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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Text analysis methods, text analysis apparatuses, and articles of manufacture

Text analysis methods, text analysis apparatuses, and articles of manufacture are described according to some aspects. In one aspect, a text analysis method includes accessing information indicative of data content of a collection of text comprising a plurality of different topics, using a computing device, analyzing the information indicative of the data content, and using results of the analysis, identifying a presence of a new topic in the collection of text.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A large-scale analysis of bioinformatics code on GitHub

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. Here, the purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Uncertainty Quantification in Wind Plant Energy Estimation: Preprint

Data characterizing a wind power plant's production is important for the validation, calibration, and refinement of the wake loss models used to simulate performance for pre-construction energy estimates. In this process, there is the potential for significant variation in the methods used by an analyst for, first, the preparation of the operational production data for comparison to model data and, second, the generation of analogous production data from simulations. In this work, the uncertainty introduced by these method variations is quantified by considering ensembles of analysis methods. Specifically, 1920 methods variations were developed for preparing the operational data as a predictable production benchmark and simulations taking as input sample sets of the observed wind resource ranged in size from covering 4% to 125% of a full year were run using 3 engineering wake loss models in FLORIS simulations. The benchmark and simulation input uncertainties place lower bounds on the precision of the calibration between the simulation model estimates and operational benchmark data. Even within subsets of method variations (representative of what might be seen within a single company), the uncertainty in the predictable expected production is estimated to be between 1% and 3% the median predictable expected production while the uncertainty in the simulated expected production remains at best on the order of 2%. To put this into context: the simulated expected production is very closely related to the annual energy production (AEP) metric commonly used to quantify a plant's predicted performance and a 3% error in the pre-construction AEP estimate can result in a $17M loss due to additional financing costs for a typical 100MW Texas plant.

17 WIND ENERGY↗
DOE OSTI2022

SERS-based ssDNA composition analysis with inhomogeneous peak broadening and reservoir computing

Surface-enhanced Raman spectroscopy employed in conjunction with post-processing machine learning methods is a promising technique for effective data analysis, allowing one to enhance the molecular and chemical composition analysis of information rich DNA molecules. In this work, we report on a room temperature inhomogeneous broadening as a function of the increased adenine concentration and employ this feature to develop one-dimensional and two dimensional chemical composition classification models of 200 long single stranded DNA sequences. Afterwards, we develop a reservoir computing chemical composition classification scheme of the same molecules and demonstrate enhanced performance that does not rely on manual feature identification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Graphical methods for the sensitivity analysis in discriminant analysis

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern of the change.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Contact resonances of U-shaped atomic force microscope probes

Recent approaches used to characterize the elastic or viscoelastic properties of materials with nanoscale resolution have focused on the contact resonances of atomic force microscope (CR-AFM) probes. The experiments for these CR-AFM methods involve measurement of several contact resonances from which the resonant frequency and peak width are found. The contact resonance values are then compared with the noncontact values in order for the sample properties to be evaluated. The data analysis requires vibration models associated with the probe during contact in order for the beam response to be deconvolved from the measured spectra. To date, the majority of CR-AFM research has used rectangular probes that have a relatively simple vibration response. Recently, U-shaped AFM probes have created much interest because they allow local sample heating. However, the vibration response of these probes is much more complex such that CR-AFM is still in its infancy. In this article, a simplified analytical model of U-shaped probes is evaluated for contact resonance applications relative to a more complex finite element (FE) computational model. The tip-sample contact is modeled using three orthogonal Kelvin-Voigt elements such that the resonant frequency and peak width of each mode are functions of the contact conditions. For the purely elastic case, the frequency results of the simple model are within 8% of the FE model for the lowest six modes over a wide range of contact stiffness values. Results for the viscoelastic contact problem for which the quality factor of the lowest six modes is compared show agreement to within 13%. These results suggest that this simple model can be used effectively to evaluate CR-AFM experimental results during AFM scanning such that quantitative mapping of viscoelastic properties may be possible using U-shaped probes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Neutron Polarization Analysis for Biphasic Solvent Extraction Systems

