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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 469 records · Page 26
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DOE OSTI2025

Standardized nomenclature for photovoltaic connectors

Photovoltaic (PV) systems rely on discrete connectors for the efficient and safe flow of power from module to module and from strings to combiner boxes and inverters. Despite their functional importance, no common nomenclature for PV connectors currently exists, resulting in confusion and miscommunication. Misunderstood terms like "MC4 compatible", "cross-mating", "intermating", "female", and "male" can lead to installation and maintenance errors and compromise system reliability. We believe a standardized terminology will reduce confusion, help support installation best practices, aid in maintenance and repair, inform next-generation designs, and provide a technical basis for improved codes and standards. To that end, we are proposing a standardized glossary for 4 mm PV connectors (the most common type of connector used in PV applications) based on, and validated by, a Sandia National Laboratories' investigation that included the following sources: 1) a comprehensive review of official documents from 20 connector manufacturers, including schematics, datasheets, installation manuals, and catalogs, as well as relevant patents; 2) two rounds of surveys distributed to stakeholders, including connector manufacturers, engineers, asset owners, test labs, and researchers; and 3) visual examination of 25 different models of 4 mm single-pole DC PV connectors to document variations in design and functionality. This work provides a foundation for establishing a clear and consistent terminology for PV connectors that will in turn enable progress toward greater reliability and collaboration across the industry.

14 SOLAR ENERGY↗
DOE OSTI2024

Testing of a 40-kWth Counterflow Particle-Supercritical Carbon Dioxide Narrow-Channel, Fluidized Bed Heat Exchanger

Particle-based primary heat exchangers (HXs) must deliver sCO2 fluid temperatures above 700°C to couple particle-based concentrating solar receivers and thermal energy storage (TES) sub-systems with efficient sCO2 power cycles. Particle-sCO2 HX designs have struggled to meet DOE cost targets (≤ $150/kWth) due to the amount of expensive nickel alloys necessary for manufacturing full-scale, particle-sCO2 HXs. Our team has demonstrated that mild bubbling fluidization of falling particles in a counterflow narrow-channel fluidized bed can reduce required HX surface area and thus, costs by increasing particle-wall heat transfer coefficients hT,w > 800 W m-2 K-1. This paper reports on the fabrication and testing of a stainless steel, particle-sCO2 HX with 12 fluidized-bed channels approximately 10.5 mm deep spaced between diffusion-bonded, micro-channel sCO2 plates. The HX with a core length of ≈0.56 m is fed with CARBOBEAD HSP particles through a short, fluidized freeboard zone just above the core. Testing to date in the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories has shown that parallel bed fluidization maintains uniform particle inventory across the instrumented channels. Heat transfer thermal duty between the particle and sCO2 flows exceeds 30 kWth with sCO2 inlet temperatures of 200ºC and particle inlet temperatures up to 440ºC and mass flow rates of 0.2 kg s-1 fluidized by counterflowing gas flow rates of 0.005 kg s-1. Tests at higher particle and sCO2 inlet temperatures (600ºC and 400ºC respectively) are targeted to achieve > 40 kWth with model-predicted overall heat transfer coefficients U > 400 W m-2 K-1.

14 SOLAR ENERGY↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Analysis of Dust Samples Collected from an Inland ISFSI Site (Site A)

In September of 2020, dust samples were collected from the surface of spent nuclear fuel (SNF) dry storage canisters during an inspection at an inland Independent Spent Fuel Storage Installation. The purpose of the sampling was to assess the composition and abundance of the soluble salts present on the canister surface, information which provides a metric for potential corrosion risks. The samples were delivered to Sandia National laboratories for analysis. At Sandia, the soluble salts were leached from the dust and quantified by ion chromatography. In addition, subsamples of the dust were taken for scanning electron microscope analysis to determine the texture and mineralogy of the dust and salts. The results of those analyses are presented in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Analysis of Dust Samples Collected from an Inland ISFSI Site (''Site B'').

In October of 2020, dust samples were collected from the surface of spent nuclear fuel (SNF) dry storage canisters during an inspection at an inland Independent Spent Fuel Storage Installation, the second inland site at which surface deposits have been sampled. The purpose of the sampling was to assess the composition and abundance of the soluble salts present on the canister surface, information which provides a metric for potential corrosion risks. The samples were delivered to Sandia National laboratories for analysis. At Sandia, the soluble salts were leached from the dust and quantified by ion chromatography. In addition, subsamples of the dust were taken for scanning electron microscopy to determine the texture and mineralogy of the dust and salts. The results of those analyses are presented in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Analysis of Dust Samples Collected from a Near-Marine East Coast ISFSI Site ("Site C")

