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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scalable and Energy-Efficient Methods for Interactive Exploration of Scientific Data

The main scientific contributions of this project are the following novel concepts for multidimensional arrays: shape-based similarity join (SIGMOD 2016), incremental view maintenance (SIGMOD 2017), user-defined stencil functions (HPDC 2017), and distributed caching for in-situ processing (SSDBM 2018). Building on our collaboration with the astrophysics group at LBNL, we applied these techniques to the data generated in the Palomar Transient Factory (PTF) astronomical survey. They played a pivotal role in the first-ever observation of a neutron star merger, which produces gravitational waves and turns out to be the origin of heavy elements, including gold. This has lead to a Science magazine article that has received extensive media coverage on ACM TechNews, Slashdot, FiveThirtyEight, and Quanta Magazine, among others. Additionally, two other articles detailing related aspects of the same discovery have been published in the Astrophysical Journal Letters journal. These publications have more than 3,000 citations according to Google Scholar (as of February 2022). This cross-disciplinary collaboration provided very good opportunities to apply database techniques to real-life scientific problems. The fact that they facilitated major discoveries in astrophysics proves the importance of our research. In addition to the work on multidimensional array databases, this project has also developed stochastic gradient descent (SGD) optimization algorithms for training large scale machine learning models, methods for querying in-situ data, and a database query optimizer based on sketch synopses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Small Hydropower Interconnections: Analysis of Interconnection Processes

Small hydropower projects have faced the challenge of navigating the process to interconnect their generation source to electricity distribution and transmission grids. Small hydropower developers have found interconnection procedures to be opaque and ultimately result in unexpected cost surprises and long timelines. Noting these challenges, the U.S. Department of Energy Water Power Technologies Office enlisted Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to investigate the small hydropower interconnection landscape across the United States. After reviewing the status of small hydropower (“Small Hydropower Interconnections: Small Hydropower in the United States”) and the interconnection procedures across the United States (“Small Hydropower Interconnections: State Interconnection Processes”) in the first two white papers of this series, this paper uses recent data from small hydropower interconnection applications to benchmark the efficacy of the process. Using data from interconnection queues hosted by utilities, balancing authorities, independent system operators (ISOs), and regional transmission organizations (RTOs), this paper provides context for the costs, timelines, and types of upgrades required for small hydropower projects. Interconnection applications and study reports for small hydropower projects were analyzed to collect key pieces of information about the interconnection process, timeline, costs, and type of upgrades required for interconnection. Information sourced from the reports was entered into an Interconnection Benchmarking database (IBdb), which may be found in Appendix A.1. Information from this database was used to evaluate the performance and challenges associated with interconnecting small hydropower projects. This white paper presents a description of the sources contained in the interconnection database (Section 2.0), an analysis of the interconnection timeline (Section 3.0), an evaluation the cost of interconnection upgrades (Section 4.0), and a description of the types of infrastructure upgrades (Section 5.0). The final paper in this series (“Small Hydropower Interconnections: Best Practices”) will use the analysis described here to outline best practices for interconnection processes that will help overcome barriers to future small hydropower development.

13 HYDRO ENERGY↗
DOE OSTI2022

Review of Experimental Data for Validating Computer Codes Used in Shielding Calculations for Spent Fuel Storage and Transportation Systems

This report presents a review of available radiochemical assay data and shielding benchmarks applicable to spent nuclear fuel (SNF) shielding calculations. The relevant information reviewed herein includes the Spent Fuel Composition (SFCOMPO) database, the Shielding Integral Benchmark Archive and Database (SINBAD), the International Handbook of Evaluated Criticality Safety Benchmark Experiments, and published measurements of external dose rates of casks loaded with SNF. The relevant experimental data identified in this report may be used to support verification and validation of computer codes used in SNF cask/transport shielding applications, as well as development of calculation uncertainties. It should be noted that a relatively small subset of the identified experimental data (e.g., criticality alarm experiments) is available in a standard format established by the international community participating in experimental isotopic and shielding data evaluations. An effort of the SFCOMPO Technical Review Group (TRG) is underway to publish first isotopic evaluations of individual assay data using a standard data evaluation format. The SINBAD TRG has recently initiated benchmark evaluations and modernization of the database. Therefore, more relevant information is expected in the future that will enable users to select quality experimental data in depletion code and shielding code validations for SNF applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Nuclear Global Talent Development Program (Final Report)

