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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 OSTI2016

TH-A-207B-02: QIBA Ultrasound Elasticity Imaging System Biomarker Qualification and User Testing of Systems

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: Introduction: Importance of tissue elasticity measurement Strain vs. shear wave elastography (SWE), beneficial features of SWE The link between shear wave speed and material properties, influence of viscosity Generation of shear waves External vibration (Fibroscan) ultrasound radiation force Point push Supersonic push (Aixplorer) Comb push (GE Logiq E9) Detection of shear waves Motion detection from pulse-echo ultrasound Importance of frame rate for shear wave imaging Plane wave imaging detection How to achieve high effective frame rate using line-by-line scanners Shear wave speed calculation Time to peak Random sample consensus (RANSAC) Cross correlation Sources of bias and variation in SWE Tissue viscosity Transducer compression or internal pressure of organ Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: Goals Review the need for quantitative medical imaging Provide examples of quantitative imaging biomarkers Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization Review the QIBA process for creating a quantitative biomarker Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile Underlying Premise and Assumptions Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice Reasons for quantification Evidence based medicine requires objective, not subjective observer data Computerized decision support tools (eg CAD) generally require quantitative input. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems What is quantitative imaging? Definition from Imaging Metrology Workshop The Quantitative Imaging Biomarker Alliance Formation 2008 Mission Structure Example Imaging Biomarkers Being Explored Biomarker Selection Groundwork Draft Protocol for imaging and data evaluation QIBA Profile Drafting Equipment and Site Validation Technical Clinical Site and Equipment QA and Compliance Checking Ultrasound Elasticity Estimation Biomarker US Elasticity Estimation Background Current Status and Problems Biomarker Selection-process and outcome US SWS for Liver Fibrosis Biomarker Work Groundwork Literature search and analysis results Phase I phantom testing-Elastic phantoms Phase II phantom testing-Viscoelastic phantoms Digital Simulated Data Protocol and Profile Drafting Protocol: based on UPICT and existing literature and standards bodies protocols Profile-Current claims, Manufacturer specific appendices What comes after the profile Profile Validation Technical validation Clinical validation QA and Compliance Possible approaches Site Operator testing Site protocol re-evaluation Imaging system Manufacturer testing and attestation User acceptance testing and periodic QA Phantom Tests Digital Phantom Based Testing Standard QA Testing Remediation Schemes Profile Evolution Towards additional applications Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior

Most of the existing graph analytics for understanding social be- havior focuses on learning from static rather than dynamic graphs using hand-crafted network features or recently emerged graph embeddings learned independently from a downstream predictive task, and solving predictive (e.g., link prediction) rather than fore- casting tasks directly. To address these limitations, we propose (1) a novel task – forecasting user interactions over dynamic social graphs, and (2) a novel deep learning, multi-task, node-aware at- tention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings. Our model relies on graph convolutions and recurrent layers to forecast future social behavior and interaction patterns in dynamic social graphs. We evaluate our model on the ability to forecast the number of retweets and mentions of a specific news source on Twitter (focusing on deceptive and credible news sources) with R2 of 0.79 for retweets and 0.81 for mentions. An additional evaluation includes model forecasts of user-repository interactions on GitHub and comments to a specific video on YouTube with a mean absolute error close to 2% and R2 exceeding 0.69. Our results demonstrate that learning from connectivity information over time in combination with node embeddings yields better forecasting results than when we incorporate the state-of-the-art graph em- beddings e.g., Node2Vec and DeepWalk into our model. Finally, we perform in-depth analyses to examine factors that influence model performance across tasks and different graph types e.g., the influence of training and forecasting windows as well as graph topological properties.

Shrestha, Prasha↗
DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗
DOE OSTI2016

TagDigger: user-friendly extraction of read counts from GBS and RAD-seq data

In genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), read depth is important for assessing the quality of genotype calls and estimating allele dosage in polyploids. However, existing pipelines for GBS and RAD-seq do not provide read counts in formats that are both accurate and easy to access. Additionally, although existing pipelines allow previously-mined SNPs to be genotyped on new samples, they do not allow the user to manually specify a subset of loci to examine. Pipelines that do not use a reference genome assign arbitrary names to SNPs, making meta-analysis across projects difficult. We created the software TagDigger, which includes three programs for analyzing GBS and RAD-seq data. The first script, tagdigger_interactive.py, rapidly extracts read counts and genotypes from FASTQ files using user supplied sets of barcodes and tags. Input and output is in CSV format so that it can be opened by spreadsheet software. Tag sequences can also be imported from the Stacks, TASSEL-GBSv2, TASSEL-UNEAK, or pyRAD pipelines, and a separate file can be imported listing the names of markers to retain. A second script, tag_manager.py, consolidates marker names and sequences across multiple projects. A third script, barcode_splitter.py, assists with preparing FASTQ data for deposit in a public archive by splitting FASTQ files by barcode and generating MD5 checksums for the resulting files. TagDigger is open-source and freely available software written in Python 3. It uses a scalable, rapid search algorithm that can process over 100 million FASTQ reads per hour. TagDigger will run on a laptop with any operating system, does not consume hard drive space with intermediate files, and does not require programming skill to use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