In June of 2022, dust samples were collected from the surface of an in-service spent nuclear fuel dry storage canister during an inspection at an Independent Spent Fuel Storage Installation. The site is anonymous but is a near-marine or brackish water east coast location referred to here as "Site C". The purpose of the sampling was to assess the composition and abundance of the soluble salts present on the canister surface, information that provides a metric for potential corrosion risks. Following collection, the samples were delivered to Sandia National Laboratories for analysis. At Sandia, the soluble salts were leached from the dust and quantified by ion chromatography. In addition, subsamples of the dust were taken for scanning electron microscopy to determine the particle sizes, morphology, and mineralogy of the dust and salts. The results of those analyses are presented in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

EV SALaD 2023 Demonstration: Best Practices and Mitigations for Protecting EVSE Infrastructure

The Electric Vehicle Secure Architecture Laboratory Demonstration (EV SALaD) program is a demonstration of cybersecurity best practices for high-power electric vehicle (EV) charging infrastructure led by Idaho National Laboratory (INL), in collaboration with other DOE National Laboratories participating in the EVs at Scale Consortium.a Sandia National Laboratories (SNL) and Pacific Northwest National Laboratory (PNNL) participated in the first 2-year (FY22-23) demonstration cycle for EV SALaD. This report documents the FY23 demonstration, the second in a series of demonstrations and collaborations in deploying and operating cybersecure EV charging infrastructure. It includes a summary of improvements from the FY22 demonstration, technical analysis of the FY23 demonstration, how the research demonstrates cyber-physical and cybersecurity best practices for high-power EV charging infrastructure, and related impacts to national and energy security. For EV SALaD, the FY22 demonstration focused on the detection, ranking, and prioritization of anomalous events for high-power EV charging. The FY23 demonstration additionally included the demonstration of cybersecurity best practices, which included protection and mitigation solutions to prevent, respond, and recover from anomalous events. During the demonstrations, the multi-lab EV SALaD team conducted a Test Effect Payload (TEP)b evaluation on extreme fast charger (XFC) hardware equipped with Cerberus, a detection and response solution, to demonstrate anomaly detection and mitigation cybersecurity best practices against cyber-enabled events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Design Guidelines for Deployable Wind Turbines for Defense and Disaster Response Missions

Access to on-site electrical energy is critical to ensuring a successful military or humanitarian response to conflicts and disasters. These missions typically rely on access to liquid fuel that could be vulnerable to disruption or attack during transport. Generating power on location with wind technology can reduce this risk and enhance mission reach by diversifying energy sources. Common characteristics of these missions are short planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. These design drivers depart from features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This poster serves as a summary of project publications which presents the best currently available design guidance for deployable wind turbines to facilitate the effective development and acquisition of technology solutions to support mission success. This Defense and Disaster Deployable Turbine Project (D3T) is a multi-laboratory effort led by Sandia National Laboratories and funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office.

deployable wind↗
DOE OSTI2025

Material Control & Accounting Modeling Developments for a Generic TRISO Fuel Fabrication Facility

The Material Protection, Accounting, and Control Technologies (MPACT) program utilizes modeling and simulation to assess Material Control and Accountability (MC&A) concerns for a variety of nuclear facilities. The Sandia National Laboratories (SNL)-developed Fissile Facility Flow Modeler (F3M) and the Material Accountancy Performance Indicator Toolkit (MAPIT) have historically provided MPACT with the capability to analyze MC&A approaches for nuclear facilities to determine that these facilities meet regulatory requirements. In FY25, improvements on the application of the F3M and MAPIT tools to simulate a generic TRi-structural ISOtropic (TRISO) fuel fabrication facility were successfully completed. The generic TRISO fuel fabrication F3M model captures the entire TRISO fuel fabrication process and is adaptable to any final TRISO fuel form, including spherical pebbles and cylindrical compacts loaded into graphite prismatic blocks. Comprehensive F3M/MAPIT functionality for the generic TRISO fuel fabrication model has been demonstrated. This modeling framework can be applied to support the U.S. Department of Energy and domestic nuclear industry stakeholders in developing MC&A approaches for advanced fuel fabrication facilities via statistical tests that demonstrate compliance to regulatory requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

2026 Annual Molten Salt Reactor Campaign Review

This report documents the 2026 Annual Molten Salt Reactor (MSR) Campaign Review held in Albuquerque, New Mexico, from April 21–24, 2026. The review was organized by Dr. Patricia Paviet, National Technical Director of the Advanced Reactor Technology (ART) MSR program for the U.S. Department of Energy Office of Nuclear Energy (DOE-NE), and included participation from principal investigators, federal managers, developers, regulators, and members of the broader MSR community. The 2026 review expanded beyond the ART-MSR campaign to include related DOE-NE programs supporting molten salt reactor advancement, including the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the Advanced Materials and Manufacturing Technologies (AMMT) program, Advanced Reactor Safeguards and Security (ARSS), Material Protection, Accounting and Control Technologies (MPACT), and relevant advanced fuels activities. Three days were dedicated to technical presentations and panel discussions covering thermal properties, off-gas management, modeling and simulation, safety and licensing, safeguards and security, materials and corrosion, and irradiation activities. A fourth day was dedicated to technical tours of Sandia National Laboratories and Kairos Power facilities. Attendance was strong and comparable to the prior annual review, with approximately 75 in-person participants per day, 75–80 virtual attendees per day, and approximately 30 participants in the tour day. The review fostered significant technical exchange across national laboratories, universities, industry, regulators, and international participants. This report summarizes the review structure, technical themes, participation, tours, feedback, and conclusions relevant to future planning for the MSR campaign.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Storm Water Pollution Prevention Plan (SWPPP) for the Limited Area, Multi-Purpose (LAMP) High Bay Laboratory

