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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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DOE OSTI2024

Curating Carbon Storage Data for Reuse: Enabling Research and Modeling from Earth’s Surface to Subsurface

The volume of public geologic carbon storage (GCS) data resources has continued to increase in recent years as the result of an increase in funding from government, industry, and academia towards national, basin, regional and field scale studies to ensure carbon capture and storage becomes a commercially viable operation. Despite the increasing volume of data, GCS data applied towards analyses such as geologic, cost, and risk modeling continues to be multi-sourced and often disparate in nature, published across government agencies, websites, data repositories and buried in derivative reports and documents. Much of the time preparing for an analysis and derivative product development is spent collecting, aggregating, transforming and preparing input data. There have been significant efforts within the DOE National Energy Technology Laboratory’s Carbon Storage Program to optimize multi-source, multi-scale subsurface geologic data curation and aggregation to support data discovery, interoperability, and reuse. Methods include the use of artificial intelligence, machine learning, and data science techniques. This talk will discuss the workflows, best practices, and processes developed to support the aggregation and curation of data through the whole system – surface to subsurface data - that support multi-scale, multi-purpose analysis for carbon storage research.

Morkner, Paige↗
DOE OSTI2019

Certification and Rating of Attachments for Fenestration Technologies

In the U.S., windows still represent a significant energy savings opportunity in residential and commercial buildings, and for decades the United States Department of Energy (DOE) has funded fenestration software tools, analysis, studies, and the National Fenestration Rating Council (NFRC). Despite significant developments in window technology and recognition of efficient products, studies conducted by the U.S. Energy Information Administration and Pacific Northwest National Laboratory (PNNL) found that approximately 64% of U.S. homes currently have inefficient windows. Window attachments (interior and exterior blinds, shades, storm windows, awnings, shutters, etc.) can offer a more cost-effective option to improve the energy performance of residential and commercial windows. In previous years, the window attachment industry attempted to work with the NFRC to generate a window attachment rating program, but was unable to access funding or technical resources necessary to address window attachments, which added many levels of complexity on top of NFRC’s scope of windows and glazing. Starting in 2014, the DOE Office of Energy Efficiency and Renewable Energy (EERE) funded the Window Coverings Manufacturing Association (WCMA) to develop the Attachments Energy Rating Council (AERC) along with policies and procedures to deliver an unbiased, credible, comparable, relevant, and cost-effective window attachment energy rating program for the residential and commercial sectors over a four-year period. This project was created because there were no consistent and credible performance rating mechanisms for assessing the energy performance of window attachments. Without a standardized approach, consumers found it difficult to make informed and comparative decisions about the energy performance of window attachments, and utilities faced challenges offering incentives for energy-efficient attachments. The goal of this project was two-fold: to develop procedures to rate and certify the energy performance of window attachments and to create an educational, consumer-facing Certified Products Website (CPW) to help consumers and other stakeholders make more informed window attachments purchasing decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

SNF Data Visualization on the Centralized Used Fuel Resource for Information Exchange (CURIE) Website

To better inform stakeholders regarding the nature of spent nuclear fuel (SNF) at utility sites, an online data visualization system has been created for the U.S. Department of Energy's Office of Nuclear Energy (DOENE). The data visualization system is part of the Centralized Used Fuel Resource for Information Exchange (CURIE) website, hosted by the Oak Ridge National Laboratory (ORNL) for DOE-NE. The visualization employs a database that is part of an analysis tool known as the Used Nuclear Fuel Storage, Transportation and Disposal Analysis Resource and Data System (UNF-ST[and]DARDS). Because many of the previous document management and sharing tools related to SNF have been terminated (or are inaccessible), a new tool, the CURIE, was developed for DOE-NE and launched on March 1, 2013 at http://curie.ornl.gov. In addition to supporting DOE SNF programs, CURIE is a national resource that is accessible to industry, vendor, federal, and laboratory partners, as well as other stakeholders. CURIE's architecture was initially based on previous work that supported DOE's Bioenergy Technologies Office on the Bioenergy Knowledge Discovery Framework (KDF). The site uses an open-source search engine, Apache Solr, which provides intuitive search results based on a faceted search method similar to that used by Amazon.com{sup R}. This algorithm helps to ensure that finding documents and data is straightforward. SNF data visualization capability was added to the CURIE website which provides a capability to post and access documents and data related to the management of SNF. CURIE currently maintains over 4,100 documents and the Siting Experience Database (SED), which was developed in response to a near-term recommendation by the Blue Ribbon Commission on America's Nuclear Future (BRC). The SED is a database of the experience that has been gained and relevant documentation that has been produced in efforts to site nuclear waste facilities, both in the US and abroad. Most of these documents are publicly accessible. In addition, CURIE maintains a current calendar of related events, an SNF image gallery, featured documents, and external links to related databases and websites. CURIE also maintains private groups (or communities) to facilitate collaborative development through sharing and editing documents that are not publicly available. There are more than 250 registered users in 23 different communities within CURIE.ORNL has also been developing UNF-ST[and]DARDS to provide a controlled source of technical data for various waste management system analysis/evaluation tools, access to key technical data, and analysis capabilities for characterizing the SNF inventory. UNF-ST[and]DARDS also provides enhanced capabilities to assess safety, risk, and uncertainty throughout the integrated waste management system. This paper describes the UNF-ST[and]DARDS data available for visualization and the associated visualization techniques used by CURIE to display this data.

