Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Task analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Henson v1.0

Modern scientific and engineering simulations track the time evolution of billions of elements. For such large runs, storing most time steps for later analysis is not a viable strategy. It is far more efficient to analyze the simulation data while it is still in memory. The developers present a novel design for running multiple codes in situ: using coroutines and position-independent executables they enable cooperative multitasking between simulation and analysis, allowing the same executables to post-process simulation output, as well as to process it on the fly, both in situ and in transit. They present Henson, an implementation of our design, and illustrate its versatility by tackling analysis tasks with different computational requirements. Our design differs significantly from the existing frameworks and offers an efficient and robust approach to integrating multiple codes on modern supercomputers. The presented techniques can also be integrated into other in situ frameworks.

Monozov, Dmitriy↗
DOE OSTI2021

MoorPy (Quasi-Static Mooring Analysis in Python)

MoorPy is a quasi-static mooring model and a suite of associated functions for mooring system analysis. The core model supports quasi-static analysis of moored floating systems including any arrangement of mooring lines and floating platforms. It solves the distributed position and tension of each mooring line segment using standard catenary equations. Floating platforms can be represented with linear hydrostatic characteristics. MoorPy automatically computes a floating system's equilibrium state and can be queried to identify a mooring system's nonlinear force-displacement relationships. Linearized stiffness matrices are efficiently computed using semi-analytic Jacobians. MoorPy also includes plotting functions and a library of mooring component property and cost coefficients. MoorPy can be used directly from Python scripts to perform mooring design and analysis tasks, or it can be coupled with other tools to compute quasi-static mooring reactions as part of a larger simulation.

Hall, Mathew↗
DOE OSTI2025

TalkPipe

SAND2025-11168O TalkPipe is a software tool to help users create and manage complex data analysis tasks involving Large Language Models. Its easy-to-use interface allows users to combine different analytical processes. TalkPipe includes a Python library, a scripting language, and can be run in a Docker container, making it simple to customize and extend. 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.

Bauer, Travis [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

artdaq

The artdaq toolkit is a data-acquisition framework designed for high-energy physics experiments. It provides a flexible, reliable backbone for data transfers and has several locations where users can perform custom analysis tasks using the art framework.

Flumerfelt, EricL. [Fermi National Accelerator Lab↗
DOE OSTI2018

MaDaTS: Managing Data on Tiered Storage for Scientific Workflows

Scientific workflows are processing large amounts of data through complex simulation and analysis tasks. Meanwhile, the need to minimize I/O costs on next generation systems and the evolution of new technologies (NVRAMs, SSDs etc.) is resulting in deeper storage hierarchies on High Performance Computing (HPC) systems. A multi-tiered storage hierarchy introduces complexities in workflow and data management. There is need for simple and flexible data abstractions that can allow users to seamlessly manage workflow data and tasks on HPC systems with multiple storage tiers. MaDaTS (Managing Data on Tiered Storage for Scientific Workflows) provides an API and a command-line tool that allows users to manage their workflows and data on tiered storage (Ghoshal & Ramakrishnan (2017)).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Performance Characterization and Validation of mocfe_bone (Contribution to the CESAR Annual Report FY12)

In the first phase of CESAR, the performance analysis task focused on understanding the baseline performance characteristics of mocfe bone | studying its behavior under different parallelization scenarios and comparing its scaling behavior to the MOCFE solver in the main application, UNIC. The results will help drive the co-design process for this application, which is one of the three target applications in CESAR, and will help identify current and future scaling bottlenecks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Validating the Melusine Gamma Spectroscopy Tool