Kier + Wright, as Qualified SWPPP Developer (QSD), puts forth this Storm Water Pollution Prevention Plan (SWPPP) for the Limited Area, Multi-Purpose (LAMP) High Bay Laboratory facility (Project) located at Sandia National Laboratories, 7011 East Avenue, CA. The property is owned by the U.S. Department of Energy, and managed and operated by National Technology & Engineering Solutions of Sandia, LLC. The project proposes converting an asphalt parking lot into a new high bay machine shop building and a low bay office building. Per the California State Water Resources Control Board’s (California State Water Board) Construction General Permit (CGP), a SWPPP is required when 1 acre or more of land is disturbed. The project site area of 1.6 acres exceeds the minimum acreage threshold of 1 acre and therefore requires SWPPP implementation. QSD has determined the sediment risk for this project, based on soil type at the site and starting and ending dates of construction, to be low (Section 3.4.1 and Appendix B). Receiving water for this project is the Arroyo Seco. QSD has determined the Arroyo Seco to be a high-risk receiving water because it has the three beneficial uses of “spawn”, “cold”, and “migratory” (Sections 3.3 and 3.4.2 and Appendix B). QSD has determined the overall risk level for the site to be Risk Level 2, based on a combination of low sediment risk and high receiving water risk (Appendix B). As such, QSD has delineated a variety of Best Management Practices (BMPs) to be employed during project construction to reduce or eliminate pollutants in stormwater runoff or any other discharges from the Project site. In addition to site-specific BMPs, this SWPPP report provides instruction for on site monitoring. Electronic copies of required documentation such as inspection reports, REAPs, annual report documentation, etc. shall be submitted to NTESS Sandia Delegated Representative via Newforma.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impala

SAND2025-07450O Impala is a software tool that performs Bayesian model calibration for emulator-based models. The program uses R code to perform the calculations. 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.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

ENnUI : Exemplar Navigator Using Inertial Sensors

SAND2025-04794O ENnUI: Exemplar Navigator Using Inertial Sensors helps users understand and compare different navigation algorithms that rely on inertial sensors. This reference library offers a collection of reference mechanization equations and methods for estimating the position and movement of vehicles over time. Rather than aiming for the highest precision or performance, ENnUI focuses on creating a user-friendly framework that allows users to benchmark their own algorithms against a standard set of tools. It is useful for post-processing data and real-time navigation solutions. 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.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗
DOE OSTI2025

VorLap

SAND2025-10210O VorLap is a vortex-induced vibration overlap prediction tool for static structures, such as parked wind turbines and marine turbines encountering fluid flow (water or air movement around them) that may induce vortex-induced vibration. This tool uses precomputed frequency domain data for specific cross-sectional shapes or a generalized shedding model, along with geometric data and structural natural frequency data, to identify areas and conditions where vortex-induced vibration may occur. 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.

Moore, Kevin [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2025

Uniform Field Electrode Generator

SAND2025-03788O Uniform Field Electrode Generator is a user-friendly tool designed to create uniform electric field electrodes, essential for high voltage applications. By generating electrodes with precise dimensions, it ensures a consistent electric field across their surfaces. This software is particularly beneficial for researchers and engineers working on projects like closing switches and studying power flow in advanced systems, such as the Z machine. Its unique approach simplifies the design process, making it accessible for users without technical expertise, while enhancing the efficiency and effectiveness of their electrical applications. 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.

Mason, Tyler [Sandia National Lab. (SNL-CA), Liver↗
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 OSTI2024

Defect diffusion graph neural networks (d2gnn)

SAND2025-01004O Defect Diffusion Graph Neural Networks (d2gnn) is a software tool that assists in the discovery of new materials for high-temperature, clean-energy applications. It uses advanced graph neural networks to model the relationship between material structures and their defect properties. The application helps predict how materials will behave under different conditions and accelerates the development of innovative materials. 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.

Witman, Matthew [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2024

PyTrac

SAND2025-00635O PyTrac is a software tool that analyzes and visualizes PTRAC event files generated by MCNP 6.3. It converts these files into a graph network that makes it easier to interpret individual histories. The software includes command line tools for viewing the graph data structure in both 2D and 3D formats. PyTrac also integrates with MCNP to run simulations and manage data files. This provides a streamlined approach to analyzing and understanding the complex data generated by MCNP simulations. 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.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