modified analytic functions are equivalent to the conventional analytic functions. The new method is demonstrated using the L336C5 benchmark problem. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Comparison of Laboratory Analytical Methods for Uranium Analysis Superior Steel FUSRAP Site - 16606

The Superior Steel Site processed uranium metal in support of the Atomic Energy Commission's fuel-element development program in the 1950's, and was subsequently classified as a Formerly Utilized Sites Remedial Action Program (FUSRAP) Site. Previous investigations at the site identified uranium contamination both inside and outside the buildings associated with historic uranium metalworking activities. The U.S. Army Corps of Engineers (USACE) is currently engaged in the Remedial Investigation (RI) phase in accordance with the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) of 1980, as amended. As part of the RI, gamma spectroscopy analysis of environmental samples was performed on-site using a laboratory-grade, high-purity germanium (HPGe) spectrometer. The gamma spectroscopy analysis included isotopes of uranium and their progeny. As a quality assurance measure, a subset of samples already analyzed on-site were sent to a certified, commercial, off-site laboratory for analysis using a suite of analytical methods including gamma spectroscopy, alpha spectroscopy, and mass spectroscopy. As an element of the quality assurance program implemented for the RI, an assessment of the correlations between the various analytical methods employed by both the on-site and off-site laboratory was performed. This assessment revealed inconsistencies between on-site and off-site gamma spectroscopy analytical results, which raised concerns about the appropriateness and suitability of the analytical data. To resolve the data quality concerns and better understand the source(s) of analytical results variability, the Superior Steel RI team (USACE together with its contractor, Amec Foster Wheeler) developed a quality control test whose design objective was to control or practically eliminate sampling and sample preparation variability in order to focus more clearly on analytical variability. This paper discusses the results of this supplemental quality control test, showing the differences in the various analytical methods (both on-site and off-site). It demonstrates in a practical and real world setting how sample preparation and analytical variability, if not controlled and understood can impact data quality and data defensibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Characterization Techniques for TRU Waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory - 19610

Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical laboratory (AL) at the INL Materials and Fuels Complex (MFC) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste as Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RHTRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization of RHTRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all Acceptable Knowledge (AK) documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Analytical and Computational Advances, Opportunities, and Challenges in Marine Organic Biogeochemistry in an Era of “Omics”

Advances in sampling tools, analytical methods, and data handling capabilities have been fundamental to the growth of marine organic biogeochemistry over the past four decades. There has always been a strong feedback between analytical advances and scientific advances. However, whereas advances in analytical technology were often the driving force that made possible progress in elucidating the sources and fate of organic matter in the ocean in the first decades of marine organic biogeochemistry, today process-based scientific questions should drive analytical developments. Several paradigm shifts and challenges for the future are related to the intersection between analytical progress and scientific evolution. Untargeted “molecular headhunting” for its own sake is now being subsumed into process-driven targeted investigations that ask new questions and thus require new analytical capabilities. However, there are still major gaps in characterizing the chemical composition and biochemical behavior of macromolecules, as well as in generating reference standards for relevant types of organic matter. Field-based measurements are now routinely complemented by controlled laboratory experiments and in situ rate measurements of key biogeochemical processes. And finally, the multidisciplinary investigations that are becoming more common generate large and diverse datasets, requiring innovative computational tools to integrate often disparate data sets, including better global coverage and mapping. Here, we compile examples of developments in analytical methods that have enabled transformative scientific advances since 2004, and we project some challenges and opportunities in the near future. We believe that addressing these challenges and capitalizing on these opportunities will ensure continued progress in understanding the cycling of organic carbon in the ocean.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

BETO 2021 Peer Review - Analytical Development & Support WBS 2.5.1.101

The objective of the Analytical Development and Support (ADS) Project is to produce and maintain the critical analytical methods and tools that enable evaluation of emerging biofuels R&D at NREL and in the broader biofuels research community. Our project is divided into two tasks: one task to develop novel analytical techniques and improve existing methods and one task to maintain existing analytical capabilities at NREL and provide outreach to the wider community. The ADS Project is world-recognized for our Laboratory Analytical Procedures (LAPS) which provide detailed procedures for compositional analysis of biomass and have been adopted as the de facto standards within the biofuels community largely due to the transparency of the methods and the high reputation of NREL's research. Our dialog with stakeholders allows us to provide robust, precise, accurate, and publicly available analytical procedures for better valuation of scientific tools such as our recent accomplishment in developing a cellulose assay to support the EPA and industry in calculation of converted cellulose during starch ethanol production. We continue to develop analytical capabilities to support BETO's directives to research cost advantaged fuels such as animal wastes and novel bioproducts like 2,3-Butane-diol.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

