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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 OSTI2023

PETSc/TAO Users Manual (Rev. 3.19)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and opti mization that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than packages such as MATLAB or a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or lldb, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. There are several packages, built on PETSc, that may satisfy your needs without requiring directly using PETSc. We recommend reviewing these packages functionality before starting to code directly with PETSc. PETSc can be used to provide a “MPI parallel linear solver” in an otherwise sequential, or OpenMP parallel code. This approach cannot provide extremely large improvements in the application time by utilizing large numbers of MPI processes but can still improve the performance. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect true scalability to large numbers of MPI processes. See PCMPI for details on how to utilize the PETSc MPI linear solver server. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc has been supported for twenty-five years; see mailing list information on our website for information on contacting support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Home Energy Scoring Tools (website) and Application Programming Interfaces, APIs (aka HEScore)

A web-based residential energy rating tool with APIs that runs the LBNL website: Provides customized estimates of residential energy use and energy bills based on building description information provided by the user. Energy use is estimated using engineering models developed at LBNL. Space heating and cooling use is based on the DOE-2. 1E building simulation model. Other end-users (water heating, appliances, lighting, and misc. equipment) are based on engineering models developed by LBNL.

Mills, Evan↗
DOE OSTI2014

System Advisor Model, SAM 2014.1.14: General Description

This document describes the capabilities of the U.S. Department of Energy and National Renewable Energy Laboratory's System Advisor Model (SAM), Version 2013.9.20, released on September 9, 2013. SAM is a computer model that calculates performance and financial metrics of renewable energy systems. Project developers, policy makers, equipment manufacturers, and researchers use graphs and tables of SAM results in the process of evaluating financial, technology, and incentive options for renewable energy projects. SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating, wind, geothermal, biomass, and conventional power systems. The financial model can represent financial structures for projects that either buy and sell electricity at retail rates (residential and commercial) or sell electricity at a price determined in a power purchase agreement (utility). SAM's advanced simulation options facilitate parametric and sensitivity analyses, and statistical analysis capabilities are available for Monte Carlo simulation and weather variability (P50/P90) studies. SAM can also read input variables from Microsoft Excel worksheets. For software developers, the SAM software development kit (SDK) makes it possible to use SAM simulation modules in their applications written in C/C++, C#, Java, Python, and MATLAB. NREL provides both SAM and the SDK as free downloads at http://sam.nrel.gov. Technical support and more information about the software are available on the website.

14 SOLAR ENERGY↗
DOE OSTI2018

A Radiation Weather Station: Development of a Continuous Monitoring System for the Collection, Analysis, and Display of Environmental Radiation Data

Due to heightened fear surrounding the possibility of future terrorism involving nuclear weapons and radiological dispersive devices, compounded by nonroutine nuclear power plant releases such as from emergencies or accidents, interest in contamination levels of environmental radiation has spiked. This project sought to develop a continuously operational radiation-monitoring system, with graphically visualized data easily accessible to the public. Because this continuing project is housed at a university facility, it bears no connection to perceived political or commercial interests, generally increasing the credibility of the endeavor. Outdoor weather and radiation parameters were gathered by sensors installed on the rooftop of a two-story building. A display and cloud service website was used to project the live data in an understandable format. A correlation was observed between weather and visibly heightened levels of gamma radiation. Furthermore, the goal of this paper is to share and highlight the overall hardware selection and the unique software challenges encountered when developing a robust collection and analysis system, along with the challenges of displaying meteorological and radiological data in a clear and concise fashion.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

U.S. Department of Energy Solar Decathlon Visitors Guide 2015

The U.S. Department of Energy 2015 Visitors Guide is a free, hard-copy publication distributed free to those attending the Solar Decathlon event. The publications' objectives are to serve as the primary information resource for those in attendance, and to deliver a compelling message about the Solar Decathlon's success as a proven workforce development program and its role in educating students and the public about clean energy products and design solutions. The U.S. Department of Energy 2015 Visitors Guide SD15 Visitors Guide goals are to guide attendees through the Solar Decathlon village; List and explain the 10 contests; educate attendees about the participating teams and their competition houses; provide access to more information on the Solar Decathlon website through the use of QR codes; and acknowledge the support of all event sponsors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Final Report - Phylogenomic tools and web resources for the Systems Biology Knowledgebase