42 ENGINEERING↗
DOE OSTI2019

GenomeGraphR: A user-friendly open-source web application for foodborne pathogen whole genome sequencing data integration, analysis, and visualization

Food safety risk assessments and large-scale epidemiological investigations have the potential to provide better and new types of information when whole genome sequence (WGS) data are effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have grown significantly through improvements in technology, coordination, and collaboration, such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not often coupled with high-quality epidemiological or food chain metadata. We have created a set of tools for cleaning, curation, integration, analysis and visualization of microbial genome sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates in 2018). GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a user-friendly interface that allows a user to query a variety of research questions such as, transmission sources and dynamics, global reach, and persistence of genotypes associated with contamination in the food supply and foodborne illness across time or space. The application is freely available (https://fda-riskmodels.foodrisk.org/ genomegraphr/).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Recent developments targeting new and experienced users in EXPGUI, an open source Rietveld analysis interface.

EXPGUI is an open source interface that supplements the GSAS program package, which together provide a powerful set of tools for structure refinement, especially Rietveld Analysis. The combined GSAS-EXPGUI packages are freely distributed and are ideal for both new and experienced users. The latest EXPGUI version includes many new intuitive features including methods of implementing distance restraints, fixing coordinates, advanced searching and viewing of interatomic distances and angles as well as improved user friendliness and much more.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Graphical User Interface for Simplified Neutron Transport Calculations

A number of codes perform simple photon physics calculations. The nuclear industry is lacking in similar tools to perform simplified neutron physics shielding calculations. With the increased importance of performing neutron calculations for homeland security applications and defense nuclear nonproliferation tasks, having an efficient method for performing simple neutron transport calculations becomes increasingly important. Codes such as Monte Carlo N-particle (MCNP) can perform the transport calculations; however, the technical details in setting up, running, and interpreting the required simulations are quite complex and typically go beyond the abilities of most users who need a simple answer to a neutron transport calculation. The work documented in this report resulted in the development of the NucWiz program, which can create an MCNP input file for a set of simple geometries, source, and detector configurations. The user selects source, shield, and tally configurations from a set of pre-defined lists, and the software creates a complete MCNP input file that can be optionally run and the results viewed inside NucWiz.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2011

EMPHASIS/Nevada UTDEM user guide. Version 2.0.

The Unstructured Time-Domain ElectroMagnetics (UTDEM) portion of the EMPHASIS suite solves Maxwell's equations using finite-element techniques on unstructured meshes. This document provides user-specific information to facilitate the use of the code for applications of interest. UTDEM is a general-purpose code for solving Maxwell's equations on arbitrary, unstructured tetrahedral meshes. The geometries and the meshes thereof are limited only by the patience of the user in meshing and by the available computing resources for the solution. UTDEM solves Maxwell's equations using finite-element method (FEM) techniques on tetrahedral elements using vector, edge-conforming basis functions. EMPHASIS/Nevada Unstructured Time-Domain ElectroMagnetic Particle-In-Cell (UTDEM PIC) is a superset of the capabilities found in UTDEM. It adds the capability to simulate systems in which the effects of free charge are important and need to be treated in a self-consistent manner. This is done by integrating the equations of motion for macroparticles (a macroparticle is an object that represents a large number of real physical particles, all with the same position and momentum) being accelerated by the electromagnetic forces upon the particle (Lorentz force). The motion of these particles results in a current, which is a source for the fields in Maxwell's equations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

EMPHASIS/Nevada CABANA user Guide. Version 2.0.

The CABle ANAlysis (CABANA) portion of the EMPHASIS{trademark} suite is designed specifically for the simulation of cable system-generated electromagnetic pulse (SGEMP). The code can be used to evaluate the response of a specific cable design to threat or to compare and minimize the relative response of difference designs. This document provides user-specific information to facilitate the application of the code to cables of interest. It solves the electrical portion of a cable SGEMP simulation. It takes specific results from the deterministic radiation-transport code CEPTRE as sources and computes the resulting electrical response to an arbitrary cable load. The cable geometry itself is also arbitrary and is limited only by the patience of the user in meshing and by the available computing resources for the solution. The CABANA simulation involves solution of the quasi-static Maxwell equations using finite-element method (FEM) techniques.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Sierra/SolidMechanics 4.22 user's guide : addendum for shock capabilities.

