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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 OSTI2015

Directly imaging steeply-dipping fault zones in geothermal fields with multicomponent seismic data

For characterizing geothermal systems, it is important to have clear images of steeply-dipping fault zones because they may confine the boundaries of geothermal reservoirs and influence hydrothermal flow. Elastic reverse-time migration (ERTM) is the most promising tool for subsurface imaging with multicomponent seismic data. However, conventional ERTM usually generates significant artifacts caused by the cross correlation of undesired wavefields and the polarity reversal of shear waves. In addition, it is difficult for conventional ERTM to directly image steeply-dipping fault zones. We develop a new ERTM imaging method in this paper to reduce these artifacts and directly image steeply-dipping fault zones. In our new ERTM method, forward-propagated source wavefields and backward-propagated receiver wavefields are decomposed into compressional (P) and shear (S) components. Furthermore, each component of these wavefields is separated into left- and right-going, or downgoing and upgoing waves. The cross correlation imaging condition is applied to the separated wavefields along opposite propagation directions. For converted waves (P-to-S or S-to-P), the polarity correction is applied to the separated wavefields based on the analysis of Poynting vectors. Numerical imaging examples of synthetic seismic data demonstrate that our new ERTM method produces high-resolution images of steeply-dipping fault zones.

58 GEOSCIENCES↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Verification Of the Defense Waste Processing Facility's (DWPF) Process Digestion Methods for the Sludge Batch 8 Qualification Sample

This report contains the results and comparison of data generated from inductively coupled plasma – atomic emission spectroscopy (ICP-AES) analysis of Aqua Regia (AR), Sodium Peroxide/Sodium Hydroxide Fusion Dissolution (PF) and Cold Chem (CC) method digestions and Cold Vapor Atomic Absorption analysis of Hg digestions from the DWPF Hg digestion method of Sludge Batch 8 (SB8) Sludge Receipt and Adjustment Tank (SRAT) Receipt and SB8 SRAT Product samples. The SB8 SRAT Receipt and SB8 SRAT Product samples were prepared in the SRNL Shielded Cells, and the SRAT Receipt material is representative of the sludge that constitutes the SB8 Batch or qualification composition. This is the sludge in Tank 51 that is to be transferred into Tank 40, which will contain the heel of Sludge Batch 7b (SB7b), to form the SB8 Blend composition.

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

Improved Quantitative Dynamic Range of Time-of-Flight Mass Spectrometry by Simultaneously Waveform-Averaging and Ion-Counting Data Acquisition

Two different types of data acquisition methods, “averaging mode” and “ion-counting mode”, have been used in a time-of-flight (TOF) mass spectrometry. The most common method is an averaging mode that sums waveform signals obtained from each flight cycle. While it is possible to process many ions arriving at the same TOF in one flight cycle, low-abundance ions are difficult to measure because ion signals are overwhelmed by noises from the detection system. An ion-counting mode is suitable for the detection of such low-concentration ions, but counting loss occurs when two or more ions arrive at the detector within the dead time of the acquisition system. In this study, we introduce a technique that combines two methods to measure target ions with a high concentration difference, i.e., averaging mode and ion-counting mode are used simultaneously for high abundant and trace ions, respectively. By processing waveforms concurrently during data acquisition, one can choose to analyze either or both types of data to achieve a highly quantitative mass spectrum over a wide range of sample concentrations. The result of the argon isotope analysis shows that this method provides a more accurate determination of the isotope ratio compared to averaging mode alone at one-twentieth of the analysis time required by ion-counting alone. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Enhanced data validation strategy of air quality monitoring network

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Study of Orbitally Excited $B_{(s)}$ Mesons and Evidence for a New $B\pi$ Resonance with the CDF II Detector

This thesis presents an analysis of excited states of B0, B+ and B0 s mesons, decaying to B mesons while emitting a pion or kaon. They are reconstructed from their decay products and a selection is performed to discard wrongly reconstructed B(s) mesons with the multivariate analysis software NeuroBayes, as described in chapter 5. In the training process, the sPlot method and measured and simulated data are used. Chapter 6 describes how the properties of excited B(s) are determined by an unbinned maximum likelihood t to their mass spectra. The systematic uncertainties determined in this analysis are described in chapter 7. The results of this thesis are presented in chapter 8 and a conclusion is given in chapter 9. The results shown in this thesis have been published before in [1].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Using Nuclear Theory, Data and Uncertainties in Monte Carlo Transport Applications

These are slides for a presentation on using nuclear theory, data and uncertainties in Monte Carlo transport applications. The following topics are covered: nuclear data (experimental data versus theoretical models, data evaluation and uncertainty quantification), fission multiplicity models (fixed source applications, criticality calculations), uncertainties and their impact (integral quantities, sensitivity analysis, uncertainty propagation).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Methods for the robust computation of the long-period seismic spectrum of broad-band arrays

SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.