The major advance during this last reporting period (8/15/12 to present) is our release of data on the PhyloFacts website: phylogenetic trees, multiple sequence alignments and other data for protein families are now available for download from http://phylogenomics.berkeley.edu/data/. This project as a whole aimed to develop high-throughput functional annotation systems that exploit information from protein 3D structure and evolution to provide highly precise inferences of various aspects of gene function, including molecular function, biological process, pathway association, Pfam domains, cellular localization and so on. We accomplished these aims by developing and testing different systems on a database of protein family trees: the PhyloFacts Phylogenomic Encyclopedia (at http://phylogenomics.berkeley.edu/phylofacts/ ).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Decision Theoretic Approach To Optimizing Machine Learning Decisions with Prediction Uncertainty

While the use of machine learning (ML) classifiers is widespread, their output is often not part of any follow-on decision-making process. To illustrate, consider the scenario where we have developed and trained an ML classifier to find malicious URL links. In this scenario, network administrators must decide whether to allow a computer user to visit a particular website, or to instead block access because the site is deemed malicious. It would be very beneficial if decisions such as these could be made automatically using a trained ML classifier. Unfortunately, due to a variety of reasons discussed herein, the output from these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making process; and (3) making optimal decisions for classification under uncertainty with single or multiple objectives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Toward Uncertainty Quantification for Supervised Classification

Our goal is to develop a general theoretical basis for quantifying uncertainty in supervised machine learning models. Current machine learning accuracy-based validation metrics indicate how well a classifier performs on a given data set as a whole. However, these metrics do not tell us a model's efficacy in predicting particular samples. We quantify uncertainty by constructing probability distributions of the predictions made by an ensemble of classifiers. This report details our initial investigations into uncertainty quantification for supervised machine learning. We apply an uncertainty analysis to the problem of malicious website detection. Machine learning models can be trained to find suspicious characteristics in the text of a website's Uniform Resource Locator (URL). However, given the vast numbers of URLs and the ever changing tactics of malicious actors, it will always be possible to find sets of websites which are outliers with respect to a model's hypothesis. Therefore, we seek to understand a model's per-sample reliability when classifying URL data. Acknowledgements This work was funded by the Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PV Validation Hub

The Validation Hub will be a clearinghouse for the transfer of novel algorithms and software from the PV research community to industry. Potential algorithms tested in the Hub could include the estimation of various PV loss factors and the detection of various operational issues. The primary function of the Hub will be for developers to submit executable code which will run on hosted data sets. Developers will receive private reports on the accuracy and performance (e.g., run-time) of the submitted algorithms, and public high level summaries will be hosted. These summaries will indicate the organization who submitted the algorithm (e.g., links to GitHub pages, documentation websites, etc.), high-level accuracy metrics, and standardized performance metrics. These results will be stored in a publicly available database, accessible through the Hub, with the ability for users to sort and filter the results. In short, the Hub will be presented to public users as a collection of interactive leaderboards, organized around specific analysis tasks pertinent to the PV data science community. These tasks include things such as the estimation of various PV loss factors and the detection of various operational issues. We will present progress on the development of this hub, including preliminary results of comparative validation of PV data science algorithms and progress towards building the platform itself.

algorithm↗
DOE OSTI2020

Interactive Mobility Landscape App

This is the companion repository to NREL's Interactive Mobility Data Landscape (SEE: DOE Code ID 61284. NREL SWR-21-09). The landscapeapp is an upstream NPM module that supports building interactive landscape websites such as the CNCF Cloud Native Landscape (source) and the LF Artificial Intelligence Landscape (source). The application is managed by Dan Kohn of CNCF and is under active development by Andrey Kozlov (who did most of the development to date, 9/13/2021) and Jordi Noguera.

