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All sources NASA NTRS DOE OSTI

DOE OSTI2016

Scale-Free Compact Routing Schemes in Networks of Low Doubling Dimension

In this work, we consider compact routing schemes in networks of low doubling dimension, where the doubling dimension is the least value α such that any ball in the network can be covered by at most 2 α balls of half radius. There are two variants of routing-scheme design: (i) labeled (name-dependent) routing, in which the designer is allowed to rename the nodes so that the names (labels) can contain additional routing information, for example, topological information; and (ii) name-independent routing, which works on top of the arbitrary original node names in the network, that is, the node names are independent of the routing scheme. In this article, given any constant ε ϵ (0, 1) and an n-node edge-weighted network of doubling dimension α ϵ O(loglog n), we present —a (1 + ε)-stretch labeled compact routing scheme with Γlog n-bit routing labels, O(log 2 n/loglog n)-bit packet headers, and ((1/ε) O(α) log 3 n)-bit routing information at each node; —a (9 + ε)-stretch name-independent compact routing scheme with O(log 2 n/loglog n)-bit packet headers, and ((1/ε) O(α) log 3 n)-bit routing information at each node. In addition, we prove a lower bound: any name-independent routing scheme with o(n (ε/60)2 ) bits of storage at each node has stretch no less than 9 - ε for any ε ϵ (0, 8). Therefore, our name-independent routing scheme achieves asymptotically optimal stretch with polylogarithmic storage at each node and packet headers. Note that both schemes are scale-free in the sense that their space requirements do not depend on the normalized diameter Δ of the network. Finally, we also present a simpler nonscale-free (9 + ε)-stretch name-independent compact routing scheme with improved space requirements if Δ is polynomial in n.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Translation-Invariant Quantum Algorithms for Ordered Search are Optimal

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses [log 2 n] queries for a list of length n. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of log 2 n for exact quantum algorithms is only known to lie between (ln2)/π ≈ 0.221 and 4/log 2 605 ≈ 0.4333. We consider a special class of translation-invariant algorithms with no workspace, introduced by Farhi, Goldstone, Gutmann, and Sipser, that has been used to find the best known upper bounds. First, we show that any bounded-error, k-query quantum algorithm for ordered search can be implemented by a k-query algorithm in this special class. Second, we use linear programming to show that the best exact 5-query quantum algorithm can search a list of length 7265, giving an ordered search algorithm that asymptotically uses 5 log 7265 n ≈ 0.390 log 2 n quantum queries.

Translation-invariant quantum algorithms↗

DOE OSTI2023

Impedance Response Influenced by Variability in the Random Distribution of Physical Properties of Coated Materials in Two-Dimensional Space

Heterogeneous physical characteristics of a system featuring a single-layer film on a metallic surface have been explored via its impedance response. The Nyquist plot showed a distorted semicircle, indicative of the system's unique distribution characteristics. Utilizing a copula-based probability method, a two-dimensional deterministic impedance model was successfully integrated, accounting for spatial physical properties such as permittivity and electrical conductivity. This strategy enabled in-depth exploration and mechanistic quantification of a broad spectrum of properties. A quantitative understanding of impedance signal alterations, characterized by normally or log-normally correlated variables, was achieved through the variation in aspect ratio and characteristic frequency of the impedance spectra. Log-normally distributed electrical properties provided a superior representation of the distorted impedance spectra. As coefficient of variation (CV) values fluctuated, the aspect ratio and characteristic frequency showed heightened sensitivity to log-normal permittivity compared to log-normal electrical conductivity. Notably, a marked positive linear correlation between electrical properties resulted in an impedance response that approximated perfect semicircular spectra. The variability in the electrical properties' distribution was demonstrated by considering the correlation coefficient between electrical conductivity and the z-direction position. Furthermore, the highest aspect ratio of the impedance spectra was observed when the electrical conductivity was randomly distributed across the z-direction space.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Gnawts

High Performance Computing (HPC) systems generate millions of inter-related event log lines per day from thousands of computers, providing feedback on both successful and unsuccessful operations within the system. Thorough analysis of these logs is often required for root cause analysis of problems and tuning for greatest performance. This "app" for the "Gnawts" log analysis software (http://www.gnawts.com) facilitates faster and deeper insights into HPC system logs.