Caton, Ross C.↗
DOE OSTI2017

Fitting of Failure Rate Data to a Closed Form Solution using Gamma-Poisson Method of Moments

Due to the responsibility and gravity of estimation of nuclear power plant failure rates, it is required that there be consistent and accurate methodology for describing the likelihood of an event occurring. However, the methodology currently employed can vary dependent on the data, as well as subjective conjecture from the expert performing the analysis. The current implementation of the empirical Bayes method to a Gamma-Poisson (GaP) distribution utilizes algorithms to solve for the parameters that do not provide consistent answers. Additionally, to achieve a distribution of the likelihood, the variance of each parameter of the GaP must be determined. There does not exist an exact solution to one of the parameters variance and is typically estimated using techniques like the Kass-Steffey adjustment. Thus a new approach to the problem is proposed, built upon the method of moments for a negative binomial, based upon the work of Bradlow. Using the method of moments approach, we are able to achieve a closed form estimation of the mean and variance for each parameter in the negative binomial distribution. Due to the relationship between the negative binominal and GaP distribution, results can be made between the distributions. The hyper-priors defined assume beta prime distribution which is appropriately informed; the results of this application translate back to the Gamma distribution for easy utilization in SAPHIRE. Additionally several cases are explored using publically available NRC data that considers zero inflated, over dispersed, and Poisson data.

Failure Rate↗
DOE OSTI2015

Natural Reservoir Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin

The files included in this submission contain all data pertinent to the methods and results of this task's output, which is a cohesive multi-state map of all known potential geothermal reservoirs in our region, ranked by their potential favorability. Favorability is quantified using a new metric, Reservoir Productivity Index, as explained in the Reservoirs Methodology Memo (included in zip file). Shapefile and images of the Reservoir Productivity and Reservoir Uncertainty are included as well (hover over file display names to see actual file names in bottom-left corner of screen).

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

ARCADIA and advanced methods licensing and implementation update

AREVA NP is implementing a consistent code and methodology suite for core design and safety analysis, based on first principles modeling with an extremely broad verification and validation data base. Thanks to the high computational power increase in the last decades methods' development and application now include capabilities that were not available in the past. An example is the 3D coupled transient core power distribution characterization, which allows more realistic modeling of plant behavior, removal of inaccuracies by high resolution and access to local information. Another one is the progressive replacement, respectively complement of old deterministic methods by order statistics. This paper provides an update of the current licensing and implementation status of the main AREVA NP new codes (ARCADIA, GALILEO, S-RELAP5) and their associated advanced methods for PWR applications. ARCADIA is the state-of-the-art neutron transport/ thermal-hydraulics (TH)/ thermal-mechanics (TM) code system for PWR applications. Development is completed, implementation started in France, the Netherlands, the U.S.A. and other countries. The extension to BWR applications is on-going. GALILEO is the state-of-the-art fuel rod performance code for PWR and BWR applications. Development is completed, implementation started in France and the U.S.A. S-RELAP5 is a thermal-hydraulic code that has been completely re-coded and modernized in terms of software architecture. S-RELAP5 has been successfully coupled with ARCADIA. It is shown that advanced codes and integrated methods for accident analysis are keys for extending the fuel cycle design space.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

e RPCA : Robust Principal Component Analysis for Exponential Family Distributions

Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.