Shankari, Kalyanaraman↗
DOE OSTI2018

NSUF 2016 Annual Report

The Nuclear Science User Facilities (NSUF) and its partner facilities represent a prototype laboratory that utilizes a distributed partnership, with each facility bringing exceptional capabilities to the relationship including reactors, beamlines, state-of-the-art instruments, hot cells, and equally important, expertise. These capabilities and people together create a nationwide infrastructure that allows the best ideas to be tested using the most advanced capabilities. Through the NSUF, researchers and their collaborators are building on current knowledge to better understand the complex behavior of materials and fuels in a nuclear reactor. The NSUF’s partnership program in 2016 included eight universities, one research and education consortium, three national laboratories, and one industry partner. The avenues opened through these partnerships facilitate cooperative research across the country, matching people with capabilities and students with mentors. The NSUF in 2016 included Idaho National Laboratory (INL) and the following institutions: • Argonne National Laboratory • Center for Advanced Energy Studies (a research and education consortium between Boise State University, INL, Idaho State University, University of Idaho, and University of Wyoming) • Illinois Institute of Technology • Massachusetts Institute of Technology • North Carolina State University • Oak Ridge National Laboratory • Pacific Northwest National Laboratory • Purdue University • University of California, Berkeley • University of Michigan • University of Nevada, Las Vegas • University of Wisconsin • Westinghouse Materials Center of Excellence. This report contains details on new capabilities brought into the NSUF in 2016, the evidence of the increasing research facilitated by the NSUF, and new tools and activities to propel nuclear research and development forward to meet energy reliability and security needs. Learn about the NSUF Nuclear Fuels and Materials Library and the Nuclear Energy Infrastructure Database, and how the robust NSUF website continues to evolve to support new tools. Read about how the NSUF supports Department of Energy (DOE) missions through efforts like the 2016 NSUF Ion Beam Options Workshop, which led to a prioritiza-tion of ion beam facilities according to their ability to support Department of Energy Office of Nuclear Energy (DOE-NE) missions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

National Wind Technology Center-Characterization of Atmospheric Conditions

Data provided by the instrumentation located at the National Wind Technology Center (NWTC) is compiled below for the purposes of characterizing the atmospheric operating conditions at the National Renewable Energy Laboratories (NREL) wind site. Conditions are presented conditioned on month of the year to show seasonal variation, and based on characteristic quantities seen in the atmospheric flow including, bulk wind speed, direction, and atmospheric stability. All data reported and analyzed in the following document were collected from instrumentation mounted on the 135 m-tall masts erected at the NWTC near Boulder, Colorado. Each of the two met-masts (named M4 and M5) were sited specifically to measure the inflow wind to several of the utility-scale turbines at the NWTC. The construction, operation, and maintenance of the masts has been supported since their commissioning in 2011 by the Department of Energy, Office of Energy Efficiency and Renewable Energy, Wind and Water Power Program. Data from the masts are publicly available for download or basic visualization, hosted on NREL's website (https://nwtc.nrel.gov/135mData). Data from the masts have been used in the past for a wide range of projects including wind turbine power performance measurements, high fidelity model development and validation, and atmospheric science research. An interactive visualization platform has been developed on NREL's Open Energy Information platform and can be used to reproduce many of the graphics included in the current work. This report describes in detail the atmospheric conditions recorded by M5, the inflow mast for the DOE-owned GE 1.5 MW wind turbine. The report focuses on data recorded from August 2012 to May 2018 and includes: an introduction to the wind climate at the NWTC; a physical description of the met masts; a review of installed instrumentation; and data collection, processing, and quality control methods. In addition to a concise description of the instrumentation from the met masts and the data channels they provide, this report characterizes the behavior of the atmospheric resource first in a cumulative sense, discussing mainly the distributions wind speeds, flow directions and thermodynamic properties at several heights along the towers. Periodicity in atmospheric conditions is then detailed through analysis of diurnal and annual cycles of the data streams. Periods of both low turbulence and extreme atmospheric events that are of interest in terms of wind turbine operation and wind energy research are detailed in the final chapter of this report. Low turbulence events are somewhat rare at the NWTC and are of interest from the perspective of validating the operation of new wind turbine and blade designs. Similarly, the NTWC is known for a relatively higher rate of occurrence of extreme weather events.