This technical report describes testing to evaluate the gamma spectroscopy tool, Melusine, under development by Pacific Northwest National Laboratory. The goal was to verify that the software can successfully be used to provide accurate results and statistical uncertainties for the detection of isotopes of interest and their activities. Of special interest were spectra similar to those produced by radionuclide stations that contribute to the Comprehensive Nuclear Test Ban Treaty Organization’s International Monitoring System. Two data sets were used to test Melusine’s capabilities. The first was the result of a multi-lab calibration effort based on neutron activations produced at the University of California at Davis. The second was taken from the Proficiency Test Exercises conducted by the CTBTO directly in 2005. In 37 of 42 cases, Melusine produced results in agreement with the best answer presently available, in most cases with calculated uncertainties comparable to or better than competing analyses. In fact, Melusine technically provided one more result than CTBTO’s PTE analyses that agreed with the “book answer” (Monte Carlo simulation). Despite these promising results, the Melusine software is still under development. Effort is especially needed to simplify its analysis process, improve stability, and provide user documentation. Some significant analysis tasks require further vetting, such as those to address summing effects. However, our test results indicate that Melusine’s calculations as presently implemented are sound and can be used to reliably analyze spectra from the CTBTO’s radionuclide stations.

CTBTO↗
DOE OSTI2013

Plug-and -Play Model Architecture and Development Environment for Powertrain/Propulsion System - Final CRADA Report

Several tools already exist to develop detailed plant model, including GT-Power, AMESim, CarSim, and SimScape. The objective of Autonomie is not to provide a language to develop detailed models; rather, Autonomie supports the assembly and use of models from design to simulation to analysis with complete plug-and-play capabilities. Autonomie provides a plug-and-play architecture to support this ideal use of modeling and simulation for math-based automotive control system design. Models in the standard format create building blocks, which are assembled at runtime into a simulation model of a vehicle, system, subsystem, or component to simulate. All parts of the graphical user interface (GUI) are designed to be flexible to support architectures, systems, components, and processes not yet envisioned. This allows the software to be molded to individual uses, so it can grow as requirements and technical knowledge expands. This flexibility also allows for implementation of legacy code, including models, controller code, processes, drive cycles, and post-processing equations. A library of useful and tested models and processes is included as part of the software package to support a full range of simulation and analysis tasks, immediately. Autonomie also includes a configuration and database management front end to facilitate the storage, versioning, and maintenance of all required files, such as the models themselves, the model’s supporting files, test data, and reports. During the duration of the CRADA, Argonne has worked closely with GM to implement and demonstrate each one of their requirements. A use case was developed by GM for every requirement and demonstrated by Argonne. Each of the new features were verified by GM experts through a series of Gate. Once all the requirements were validated they were presented to the directors as part of GM Gate process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Energy Auditor and Quality Control Inspector Competency Model

The Energy Auditor (EA) and Quality Control Inspector (QCI) Competency model was developed to identify the soft skills, foundational competencies and define the levels of Knowledge, Skills, and Abilities (KSAs) required to successfully perform the tasks defined in the EA and QCI Job Task Analysis (JTAs), the U.S. Department of Energy (DOE) used the U.S. Department of Labor's (DOL) Competency Model Clearinghouse resources to develop a QCI and EA Competency Model. To keep the QCI and EA competency model consistent with other construction and energy management competency models, DOE and the National Renewable Energy Laboratory used the existing 'Residential Construction Competency Model' and the 'Advanced Commercial Building Competency Model' where appropriate.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Battery Second Use Analysis and Demonstration: Cooperative Research and Development Final Report, CRADA Number CRD-13 -537

The goal of this project is to test the performance of used automotive batteries providing stationary energy storage services to the grid, identifying barriers and opportunities for widespread battery second use strategies. These goals will be achieved by the installation and testing of an energy storage system comprised of used EV batteries and supporting data provided by BMW. The National Renewable Energy Laboratory (NREL) will complete test planning, modeling, and analysis tasks in support of this effort. Once complete, results will be applied to make recommendations on use strategies for used EV batteries, and to identify areas requiring further research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