This is an addendum to the Sierra/SolidMechanics 4.22 User's Guide to document additional capabilities that are available for use in the Presto{_}ITAR code that are not available for use in the standard version of Sierra/SolidMechanics (Sierra/SM). Presto{_}ITAR is an enhanced version of Sierra/SM that provides capabilities that make it regulated under the U.S. Department of State's International Traffic in Arms Regulations (ITAR) export-control rules. This code is part of the Vivace product, and is only distributed to entities that comply with ITAR regulations. The enhancements primarily focus on material models that include an energy-dependent pressure response, appropriate for very large deformations and strain rates. Since this is an addendum to the standard Sierra/SolidMechanics User's Guide, please refer to that document first for general descriptions of code capability and use. This addendum documents material models and element features that support energy-dependent material models.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

C++ Tensor Toolbox User Manual (V.1.0)

The C++ Tensor Toolbox is a software package for computing tensor decompositions. It is based on the Matlab Tensor Toolbox, and is particularly optimized for sparse data sets. This user manual briefly overviews tensor decomposition mathematics, software capabilities, and installation of the package. Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors in C++. The Toolbox compiles into libraries and is intended for use with custom applications written by users.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

2012 PATRIOT SCRIPT User's Guide

PATRIOT Script is an application that executes Patriot batch runs. This document provides a description of this application and how to run it. The basic user access tool PATRIOT Client allows a user to generate several most reliable paths in one run: one can specify a list of sources (origins) and targets, and PATRIOT finds for a given architecture option and one choice of device all the most reliable paths between all these sources and targets. The main objective of PATRIOT Script is to provide a tool for making automatic PATRIOT runs not only for a prespecified set of sources and targets, but also for a pre-specified set of devices and various architecture options. Running PATRIOT Script requires two basic steps that will be explained in more detail next: (1) Pre-preparation of an excel spreadsheet with the information about the desired runs; and (2) Opening the PATRIOT Script application, reading in the excel-spreadsheet and running the desired scenarios. Sections 1 and 2 explain each of these steps, and section 3 describes the output of the PATRIOT Script. For a detail description of the models and data behind PATRIOT and a detailed explanation of all the architecture options see [1]. For instructions of how to run PATRIOT Client see [2].

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2012

Hydra-TH User's Manual, Dated: December 1, 2011 (V.LA-CC-11-120)

Hydra-TH is a hybrid finite-element/finite-volume code built using the Hydra toolkit specifically to attack a broad class of incompressible, viscous fluid dynamics problems prevalent in the thermal-hydraulics community. The purpose for this manual is provide sufficient information for an experience analyst to use Hydra-TH in an effective way. The Hydra-TH User's Manual present a brief overview of capabilities and visualization interfaces. The execution and restart models are described before turning to the detailed description of keyword input. Finally, a series of example problems are presented with sufficient data to permit the user to verify the local installation of Hydra-TH, and to permit a convenient starting point for more detailed and complex analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Numerical Manufacturing And Design Tool (NuMAD v2.0) for Wind Turbine Blades: User’s Guide

Sandia National Laboratories has an on-going effort to reduce the cost of energy and improve reliability for wind systems through improved blade design and manufacture. As part of this effort, a software tool named NuMAD (Numerical Manufacturing And Design) has been developed to greatly simplify the process of creating a three-dimensional finite element model for a modern wind turbine blade. NuMAD manages all blade information including databases of airfoils, materials, and material placement to enable efficient creation of models. NuMAD is a stand-alone, user-friendly, graphical pre-processor for the ANSYS® commercial finite element package. The blade information contained in the NuMAD database is also used to manage capabilities such as output for CFD mesh, computation of blade cross section properties, and aeroelastic instability analysis of the blade. This user’s manual describes the capabilities and usage of NuMAD.

Berg, Jonathan Charles↗
DOE OSTI2012

Level-2 Milestone 4470: Early Users on Unclassified Sequoia Hardware

This report documents the delivery and installation of Sequoia and that early users have successfully run codes on the Sequoia machine as part of the requirements for ASC L2 Milestone 4470: Early Users on the Unclassified Sequoia Hardware, due September 30, 2012. The full text of the milestone is included in Attachment 1.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Operational Philosophy for the Advanced Test Reactor National Scientific User Facility

In 2007, the Department of Energy (DOE) designated the Advanced Test Reactor (ATR) as a National Scientific User Facility (NSUF). At its core, the ATR NSUF Program combines access to a portion of the available ATR radiation capability, the associated required examination and analysis facilities at the Idaho National Laboratory (INL), and INL staff expertise with novel ideas provided by external contributors (universities, laboratories, and industry). These collaborations define the cutting edge of nuclear technology research in high-temperature and radiation environments, contribute to improved industry performance of current and future light-water reactors (LWRs), and stimulate cooperative research between user groups conducting basic and applied research. To make possible the broadest access to key national capability, the ATR NSUF formed a partnership program that also makes available access to critical facilities outside of the INL. Finally, the ATR NSUF has established a sample library that allows access to pre-irradiated samples as needed by national research teams.

99 GENERAL AND MISCELLANEOUS↗