Zheng, Xiaojun↗
DOE OSTI2019

Identification of Water-Saturated Zones in a Protective Hydraulic Earthen Structure by Synchronous Electromagnetic and Seismic Sounding

Results of field experiments in synchronized investigation of the protection embankment of the tailing pond of JSC Kovdor Mining and Processing Plant by means of different wave methods of electromagnetic and seismic sounding are presented. A comparative analysis of the data is carried out by means of which boundaries of zones of different degrees of water saturation of the embankment soils may be reliably established. The ranges of velocity of the electromagnetic wave and range of values of the ratio of the velocities of the longitudinal and transverse elastic wave in the transition from dry dense soil of the embankment to moistened soil right up through water-saturated soil are determined.

42 ENGINEERING↗
DOE OSTI2024

Fast permeability measurement for tight reservoir cores using only initial data of the one chamber pressure pulse decay test

Here, in this study, a mathematical model for fast determination of the permeabilities of tight rocks using measurements taken from the initial period of the One Chamber Pressure Pulse Decay (OC-PPD) test is presented. The model applies to measurements taken both before and after the pressure pulse front has reached the downstream end of the specimen. The analytical solutions for the pressure decay in the upstream chamber are derived based on a parabolic arc approximation of pore pressure distribution along the test specimen. This approximation allows converting the initial–boundary value problem of fluid diffusion in the specimen, governed by partial differential equations, to a system of ordinary differential equations that can be easily solved by explicit formulae. Thus, an explicit formula for the pressure decay rate is obtained, which enables inverse analysis of the initial experimental data to estimate the rock permeability. The proposed method expedites the pulse decay test as it does not require the system to reach equilibrium. The method is validated with three sets of experimental data of the OC-PPD test using helium as the diffusing fluid, for which the relative error of the permeability is found to be less than 6%. This method is particularly useful if the equilibrium time of the pulse decay test for rock specimens with permeabilities in the range of nano-Darcy takes hours or days.

early-time solution↗
DOE OSTI2017

Background of SAM atom-fraction profiles

Atom-fraction profiles acquired by SAM (scanning Auger microprobe) have important applications, e.g. in the context of alloy surface engineering by infusion of carbon or nitrogen through the alloy surface. However, such profiles often exhibit an artifact in form of a background with a level that anti-correlates with the local atom fraction. This article presents a theory explaining this phenomenon as a consequence of the way in which random noise in the spectrum propagates into the discretized differentiated spectrum that is used for quantification. The resulting model of “energy channel statistics” leads to a useful semi-quantitative background reduction procedure, which is validated by applying it to simulated data. Subsequently, the procedure is applied to an example of experimental SAM data. The analysis leads to conclusions regarding optimum experimental acquisition conditions. The proposed method of background reduction is based on general principles and should be useful for a broad variety of applications. - Highlights: • Atom-fraction–depth profiles of carbon measured by scanning Auger microprobe • Strong background, varies with local carbon concentration. • Needs correction e.g. for quantitative comparison with simulations • Quantitative theory explains background. • Provides background removal strategy and practical advice for acquisition.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Interface capturing simulations of bubble population effects in PWR subchannels

As the computational power of high-performance computing (HPC) facilities grows, so too does the feasibility of using first principle based simulation to study turbulent two-phase flows within complex pressurized water reactor (PWR) geometries. Direct numerical simulation (DNS), integrated with an interface capturing method, allows for the collection of high-fidelity numerical data using advanced analysis techniques. The research presented here employs the massively parallel, finite-element based, unstructured mesh code, PHASTA, to simulate a set of two-phase bubbly flows through PWR subchannel geometries including auxiliary structures (spacer grids and mixing vanes). The main objective of the presented work is to analyze bubble dynamics and turbulence interactions at varying bubble concentrations to support the development of advanced two-phase flow closure models. Turbulent two-phase flows in PWR subchannels were simulated at hydraulic Reynolds numbers of 81,000 with bubble concentrations of 3%–15% by gas volume fraction (768–3928 resolved bubbles, respectively) and compared against a 1% void fraction case (262 bubbles) that had been previously simulated. The finite element mesh utilized for the study at higher bubble concentrations was composed of 1.55 billion elements, compared to the previous study which employed 1.11 billion elements, ensuring all turbulence scales and individual bubbles within the flow are fully resolved. For each case, the resolved initial bubble size was 0.65 mm in diameter (resolved with 25 grid points across the diameter). The simulations were analyzed to find flow features such as the mean velocity profile, bubble relative velocity and the effect of the bubbles on the turbulent conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