Here we performed neutron polarization analysis (NPA) of extracted organic phases containing complexes, comprised of Zr(NO 3 ) 4 and tri-n-butyl phosphate, which enabled decomposition of the intensity distribution of small-angle neutron scattering (SANS) into the coherent and incoherent scattering components. The coherent scattering intensity, containing structural information, and the incoherent scattering compete over a wide range of magnitude of scattering vector, q, specifically when q is larger than q* ≈ 1/R g , where R g is the radius of gyration of scatterer. Therefore, it is important to determine the incoherent scattering intensity exactly to perform an accurate structural analysis from SANS data when R g is small, such as the aforementioned extracted coordination species. Although NPA is the best method for evaluating the incoherent scattering component for accurately determining the coherent scattering in SANS, this method is not used frequently in SANS data analysis because it is technically challenging. In this study, we successfully demonstrated that experimental determination of the incoherent scattering using NPA is suitable for sample systems containing a small scatterer with a weak coherent scattering intensity, such as extracted complexes in biphasic solvent extraction systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers

Objectives: Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. Methods and Materials: Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. Results: A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (K{sub trans}) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in K{sub trans} but with large uncertainty (111.6 ± 150.5) %. Conclusions: Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Inverting infrasound data for the seismoacoustic source time functions and surface spall at the Source Physics Experiments Phase II: Dry Alluvium Geology

This report presents the infrasound data recorded as part of the Source Physics Experiment - Phase 2, Dry Alluvium Geology. This experiment, also known colloquially as DAG, consisted of four underground chemical explosions at the Nevada National Security Site. We focus our analysis on only the fourth explosion (DAG-4) as we determined that this was the only event that produced clear source-generated infrasound energy as recorded by the DAG sensors. We analyze the data using two inversion methods. The first method is designed to estimate the point-source seismoacoustic source time functions, and the second inversion method is designed to estimate the first-order characteristics (e.g. horizontal dimensions and maximum amplitude) of the actual spall surface. For both analysis methods, we are able to fit the data reasonably well, with various assumptions of the source model. The estimated seismoacoustic source appears to be a combination of a buried, isotropic explosion with a maximum amplitude of ~2 x 10 9 Nm and a vertically oriented force, applied to the Earth's surface with a maximum amplitude of 4 x 10 7 N. We use the vertically oriented force to simulate surface spall. The estimated spall surface has an approximate radius of ~40 m with a maximum acceleration magnitude in the range of 0.8 to 1.5 m/s/s. These estimates are approximately similar to the measured surface acceleration at the site.

58 GEOSCIENCES↗
DOE OSTI2025

SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)

The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Natural Gas LCA

This presentation was provided via a pre-recorded presentation at the 2025 Coordinating Research Council Life Cycle Analysis of Transportation Workshop at Argonne National Laboratory on October 9, 2025. The presentation provides an overview of the methods and data sources used in the report, Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile and briefly discusses discrepancies between top-down and bottom-up inventory methods and how those might be overcome.

LCA↗
DOE OSTI2020

Unwarping GISAXS data

Explore the source record for details and available documents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum. II. Bayesian model calibration

Dynamic shockless compression experiments provide the ability to explore material behavior at extreme pressures but relatively low temperatures. Typically, the data from these types of experiments are interpreted through an analytic method called Lagrangian analysis. Here, in this work, alternative analysis methods are explored using modern statistical methods. Specifically, Bayesian model calibration is applied to a new set of platinum data shocklessly compressed to 570 GPa. Several platinum equation-of-state models are evaluated, including traditional parametric forms as well as a novel non-parametric model concept. The results are compared to those in Paper I obtained by inverse Lagrangian analysis. The comparisons suggest that Bayesian calibration is not only a viable framework for precise quantification of the compression path, but also reveals insights pertaining to trade-offs surrounding model form selection, sensitivities of the relevant experimental uncertainties, and assumptions and limitations within Lagrangian analysis. The non-parametric model method, in particular, is found to give precise unbiased results and is expected to be useful over a wide range of applications. The calibration results in estimates of the platinum principal isentrope over the full range of experimental pressures to a standard error of 1.6%, which extends the results from Paper I while maintaining the high precision required for the platinum pressure standard.