Stearley, Jon↗

DOE OSTI2019

numericalFunctions

The numericalFunction C library is designed to handle point-wise interpolative data representing a mathematical function. The library currently handles 1-d functions (i.e., functions of the format f(x)). The main object is the ptwXYPoints C struct that holds list of x,y pairs sorted by ascending x-values with an interpolation rule. That is, it contains the 1-d function f(x) = y(x). Functions are included that support common unitary and binary operations (e.g., negation, addition, multiplication). As example, if f(x) and g(x) are two ptwXYPoints instances, then routines to calculate f(x) + g(x) and f(x) * g(x) are included. The library support storage of data with lin-lin, lin-log, log-lin, log-log and flat (or histogram) interpolation. Functions to convert any of the supported interpolation to lin-lin are included. However, many of the binary functions only support lin-lin interpolation. The library also contains a C struct for storing a Legendre series representation of a 1-d function, and functions to convert Legendre series to and from a ptwXYPoints instance. A python interface to the library is included, with the ptwXYPoints C struct treated as a class. This class has many of the standard python math methods (e.g., __add__, __mul__).

Beck, Bret↗

DOE OSTI2020

eBPF Community ID Calculation

The code titled “eBPF Community ID Calculation” allows easy correlation of monitoring logs with existing network forensics logs on high-performance computing (HPC) systems. This code implements the Community ID Flow Hashing standard to calculate community ID Flow identifiers. These identifiers will be used to correlate host forensics logs with network forensics logs. The correlation provides valuable insight and the ability to easily pivot between disparate data sources. The computer code removes the libpcap dependency from the code base.

Maerz, StefanA↗

DOE OSTI2019

Diventi

Diventi provides a write optimized index of network logs such as netflow or zeek. The program will ingest various network traffic logs (zeek connection logs or NetflowV5, v9 or IPFix logs) and index each IP address and time of its activity. This database can then be queried for timely tracking of network activity. It currently uses the ft-index library from Percona but is adaptable to any key-value store that supports the Berkeley DB api.SAND2019-15037 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kroeger, Thomas↗

DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗

DOE OSTI2014

Thermodynamic Model for the Solubility of Ba(SeO 4 , SO 4 ) Precipitates

The solubility of Ba(SeO 4 , SO 4 ) precipitates was determined as a function of the BaSeO 4 mole fractions, ranging from 0.0015 to 0.3830, and time with an equilibration period extending to as long as 302 days. Equilibrium/steady state conditions in this system are reached in ≤ 65 days. Pitzer’s ion interaction model was used to calculate solid and aqueous phase activity coefficients. Thermodynamic analyses showed that the data do not satisfy Gibbs-Duhem equation, thereby demonstrating that a single-solid solution phase does not control both the selenate and sulfate concentrations. Our extensive data with log 10 [Ba] ranging from -3.6 to -5.9 mol kg -1 , log 10 [SeO 4 ] rangingfrom-3.6 to -5.2 mol kg -1 , and log 10 [SO 4 ] ranging from-4.0 to -5.3 mol kg -1 can be explained with the formation of an ideal BaSeO 4 solid solution phase that controls the selenium concentrations and a slightly disordered/less-crystalline BaSO 4 (s) (log 10 Κ° sp = -9.5 instead of -10.05 for barite) that controls the sulfate concentrations. In these experiments the BaSO 4 component of the solid solution phase never reaches thermodynamic equilibrium with the aqueous phase. Thermodynamic interpretations of the data show that both the ideal BaSeO 4 solid solution phase and less-crystalline BaSO 4 (s) phase are in equilibrium with each other in the entire range of BaSeO 4 mole fractions investigated in this study.

Rai, D.↗

DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Recovery Act: Develop a Modular Curriculum for Training University Students in Industry Standard CO{sub 2} Sequestration and Enhanced Oil Recovery Methodologies