BSSD 2019 Performance Metric Q4

The LANL SFA in Terrestrial Microbial Carbon Cycling aims to inform climate modeling and enable carbon management in terrestrial ecosystems by discovering widespread biological processes that control carbon storage and release in temperate biome soils. To achieve these goals, computational approaches are essential to analyze increasingly complex ‘omics’ data from microbial communities in environmental samples. The SFA continues to leverage advances in ‘omics' measurement technology and computation tools to decipher microbial community function in environmental samples. The computational approach is aligned with the recent shift in the SFA’s research approach. When the SFA began 10 years ago, computational approaches principally addressed “who is there” with extremely limited insight into function. Accessible ‘omics' data for complex soil communities were primarily amplicon DNA sequences of taxonomic or functional marker genes obtained by PCR, cloning, and Sanger sequencing. Computational tools for community analysis were nascent. Over the past decade, genome resources (DOE-Integrated Microbial Genomics database) and data acquisition increased 1000-fold, common computational pipelines for many analysis tasks have arisen, and the variety of ‘omics’ measurements has expanded to routinely include shotgun metagenomics, metatranscriptomics, and versions of metabolomics. The SFA is capitalizing on these advances while also developing new computational techniques to decipher how communities with different patterns of carbon cycling function from the species to ecosystem level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

RAMSeS: Rapid Analysis of Mission Software Systems

Over the past few decades, software has become ubiquitous as it has been integrated into nearly every aspect of society, including household appliances, consumer electronics, industrial control systems, public utilities, government operations, and military systems. Consequently, many critical national security questions can no longer be answered convincingly without understanding software, including its purpose, its capabilities, its flaws, its communication, or how it processes and stores data. As software continues to become larger, more complex, and more widespread, our ability to answer important mission questions and reason about software in a timely way is falling behind. Today, to achieve such understanding of third-party software, we rely predominantly on the ability of reverse engineering experts to manually answer each particular mission question for every software system of interest. This approach often requires heroic human effort that nevertheless fails to meet current mission needs and will never scale to meet future needs. The result is an emerging crisis: a massive and expanding gap between the national security need to answer mission questions about software and our ability to do so. Sandia National Laboratories has established the Rapid Analysis of Mission Software Systems (RAMSeS) effort, a collaborative long-term effort aimed at dramatically improving our nation’s ability to answer mission questions about third-party software by growing an ecosystem of tools that augment the human reverse engineer through automation, interoperability, and reuse. Focusing on static analysis of binary programs, we are attempting to identify reusable software analysis components that advance our ability to reason about software, to automate useful aspects of the software analysis process, and to integrate new methodologies and capabilities into a working ecosystem of tools and experts. We aim to integrate existing tools where possible, adapt tools when modest modifications will enable them to interoperate, and implement missing capability when necessary. Although we do hope to automate a growing set of analysis tasks, we will approach this goal incrementally by assisting the human in an ever-widening range of tasks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Status Update on the High Precision Isotopic Measurements on High Burnup LWR Fuel in 2020

The US Department of Energy (DOE) Office of Nuclear Energy (NE) is currently investigating the feasibility of directly disposing dual-purpose (storage and transportation) canisters (DPCs) in a spent nuclear fuel (SNF) repository. Criticality during the repository performance period (10,000 years or more) is one of the major concerns related to direct disposal of DPCs, specifically as the system undergoes degradation in the repository environment and timeframe. Oak Ridge National Laboratory (ORNL) is developing an as-loaded criticality analysis methodology using full (actinides + fission products) burnup credit that exploits the inherent criticality margin associated with actual canister-specific loading configuration. Burnup credit criticality analysis requires validation of the depletion/decay codes used to generate the burned isotopic inventory of an assembly by comparing the code-predicted inventory with experimentally determined isotopic data. Currently, isotopic measurement data for boiling water reactors (BWR) SNF are limited, and additional measurements will be highly beneficial for BWR burnup credit analysis, which is essential to demonstrate disposability of BWR DPCs. Moreover, new pressurized water reactor (PWR) samples of isotopic measurements will expand the PWR sample population and consequently will greatly improve sample statistics. In turn, this will reduce uncertainty in the computational determination of the isotopic composition of commercial SNF and eliminate additional penalties currently used for lack of data. As part of the isotopic analysis task, eight diverse, high burnup (HBU) samples from several PWR rods are being dissolved and isotopically analyzed to provide high-quality measurement data for PWR SNF to reducing the uncertainties associated with PWR isotopic depletion validation. The isotopics of interest focus on nuclides important to burnup credit, shielding, and decay heat in PWR SNF. Additionally, eight high burnup BWR fuel samples from the Limerick nuclear power plant have been identified for measurement to support reducing uncertainties associated with BWR isotopic depletion validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Classification of Orbits in Poincare Maps Using Machine Learning