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

Excluding Benchmark Statistical Outliers in Nuclear Criticality Safety Validation: A Comparison Study of Upper Subcritical Limits for Plutonium Systems using Whisper-1.1

Neutron transport methods used to establish subcriticality require validation by comparison to critical experiments considered to be benchmarks. Whisper is a sensitivity/uncertainty analysis tool developed to assist with the task of validation in nuclear criticality safety. Details on the Whisper methodology can be found in References 1-3 on the MCNP® reference collection website at https://mcnp.lanl.gov. During the process of validation there can be cases where a benchmark experiment may be found to be a statistical outlier, in which the calculated k-effective value and the experiment k-effective value differ by an amount atypical for similar experiments. A methodology optionally employed by Whisper is the exclusion of statistical outliers based upon the iterative diagonal chi-squared statistical rejection technique. Alternatively, there is an option to include all benchmarks in the Whisper library collection, even those benchmarks found to be statistical outliers, when computing the bias, bias uncertainty and margin of subcriticality (MOS) leading to establishment of the baseline upper subcritical limit (USL). A comparison study has been done to compute USLs with and without statistical outliers in the Whisper benchmark collection to determine what effect rejection of statistical outliers has on the recommended USL. The results show little overall difference in the recommended baseline USLs developed by Whisper when excluding statistical outliers. Additionally, there does not appear to be a clear trend in predicting whether the baseline USL will be higher or lower when rejecting statistical outliers from the benchmark critical experiment collection used for validation.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Technical Resilience Navigator

The Technical Resilience Navigator (TRN) is a systematic approach to identifying vulnerabilities with energy and water systems, and prioritizing solutions that reduce risk. The ultimate outcome of the TRN is a set of actionable resilience solutions that address the site's most important gaps in resilience and enhance the ability to maintain mission continuity. The TRN is designed to step users through this planning process, providing a framework to: assign roles and responsibilities; collect and document information and data; document key inputs and outputs for each of the TRN modules (Site Level Planning, Baseline Development, Risk Assessment, Solution Development, and Solution Prioritization); document prioritized list of resilience solutions; and track progress through the entire process. Currently "software as a service" at the listed website.

Rotondo, Julia↗
DOE OSTI2022

NEAMS Advanced Reactor Model Contributions to the NRIC Virtual Test Bed

The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. Several NEAMS advanced reactor models (including input files, documentation, and discussion of results) have recently been contributed to the NRIC VTB. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. This report summarizes and provides links to these new models, which include modeling phenomena important to liquid metal cooled fast reactors, molten salt reactors, high temperature gas-cooled reactors, and microreactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

NEAMS Model Contributions in 2023 to the National Reactor Innovation Center Virtual Test Bed for Use by Industry and Other Stakeholders

The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. This work documents NEAMS efforts to support industry adoption of advanced modeling tools through contribution of 10 NEAMS models to the NRIC Virtual Test Bed including models for the High Temperature Test Facility (HTTF), TRISO fuel failure in a microreactor, and multiphysics models of a molten chloride fast reactor, among others. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. In particular, the HTTF model is being used for code validation and benchmarking activities. The microreactor and molten chloride fast reactor models are representative of analysis that may be useful for current candidates of DOME and LOTUS, NRIC’s physical testbeds. This report summarizes and provides links to these new models, among others.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Bioenergy Research Centers Data Sharing Portal

The bioenergy.org website is the end product of the Data Sharing Shared Research objective for the Bioenergy Centers. The objective is to Enhance BRC data legacy through the development and use of shared software tools to make previously published datasets more findable and accessible through the Inter-BRC Data Products Portal, and to explore the use of generative AI to assist in the exploration of published datasets. The software repository is released per the license information below.

Thrower, Nicholas↗
DOE OSTI2018

Aimsgb: An algorithm and open-source python library to generate periodic grain boundary structures

An algorithm implemented in an open-source python library was developed for building periodic grain boundary models in a universal fashion. The software framework, aimsgb, aims to generate tilt and twist grain boundaries from an input cubic or non-cubic crystal structure for ab-initio and classical atomistic simulation. It can output a coincidence site lattice (CSL) grain boundary for a cubic input structure and a non-CSL grain boundary for a non-cubic input structure. This framework has two useful features: (i) it can calculate all the CSL matrices for generating CSL from a given Sigma (Σ) value and rotation axis, allowing the users to build the specific CSL and grain boundary models; (ii) it provides a convenient command line tool to enable high-throughput generation of tilt and twist grain boundaries by assigning an input crystal structure, Σ value, rotation axis, and grain boundary plane. Here, the developed algorithm in the open-source python library is expected to facilitate studies of grain boundary in materials science. The software framework is available on the website: aimsgb.org.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of learning objectives to support undergraduate virology curriculum guidelines