CO{sub 2} Enhanced Oil Recovery, Sequestration, & Monitoring Measuring & Verification are topics that are not typically covered in Geoscience, Land Management, and Petroleum Engineering curriculum. Students are not typically exposed to the level of training that would prepare them for CO{sub 2} reservoir and aquifer sequestration related projects when they begin assignments in industry. As a result, industry training, schools & conferences are essential training venues for new & experienced personnel working on CO{sub 2} projects for the first time. This project collected and/or generated industry level CO{sub 2} training to create modules which faculties can utilize as presentations, projects, field trips and site visits for undergrad and grad students and prepare them to "hit the ground running" & be contributing participants in CO{sub 2} projects with minimal additional training. In order to create the modules, UTPB/CEED utilized a variety of sources. Data & presentations from industry CO{sub 2} Flooding Schools & Conferences, Carbon Management Workshops, UTPB Classes, and other venues was tailored to provide introductory reservoir & aquifer training, state-of-the-art methodologies, field seminars and road logs, site visits, and case studies for students. After discussions with faculty at UTPB, Sul Ross, Midland College, other universities, and petroleum industry professionals, it was decided to base the module sets on a series of road logs from Midland to, and through, a number of Permian Basin CO{sub 2} Enhanced Oil Recovery (EOR) projects, CO{sub 2} Carbon Capture and Storage (CCUS) projects and outcrop equivalents of the formations where CO{sub 2} is being utilized or will be utilized, in EOR projects in the Permian Basin. Although road logs to and through these projects exist, none of them included CO{sub 2} specific information. Over 1400 miles of road logs were created, or revised specifically to highlight CO{sub 2} EOR projects. After testing a number of different entry points into the data set with students and faculty form a number of different universities, it was clear that a standard website presentation with a list of available power point presentations, excel spreadsheets, word documents and pdf's would not entice faculty, staff, and students at universities to delve deeper into the website http://www.utpb.edu/ceed/student modules.

02 PETROLEUM↗

DOE OSTI2014

The snake geothermal drilling project. Innovative approaches to geothermal exploration

The goal of our project was to test innovative technologies using existing and new data, and to ground-truth these technologies using slim-hole core technology. The slim-hole core allowed us to understand subsurface stratigraphy and alteration in detail, and to correlate lithologies observed in core with surface based geophysical studies. Compiled data included geologic maps, volcanic vent distribution, structural maps, existing well logs and temperature gradient logs, groundwater temperatures, and geophysical surveys (resistivity, magnetics, gravity). New data included high-resolution gravity and magnetic surveys, high-resolution seismic surveys, three slimhole test wells, borehole wireline logs, lithology logs, water chemistry, alteration mineralogy, fracture distribution, and new thermal gradient measurements.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Performance Evaluation of Darshan 3.0.0 on the Cray XC30

Abstract—Darshan is a lightweight I/O characterization tool used to gather and summarize salient I/O workload statistics from HPC applications. Darshan was designed to minimize any possible perturbations of an application’s performance, leading it to be enabled by default on a number of production HPC systems. For each file accessed by a given application, Darshan records the count and types of I/O operations performed, histograms of access sizes, cumulative timers on the amount of time spent doing I/O, and other statistical data. This type of data has proved invaluable in understanding and improving the I/O performance of HPC applications. Darshan 3.0.0 is the new modularized version of the traditional Darshan library and file format, allowing users to easily add more in-depth I/O characterization data to Darshan logs. In this work we perform an empirical evaluation of Darshan 3.0.0 to ensure that it continues to meet performance expectations for broad deployment. In particular, we evaluate the imposed overhead on instrumented I/O operations, time taken to shut down and generate corresponding Darshan log files, and resultant log file sizes for different workloads. These performance results are compared to results of Darshan 2.3.0 on the Edison XC30 system at NERSC to determine whether the new version is lightweight enough to run full-time on production HPC systems. Our evaluation shows that Darshan has limited impact on application I/O performance and can fully generate a corresponding log file for most application workloads in under two seconds.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Field Laboratory for Emerging Stacked Unconventional Plays in Central Appalachia

The goal of the Field Laboratory for Emerging Stacked Unconventional Plays (ESUP) in Central Appalachia project was to investigate and characterize the resource potential for multi-play production of emerging unconventional reservoirs in Central Appalachia. Project activities included drilling, logging and coring of a vertical characterization well drilled to basement to approximately 15,000 feet in depth. The data from drilling and well logs as well recovered core samples were used by the research team to support characterization of the geology and potential pay zones within the Nora Gas Field of southwestern Virginia and the greater Central Appalachian Basin. This project was led by Dr. Nino Ripepi of the Virginia Center for Coal and Energy Research (VCCER) in close collaboration with EnerVest Operating LLC (EnerVest). VCCER is housed in the Department of Mining and Minerals Engineering at Virginia Tech and was created by an Act of the Virginia General Assembly on March 30, 1977, as an interdisciplinary study, research, information, and resource facility for the Commonwealth of Virginia. EnerVest is a top tier, low-cost oil and natural gas company with a long history of traditional operating relationships with institutional investors as well as public and private companies. This report summarizes two major research activities: 1) Log and Core Analysis and 2) Numerical Modeling. Core analysis comprises the characterization efforts of the ESUP Field Laboratory and includes a discussion of the well logs run in the deep characterization wells, an initial analysis of those, the number, size and location of cores recovered, core testing and analysis done to date as well as plans for future analysis. Numerical modeling is comprised of seven major modeling efforts, including (1) Multiphysics shale transport modeling work, (2) the developed in-house compositional simulator studies, (3) reservoir modeling studies using GEM software, (4) fracture modeling work using EFRAC3D software and (5) numerical modeling efforts with ABAQUS software, (6) Numerical modeling efforts using FLAC3D and PFC3D, and (7) Automatic Machine Learning (AutoML) Studies.