Lawrence Livermore National Laboratory (LLNL) have developed sorption database named as L-SCIE (LLNL Surface Complexation/Ion Exchange). This framework (L-SCIE) includes not only a community database of sorption data but also a fitting workflow to produce optimized surface complexation reaction constants. Through this fellowship program we have updated the L-SCIE database for selenium (Se) sorption and potentiometric titration data of iron oxides. In addition, the relevant surface complexation models (SCMs) have been updated and details in model construction are given in the following section.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A Snapshot of EV-Specific Rate Designs Among U.S. Investor-Owned Electric Utilities

EV-specific electric utility rates have a profound effect on the underlying economics motivating EV adoption. State utility regulators and electric utilities play a critical role in approving and designing rates, respectively. However, the nascence of the EV industry has resulted in regulators’ and utilities’ having very limited experience with EV-specific rate designs. This suggests there could be substantial benefit from efforts intended to provide a better understanding of how EV-specific rates are designed presently in the United States. To meet that need, Berkeley Lab researchers, with support from E9 Insight, developed a database of piloted, proposed, and offered rates among U.S. investor-owned utilities (IOUs) between 2012 and 2022. The database is comprised of 217 electric utility retail rates from IOUs in 38 states and the District of Columbia that either required proof of EV ownership or were otherwise designed for the purposes of reselling energy for use in EV charging (i.e., EV-specific rates). The analysis of the database identifies the EV-specific rate designs currently observed, infers a number of policy-driven objectives, and suggests a number of next steps for future research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Radionuclide Interaction with Hydrothermally Altered Repository Materials (M4SF-23LL010302052)

This progress report (Level 4 Milestone Number M4SF-23LL010302052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline Activity Number SF-23LL01030205. The research is focused on actinide and radionuclide sequestration in hydrothermally altered repository materials. In FY23, a manuscript was in preparation for publication summarizing our analysis of radionuclide sorption and coprecipitation into Fe oxide phases and evaluation of radionuclide partitioning values across a range of radionuclides relevant to performance assessment. We demonstrated our approach in detail using Se sorption and coprecipitation with iron oxide minerals. These data were presented in our FY22 annual report and will not be repeated here. We also initiated experiments to identify radionuclide interaction with hydrothermally altered crystalline repository and backfill materials. Recent research performed at Los Alamos National Laboratory (LANL) and Sandia National Laboratory (SNL) has provided key insights regarding the hydrothermal alteration behavior of bentonite backfill in the presence of repository materials (steel, concrete, etc.). We are now examining how mineral alteration affects retardation behavior of plutonium and a suite of other radionuclides. These experiments also allow us to test the predictive ability of our component additivity approach to surface complexation and ion exchange. Our guiding hypothesis is that a robust surface complexation/ion exchange model and associated database, developed using our L-SCIE approach, can effectively predict changes in radionuclide sorption behavior resulting from the hydrothermal alteration of mineralogy in a repository near field. A short update of results to date is presented below. A manuscript was also in preparation describing a self-consistent model and approach to simulating Se(IV) and Se(VI) sorption to 5 iron oxide phases based on our L-SCIE community database. This will be the first implementation of a multi-mineral and multi-oxidation state sorption model using our new L-SCIE database and workflow. A short summary of these results is presented below.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Summary of Thermodynamic Data Integration (M4SF-23LL010301052)

This progress report (Level 4 Milestone Number M4SF-23LL010301052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number M4SF-23LL010301052. SUPCRTNE is being developed as the primary engine for thermodynamic database development in support of geologic disposal of high-level nuclear waste. As used here, “SUPCRT” refers to both a computer program and its supporting database. Additional letters or numbers refer to specific variants (SUPCRT92, Johnson et al., 1992; SUPCRTBL, Zimmer et al., 2016; and SUPCRTNE). A SUPCRT database contains the data required to calculate the thermodynamic properties of solid, gas, and aqueous species over a wide range of temperature and pressure. Normally SUPCRT is used to create a higher-level data base that directly supports modeling and simulation codes such as EQ3/6, GWB, PHREEQC, and PFLOTRAN.