17 WIND ENERGY↗
DOE OSTI2023

U.S. Hydropower Market Report 2023

This is the fourth complete edition of the U.S. Hydropower Market Report (the previous editions, published in 2015, 2018, and 2021, are available here). In intervening years between publishing the full report, updated data are also summarized and released and can be found at the Oak Ridge National Laboratory HydroSource website. This report combines data from public and commercial sources and research findings from other U.S. Department of Energy (DOE) R&D projects to provide a comprehensive picture of developments in the U.S. hydropower and pumped storage hydropower (PSH) fleet and industry trends. Prior to the first Hydropower Market Report being published, there was a noted lack of publicly available and easily accessible information about hydropower in the United States and other important trends affecting this important sector of the energy industry. New and valuable types of information are constantly being developed as part of DOE research activities and, in a rapidly evolving energy industry, it is important that these data be made available in a predictable and consistent manner for use by all different types of stakeholders and decision-makers.

13 HYDRO ENERGY↗
DOE OSTI2015

BSV Gateway Description

Los Alamos National Laboratory (LANL) is part of that worldwide team and has been developing state of the art information systems to aid the global health community. For instance, in 2014 LANL announced the release of the LANL Biosurveillance (BSV) Gateway. The site provides information about ongoing biosurveillance research projects and capabilities at LANL, and provides access to a host of LANL-developed biosurveillance tools and resources. We are seeking feedback on this gateway and would like to ask interested folk to offer comments and suggestions after they have perused the BSV website.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Responder Technology Alert Monthly (December 2014)

As part of technology foraging for the Responder Technology Alliance, established by the Department of Homeland Science and Technologies First Responders Group, this report summarizes technologies that are relevant in the area of “wearables,” with the potential for use by first responders. The content was collected over the previous month(s) and reproduced from a general Internet search using the term wearables. Additional information is available at the websites provided. This report is not meant to be an exhaustive list nor an endorsement of any technology described herein. Rather, it is meant to provide useful information about current developments in the areas wearable technology.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Responder Technology Alert Monthly (January 2015)

As part of technology foraging for the Responder Technology Alliance, established by the Department of Homeland Science and Technologies First Responders Group, this report summarizes technologies that are relevant in the area of “wearables,” with the potential for use by first responders. The content was collected over the previous month(s) and reproduced from a general Internet search using the term wearables. Additional information is available at the websites provided. This report is not meant to be an exhaustive list nor an endorsement of any technology described herein. Rather, it is meant to provide useful information about current developments in the areas wearable technology.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Responder Technology Alert Monthly (Oct-Nov 2014)

As part of technology foraging for the Responder Technology Alliance, established by the Department of Homeland Science and Technologies First Responders Group, this report summarizes technologies that are relevant in the area of “wearables,” with the potential for use by first responders. The content was collected over the previous month(s) and reproduced from a general Internet search using the term wearables. Additional information is available at the websites provided. This report is not meant to be an exhaustive list nor an endorsement of any technology described herein. Rather, it is meant to provide useful information about current developments in the areas wearable technology. The content is organized by technology function including: Sensors – Devices that detect physiological, particle, and chemical activity; Displays – Heads-up and body-worn visual displays; Power – Wearable power systems including chargers, batteries, self-powering or harvesting technologies, and power supplies; Integrated Communications – Voice and data communications systems utilizing Bluetooth, wireless, hands-free, ergonomically optimized systems, noise-filtering digital speakers or microphones, etc.; Exoskeletons – Whole or partial body suit that enhances mobility and physical performance; Wearable Computers – Body-worn data processing devices; General – Miscellaneous technologies as well as emerging trends or recent advances in the field of wearables.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Responder Technology Alert (February 2015)

As part of technology foraging for the Responder Technology Alliance, established by the Department of Homeland Science and Technologies First Responders Group, this report summarizes technologies that are relevant in the area of “wearables,” with the potential for use by first responders. The content was collected over the previous month(s) and reproduced from a general Internet search using the term wearables. Additional information is available at the websites provided. This report is not meant to be an exhaustive list nor an endorsement of any technology described herein. Rather, it is meant to provide useful information about current developments in the areas wearable technology.

99 GENERAL AND MISCELLANEOUS↗