02 PETROLEUM↗

DOE OSTI2024

Risk of theft and malicious use of radiation sources in transit

Remote radioactive source applications require frequent transportation of sources from storage locations to remote sites. This introduces risk of theft of a source during the transportation process, with the level of risk proportional to the radioactivity of the source. To that end, theft of smaller sources, such as microcurie-level moisture density gauges, are of minor concern, but larger sources, such as those used for radiography and well logging, present more risk. Radiography sources include 192 Ir, 75 Se, or 60 Co radionuclides with radioactivity amounts at or exceeding IAEA Category 2. Well-logging sources, primarily 241 Am/Be, are used for their neutron-emission properties. 137 Cs is also used in well-logging at lower activities than in radiography but at levels that still present some risk. The vulnerability for malicious use of such sources to cause contamination and associated economic effects is dependent on the elemental chemical and physical properties, especially melting point and bulk modulus. Theft of radiography sources is somewhat common, well-logging sources less so. Theft of sources commonly occurs in concert with theft of the vehicle, with the source subsequently abandoned. There have been some instances where a source appears to have been specifically targeted. There are a variety of security measures and protocols, available and under development, to mitigate the risk of theft and assist in source recovery.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Quantum simulation of real-space dynamics

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a d-dimensional Schrödinger equation with η particles can be simulated with gate complexity O ~ (ηdFpoly(log(g'/ϵ))), where ϵ is the discretization error, g' controls the higher-order derivatives of the wave function, and F measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on ϵ and g' from poly(g'/ϵ) to poly(log(g'/ϵ)) and polynomially improves the dependence on T and d, while maintaining best known performance with respect to η. For the case of Coulomb interactions, we give an algorithm using η 3 (d + η)Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates, and another using η 3 (4d) d/2 Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates and QRAM operations, where T is the evolution time and the parameter Δ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Streptococcus mutans Displays Altered Stress Responses While Enhancing Biofilm Formation by Lactobacillus casei in Mixed-Species Consortium

Like Streptococcus mutans , lactobacilli are commonly isolated from carious sites, although their exact role in caries development remains unclear. This study used mixed-species models to analyze biofilm formation by major groups of oral lactobacilli, including L. casei, L. fermentum, L. rhamnosus, L. salivarius ssp. salivarius, and L. gasseri . The results showed that lactobacilli did not form good biofilms when grown alone, although differences existed between different species. When grown together with S. mutans , biofilm formation by L. gasseri and L. rhamnosus was increased by 2-log ( P < 0.001), while biofilms by L. fermentum reduced by >1-log ( P < 0.001). L. casei enhanced biofilm formation by ~2-log when grown with S. mutans wild-type, but no such effects were observed with S. mutans deficient of glucosyltransferase GtfB and adhesin P1. Both S. mutans and L. casei in dual-species enhanced resistance to acid killing with increases of survival rate by >1-log ( P < 0.001), but drastically reduced the survival rates following exposure to hydrogen peroxide ( P < 0.001), as compared to the respective mono-species cultures. When analyzed by RNA-seq, more than 134 genes were identified in S. mutans in dual-species with L. casei as either up- or down-regulated when compared to those grown alone. The up-regulated genes include those for superoxide dismutase, NADH oxidase, and members of the mutanobactin biosynthesis cluster. Among the down-regulated genes were those for GtfB and alternative sigma factor SigX. These results further suggest that interactions between S. mutans and oral lactobacilli are species-specific and may have significant impact on cariogenic potential of the community.

Immunology↗

DOE OSTI2025

Guiding Principles for Geochemical/Thermodynamic Model Development and Validation in Nuclear Waste Disposal: A Close Examination of Recent Thermodynamic Models for H + —Nd 3+ —NO 3 - (—Oxalate) Systems

Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