Poincare plots, also called Poincare maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x, y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Vectorization of Dynamic Subgraphs via Generative Models (Final Report)

An important class of data analysis tasks stem from comparing subsets of connected records within massive sets of complex relational data. A common approach is to represent each set of connected records with a small graph, or set of data entities (graph vertices) and their relationships (graph edges), and efficient methods to gauge similarity for pairs of graphs are of high interest. This project concentrated on dynamic graphs, where each edge record has an associated timestamp denoting the time of observation. Pre-existing techniques for comparing dynamic graphs concentrate on either computing graph edit distance (number of vertex and edge deletion, addition, and timestamp modifications) or vectorizing the graph with counts of a limited set of dynamic graph motifs (tiny fundamental subgraphs) and computing distances between the vectors. These approaches are less able to see similarities in graphs that are fairly different in size but come from identical graph generation processes. The motif counting approach can be improved for graphs from the same process, but suffers from requiring many types of motifs meaning it is expensive. Moreover, many motifs are not present for small graphs, meaning realizing a a much larger graph came from the same process is difficult.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Documentation for Python Automation in CYME for DER Hosting Capacity Analysis of Different Feeder Configurations

This file provides the documentation for several Python scripts that were developed by Sandia National Laboratories to enhance the automation and customization capabilities for performing various distribution system planning and analysis tasks in CYME. Specifically, these scripts (.py files detailed in Figure 1) enable the user to evaluate different distribution system configurations and the resulting impacts on hosting capacity results and other metrics. In general, these scripts—and the accompanying documentation—provide the foundation upon which future customized tools can be created. For example, the scripts show how to extract and modify parameters of various circuit components, set up and run analyses using built-in CYME tools (iteratively), and export reports for further evaluations and comparisons. Thus, the capabilities and syntaxes used in the scripts can be adapted and leveraged for countless other objectives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Towards A Unified HFE Process For The Nuclear Industry

As nuclear power utilities embark on projects to upgrade and modernize power plants, they are likely to discover that traditional engineering methods do not typically make provision for the integration of human considerations. In addition, human factors professionals will find that traditional human performance methods such as function allocation, task analysis, human reliability analysis and human-machine interface design do not scale well to the complexity of a large-scale nuclear power upgrade project. Up-to-date human factors engineering processes, methods, techniques and tools are required to perform these kinds of analyses. This need is recognized widely in industry and an important part of the Department of Energy’s Light Water Reactor Sustainability Program deals with identifying potential impacts of emerging technologies on human performance and the technical bases needed to address them. However, so far no formal initiative has been launched to deal with the lack of integrated processes. Although human factors integration frameworks do exist in industries such as aviation or defense, no formal integrated human factors process exists in the nuclear industry. As a first step towards creating such a process, a “unified human factors engineering process” is proposed as a framework within which engineering organizations, human factors practitioners and regulatory bodies can ensure that human factors requirements are embedded in engineering activities throughout the upgrade project life cycle.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

SDS: A Framework for Scientific Data Services

Large-scale scientific applications typically write their data to parallel file systems with organizations designed to achieve fast write speeds. Analysis tasks frequently read the data in a pattern that is different from the write pattern, and therefore experience poor I/O performance. In this paper, we introduce a prototype framework for bridging the performance gap between write and read stages of data access from parallel file systems. We call this framework Scientific Data Services, or SDS for short. This initial implementation of SDS focuses on reorganizing previously written files into data layouts that benefit read patterns, and transparently directs read calls to the reorganized data. SDS follows a client-server architecture. The SDS Server manages partial or full replicas of reorganized datasets and serves SDS Clients' requests for data. The current version of the SDS client library supports HDF5 programming interface for reading data. The client library intercepts HDF5 calls and transparently redirects them to the reorganized data. The SDS client library also provides a querying interface for reading part of the data based on user-specified selective criteria. We describe the design and implementation of the SDS client-server architecture, and evaluate the response time of the SDS Server and the performance benefits of SDS.

59 BASIC BIOLOGICAL SCIENCES↗