SU-E-J-145: Validation of An Analytical Model for in Vivo Range Verification Using GATE Monte Carlo Simulation in Proton Therapy

Purpose: Predicted PET images on the basis of analytical filtering approach for proton range verification has been successful developed and validated using FLUKA Monte Carlo (MC) codes and phantom measurements. The purpose of the study is to validate the effectiveness of analytical filtering model for proton range verification on GATE/GEANT4 Monte Carlo simulation codes. Methods: In this study, we performed two experiments for validation of predicted β+-isotope by the analytical model with GATE/GEANT4 simulations. The first experiments to evaluate the accuracy of predicting β+-yields as a function of irradiated proton energies. In second experiment, we simulate homogeneous phantoms of different materials irradiated by a mono-energetic pencil-like proton beam. The results of filtered β+-yields distributions by the analytical model is compared with those of MC simulated β+-yields in proximal and distal fall-off ranges. Results: The results investigate the distribution between filtered β+-yields and MC simulated β+-yields distribution in different conditions. First, we found that the analytical filtering can be applied over the whole range of the therapeutic energies. Second, the range difference between filtered β+-yields and MC simulated β+-yields at the distal fall-off region are within 1.5mm for all materials used. The findings validated the usefulness of analytical filtering model on range verification of proton therapy on GATE Monte Carlo simulations. In addition, there is a larger discrepancy between filtered prediction and MC simulated β+-yields using GATE code, especially in proximal region. This discrepancy might Result from the absence of wellestablished theoretical models for predicting the nuclear interactions. Conclusion: Despite the fact that large discrepancies of the distributions between MC-simulated and predicted β+-yields were observed, the study prove the effectiveness of analytical filtering model for proton range verification using GATE Monte Carlo simulation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Big-Data Analytics for Electric Grid and Demand-Side Management

This report describes research to characterize the status of, and trends in, big-data analytics for the electricity grid. The research focused on 1) identifying power-grid big-data sources, types, and characteristics; and 2) characterizing big-data architecture, analytic methods, technology applications, and challenges. The first part of this report describes the main sources and characteristics of big data for the smart grid and comprehensively reviews big-data architecture, technologies, and applications in the power sector. The second part of this report presents case studies of big data applications in the power industry: (1) a smart-meter data and predictive analytics method for demand response (DR), (2) synchrophasor data analytics for the distribution grid, and (3) utility data for peak-demand management. For the predictive analytics case study, smart-meter-data-driven and physical models were developed to predict the potential kilowatt (kW) capacity reduction from DR. The DR estimation framework that was developed works for both small and large-scale customers. The synchrophasor case study demonstrates use of an algorithm applied to time-series data to detect events that appear as significant changes, known as “edges,” in voltage magnitude measurements. The synchrophasor case study also introduces an approach for clustering sets of events to reveal unique features that distinguish them (e.g., distinguishing capacitor bank switching from transformer tap changes). The peak-demand management case study describes the use of the data analytics to enable DR programs to limit forecasted peak demand, resulting in cost savings to the utility. The findings from the research described in this report support identification of opportunities and technologies for big-data and analytics applications for demand-side management in the power sector as well as other approaches to modernizing the electricity grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Semi-Analytic Reconstruction of Flux in Finite Volume Formulations

Semi-analytic reconstruction uses the analytic solution to a second-order, steady, ordinary differential equation (ODE) to simultaneously evaluate the convective and diffusive flux at all interfaces of a finite volume formulation. The second-order ODE is itself a linearized approximation to the governing first- and second- order partial differential equation conservation laws. Thus, semi-analytic reconstruction defines a family of formulations for finite volume interface fluxes using analytic solutions to approximating equations. Limiters are not applied in a conventional sense; rather, diffusivity is adjusted in the vicinity of changes in sign of eigenvalues in order to achieve a sufficiently small cell Reynolds number in the analytic formulation across critical points. Several approaches for application of semi-analytic reconstruction for the solution of one-dimensional scalar equations are introduced. Results are compared with exact analytic solutions to Burger s Equation as well as a conventional, upwind discretization using Roe s method. One approach, the end-point wave speed (EPWS) approximation, is further developed for more complex applications. One-dimensional vector equations are tested on a quasi one-dimensional nozzle application. The EPWS algorithm has a more compact difference stencil than Roe s algorithm but reconstruction time is approximately a factor of four larger than for Roe. Though both are second-order accurate schemes, Roe s method approaches a grid converged solution with fewer grid points. Reconstruction of flux in the context of multi-dimensional, vector conservation laws including effects of thermochemical nonequilibrium in the Navier-Stokes equations is developed.