Brown, Justin Lee↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Uncertainty Quantification of Calculated Temperatures for the AGR 5/6/7 Experiment

This report documents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation experiment conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technologies? research and development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR 5/6/7 capsules, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The calculated fuel temperatures for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. This report covers temperature uncertainty results for each of the five AGR 5/6/7 capsules. To quantify the uncertainty of calculated temperatures determined using the ABAQUS finite element heat transfer code, this study identifies and analyzes model parameters of potential importance to the calculated temperatures of fuel compacts and thermocouples. The selection of input parameters for uncertainty quantification is based on the ranking of their influences upon temperature predictions. Thus, selected input parameters include those with high sensitivity and those with the largest uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of calculated temperatures. Measurement uncertainty, analysis of modeling assumptions, and expert judgment are used as the basis to quantify the uncertainty range for selected input parameters. The input uncertainties are dynamic, accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperatures and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the time dependent first order (linear) expansion terms in constructing the temperature response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, using an interpolation scheme over the input parameter domain, the analysis obtains time dependent sensitivity over the test campaign duration. This allows computation of uncertainty for the calculated fuel temperatures and the calculated graphite temperatures at thermocouple locations during the entire irradiation period.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Uncertainty quantification of calculated temperatures for advanced gas reactor fuel irradiation experiments

This paper presents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) fuel irradiation experiments conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technology Research and Development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR tests, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The temperature simulation data for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. To quantify the uncertainty of AGR calculated temperatures, this study identifies and analyzes ABAQUS model parameters of potential importance to the AGR predicted fuel temperatures. The selection of input parameters for uncertainty quantification of the AGR calculated temperatures is based on the ranking of their influences on variation of temperature predictions. Thus, selected input parameters include those with high sensitivity and those with large uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of AGR calculated temperatures. Expert judgment is used as the basis to specify the uncertainty range for selected input parameters. The input uncertainties are dynamic accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperature and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the first-order (linear) expansion terms in constructing the response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, time-dependent sensitivity over the test campaign duration was obtained using an interpolation scheme over the input parameter domain. This allows computation of uncertainty for the predicted peak fuel temperatures and the predicted graphite temperatures at TC locations over the whole AGR irradiation period.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

THE ARCONS PIPELINE: DATA REDUCTION FOR MKID ARRAYS

The Array Camera for Optical to Near-IR Spectrophotometry, or ARCONS, is a camera based on Microwave Kinetic Inductance Detectors (MKIDs), a new technology that has the potential for broad application in astronomy. Using an array of MKIDs, the instrument is able to produce time-resolved imaging and low-resolution spectroscopy constructed from detections of individual photons. The arrival time and energy of each photon are recorded in a manner similar to X-ray calorimetry, but at higher photon fluxes. The technique works over a very large wavelength range, is free from fundamental read noise and dark-current limitations, and provides microsecond-level timing resolution. Since the instrument reads out all pixels continuously while exposing, there is no loss of active exposure time to readout. The technology requires a different approach to data reduction compared to conventional CCDs. Here we outline here the prototype data reduction pipeline developed for ARCONS, though many of the principles are also more broadly applicable to energy-resolved photon counting arrays (e.g., transition edge sensors, superconducting tunnel junctions). We describe the pipeline's current status, and the algorithms and techniques employed in taking data from the arrival of photons at the MKID array to the production of images, spectra, and time-resolved light curves.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