58 GEOSCIENCES↗
DOE OSTI2023

Modeling Radionuclide Vaporization from Sodium Pools for SFR Mechanistic Source Term Analysis

To assist both the advanced reactor industry and U.S. Nuclear Regulatory Commission (NRC) in the pursuit of reactor design and licensing, the U.S. Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has established a mechanistic source term (MST) research project under the Multiphysics Applications technical area. As part of this effort, an MST mod/sim development pathway was developed in FY21 under the NEAMS program, which outlines the high-level objectives and near-term tasks necessary to achieve the project objectives. One such recommended task was the assessment of a previously developed thermodynamic database to model radionuclide vaporization from liquid sodium pools (specifically in the context of MST analyses), which is the focus of this topic report. As such, a thermodynamic database was created in FactSage for liquid sodium pools containing oxygen and relevant elements which represent radionuclides pertinent to MST analyses. The database was demonstrated in calculating vapor fraction curves for each element in the system at concentrations relevant to SFR MST.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

FY23 Status of Quality Assurance Plan for Out-of-Pile Test Data

The DOE Advanced Reactor Technology program has supported recovery and preservation of legacy metallic fuel data collected as part of the US fast reactor program, recognizing it as essential to development and licensing activities for advanced fast reactors. Databases were established as organized collections of experimental records and data generated from in-pile experiments at EBRII, FFTF, and TREAT as well as related out-of-pile examinations of irradiated fuels. The Out-of-Pile Transient Database (OPTD), includes records of over 150 out-of-pile furnace transient tests on metallic fuels conducted at Argonne’s Alpha-Gamma Hot Cell Facility to evaluate their transient performance and characterize fuel/cladding interaction. The database is accessible to registered users from US universities, laboratories, and nuclear industry. Because the data in OPTD has not been formally qualified, its applicability and ability to support licensing activities is limited. This report outlines progress and plans to quality assure data in OPTD, maximizing its impact for model validation and verification as well as qualification of fuels for safe and effective use in advanced reactor designs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Schema Elements for Granta Annual Report: FY23

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE additive manufacturing (AM) database, so the objective of this project is to create and document such a schema.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum Search Approaches to Sampling-Based Motion Planning

In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree. As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm. We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm. We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice. We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sierra/SD - Its2Sierra - User's Manual - 5.20

The Integrated Tiger Series (ITS) generates a database containing energy deposition data. This data, when stored on an Exodus file, is not typically suitable for analysis within SierraMechanics for finite element analysis. The its2sierra tool maps data from the ITS database to the Sierra database. This document provides information on the usage of its2sierra.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

RECON Label Quality Report

The final quality of any AI/ML system is directly related to the quality of the input data used to train the system. In this case, we are trying to build a reliable image classifier that can correctly identify electrical components in x-ray images. The classification confidence is directly related to the quality of the labels in the training data, which are used in developing the AI/ML classifier. Incorrect or incomplete labels can substantially hinder the performance of the system during the training process, as it tries to compensate for variations that should not exist. Image labels are entered by subject matter experts, and in general can be assumed to be correct. However, this is not a guarantee, so developing ways to measure label quality and help identify or reject bad labels is important, especially as the database continues to grow. Given the current size of the database, a full manual review of each component is not feasible. This report will highlight the current state of the “RECON” x-ray image database and summarize several recent developments to try to help ensure high quality labeling both now and in the future. Questions that we hope to answer with this development include: 1) Are there any components with incorrect labels? 2) Can we suggest labels for components that are marked “Unknown”? 3) What kind of overall confidence do we have in the quality of the existing labels? 4) What systems or procedures can we put in place to maximize label quality?

42 ENGINEERING↗
DOE OSTI2024

Sierra/SD – Its2Sierra – User’s Manual – 5.18

The Integrated Tiger Series (ITS) generates a database containing energy deposition data. This data, when stored on an Exodus file, is not typically suitable for analysis within Sierra Mechanics for finite element analysis. The its2sierra tool maps data from the ITS database to the Sierra database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

IDB Data Loader

The International Database of Reference Gamma-Ray Spectra of Various Nuclear Matter is designed to hold curated gamma spectral data and will be hosted by the International Atomic Energy Agency on its public facing web site. Currently, the database to be hosted is given to the International Atomic Energy Agency by Sandia. This document describes the application used by Sandia to load spectral data into a database.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Sierra/SD – Its2Sierra – User’s Manual – (V.5.16)

The Integrated Tiger Series (ITS) generates a database containing energy deposition data. This data, when stored on an Exodus file, is not typically suitable for analysis within Sierra Mechanics for finite element analysis. The its2sierra tool maps data from the ITS database to the Sierra database. This document provides information on the usage of its2sierra.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