It has become increasingly important for microbiology educators to help students learn critical concepts of the discipline. This is particularly true in virology, where current challenges include increasing rates of vaccine hesitancy, misinformation about the COVID-19 pandemic, and controversy surrounding research on pathogens with pandemic potential. Having students learn virology can attract more people to the field and increase the number of people who can engage in meaningful discourse about issues relating to the discipline. However, the limited number of virologists who teach undergraduates, combined with the fact that many institutions lack stand-alone virology courses, results in virology often being taught as a limited number of lectures within an undergraduate microbiology course (if it is covered at all), which may or may not be taught by an individual trained as a virologist. To provide a framework to teach virology to undergraduate students, a team of virology educators, with support from the American Society for Virology (ASV), developed curriculum guidelines for use in a stand-alone undergraduate virology course or a virology section within another course. These guidelines are available at the ASV website (https://asv.org/curriculum-guidelines/). To assist educators in implementing these guidelines, we created examples of measurable learning objectives. This perspective provides details about the virology curriculum guidelines and learning objectives and accompanies the perspective by Boury et al. in this issue of the Journal of Microbiology & Biology Education about the recent revision of the microbiology curriculum guidelines overseen by the American Society for Microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Subsurface Transport Over Multiple Phases Demonstration Software

The STOMP simulator is a suite of numerical simulators developed by Pacific Northwest National Laboratory for addressing problems involving coupled multifluid hydrologic, thermal, geochemical, and geomechanical processes in the subsurface. The simulator has been applied to problems concerning environmental remediation, environmental stewardship, carbon sequestration, conventional petroleum production, and the production of unconventional hydrocarbon fuels. The simulator is copyrighted by Battelle Memorial Institute, and is available outside of PNNL via use agreements. To promote the open exchange of scientific ideas the simulator is provided as source code. A demonstration version of the simulator has been developed, which will provide potential new users with an executable (not source code) implementation of the software royalty free. Demonstration versions will be offered via the STOMP website for all currently available operational modes of the simulator. The demonstration versions of the simulator will be configured with the direct banded linear system solver and have a limit of 1,000 active grid cells. This will provide potential new users with an opportunity to apply the code to simple problems, including many of the STOMP short course problems, without having to pay a license fee. Users will be required to register on the STOMP website prior to receiving an executable.

Source record↗
DOE OSTI2022

LaserNetUS Collaboration Network—University of Rochester (Final Report)

LaserNetUS Collaborative Network established in 2018 is a network of high-power laser facilities supported by the Department of Energy (DOE) Office of Fusion Energy Sciences (FES) and operating effectively as a user facility. Its mission is to advance and promote intense laser science and applications by providing scientists and students with broad access to unique facilities and enabling technologies, advancing the frontiers of laser-science research, and fostering collaboration among researchers and networks from around the world. Users who submit proposals through an annual call are selected by an external and independent proposal review panel (PRP) not involving personnel from any of the facilities. Besides the Omega Laser Facility at the University of Rochester’s Laboratory for Laser Energetics (UR/LLE), the network during this project period includes high-intensity laser facilities from six other universities and three national laboratories, namely, the Colorado State University (CSU), the University of Michigan (UM), the University of Nebraska at Lincoln (UNL), The Ohio State University (OSU), Université du Québec, the University of Texas at Austin (UT Austin), Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory (SLAC) and Lawrence Livermore National Laboratory (LLNL), respectively. The network facilities span a wide range in laser pulse energy, pulse duration, repetition rate, and experimental diagnostic equipment enabling innovative research in a variety of exciting areas. Details of the LaserNetUS facilities, organization and committees, events, and accomplishments can be found at the network website (https://lasernetus.org/). A very important role that the LaserNetUS fulfills is the training of students and young scientists who will be key for the future development of laser-plasma science and high-power laser technology itself. The network provides these students not only with access to the most advanced instrumentation and laser facilities, but also the opportunities to interact and collaborate with students from other institutions and with a large group of experienced scientists. As the largest university-based laser users’ facility in the world, the Omega Laser Facility at the UR/LLE has served the high-energy-density physics (HEDP) and inertial fusion science community for nearly 40 years. The multi-beam multi-kJ OMEGA EP Laser System brings unique capabilities to the LaserNetUS network. The combination of high intensity and high energy in short- and long-pulse operation together with solid or gas-jet targets and externally applied magnetic fields provides users a wide domain of experimental conditions. This award provides a total of eight shot days on OMEGA EP for LaserNetUS users. During the award period of performance (June 2019–November 2021), seven teams have fully utilized the eight shot days for their unique science experiments on OMEGA EP with a total of 83 target shots. These experiments involve 13 graduate students, two undergraduate students and six postdoctoral researchers. Results have been widely disseminated at international conferences including LaserNetUS annual meeting (~20 presentations including three invited), and in peer-reviewed journal publications (three published with several manuscripts in preparation).

36 MATERIALS SCIENCE↗
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 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 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↗