Gnoffo, Peter A.↗
NASA NTRS2017

Simple Analytical Expressions for Electron Pitch Angle Diffusion Coefficients

In the present paper, we have calculated electron pitch angle diffusion coefficients due to resonant interactions with whistler mode lower band chorus (LBC), upper band chorus (UBC), and electrostatic electron cyclotron harmonic (ECH) waves. Calculations have been performed at two values of the ratio of electron plasma frequency to gyro-frequency and thirteen representative values of electron energies for the plasma sheet electrons. The numerical data of diffusion coefficients have been fitted to simple analytical expressions for each wave mode. These analytical expressions allow for simple evaluation of pitch angle diffusion coefficients for the arbitrary pitch angle, energy, the ambient magnetic field, the wave amplitude, and the ratio of plasma frequency to gyro-frequency. In the case of LBC waves, the analytical coefficients are generally within a factor of two to the numerical coefficients, except at higher pitch angles where the numerical coefficients drop to show negligible values. Likewise, also for UBC waves, the analytical coefficients are generally within a factor of two to the numerical coefficients. The analytical coefficients for ECH waves are generally in agreement with numerical coefficients. However, the analytical expressions developed for ECH waves do not reproduce sharp fluctuations, dips, and gaps, the so-called banded structure observed in the data of numerical coefficients. The effect of the shape variation on the profile of the wave spectral intensity and the effect of cold temperature on the numerical coefficients are also investigated. Applications of the analytical expressions of diffusion coefficients are discussed.

coefficients↗
DOE OSTI2016

Standardization of chemical analytical techniques for pyrolysis bio-oil: history, challenges, and current status of methods

Here, we discuss the standardization of analytical techniques for pyrolysis bio-oils, including the current status of methods, and our opinions on future directions. First, the history of past standardization efforts is summarized, and both successful and unsuccessful validation of analytical techniques highlighted. The majority of analytical standardization studies to-date has tested only physical characterization techniques. In this paper, we present results from an international round robin on the validation of chemical characterization techniques for bio-oils. Techniques tested included acid number, carbonyl titrations using two different methods (one at room temperature and one at 80 °C), 31 P NMR for determination of hydroxyl groups, and a quantitative gas chromatography–mass spectrometry (GC-MS) method. Both carbonyl titration and acid number methods have yielded acceptable inter-laboratory variabilities. 31 P NMR produced acceptable results for aliphatic and phenolic hydroxyl groups, but not for carboxylic hydroxyl groups. As shown in previous round robins, GC-MS results were more variable. Reliable chemical characterization of bio-oils will enable upgrading research and allow for detailed comparisons of bio-oils produced at different facilities. Reliable analytics are also needed to enable an emerging bioenergy industry, as processing facilities often have different analytical needs and capabilities than research facilities. We feel that correlations in reliable characterizations of bio-oils will help strike a balance between research and industry, and will ultimately help to -determine metrics for bio-oil quality. Lastly, the standardization of additional analytical methods is needed, particularly for upgraded bio-oils.

09 BIOMASS FUELS↗
DOE OSTI2015

Uncertainty Quantification for the Reliability of the Analytical Analysis for the Simplified Model of CO2 Geological Sequestration

A hydro-mechanical model with analytical solutions including pressure evolution and geomechanical deformation for geological CO2 injection and sequestration were introduced in our previous work. However, the reliability and accuracy of the hydro-mechanical model and the companion analytical solution are uncertain because of the assumptions and simplifications in the analytical model, though it was validated by a few example cases. This study introduce the method to efficiently measure the accuracy of the analytical model, and specify the acceptable input parameters range that can guarantee the accuracy and reliability of the analytical solution. A coupled hydro-geomechanical subsurface transport simulator STOMP was adopted as a reference to justify the reliability of the hydro-mechanical model and the analytical solution. A quasi-Monte Carlo sampling method was applied to efficiently sample the input parameter space.

analytical analysis↗
DOE OSTI2018

Text-based Analytics for Biosurveillance

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).

Biomedical NLP↗