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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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This poster presents a project focused on designing a storage system for a Modernized Fort Saint Vrain Spent Nuclear Fuel handing tool for use in the Road- Ready Demonstration Project.
This is the poster for the concluding LDRD project, Development of Two-Phase Separation Techniques for Advanced Fuel Cycle Studies, Project number: 23A1070-162FP.
Please see attached intern poster which focuses on various Industrial Hygiene activities as an intern.
The attached intern poster is for a humanoid robot built by the Light Water Reactor Sustainability group. It contains basic information about what the robot is and some basic points about how it was designed and built.
The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.
Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.
Liquid Metal Jetting (LMJ) is a metal additive manufacturing technique that involves jetting molten metal droplets at high frequencies to build solid metal parts. As an alternative to industry standard Lazer Powder Bed Fusion (LPBF) and Direct Metal Writing (DMW) techniques, LMJ poses significant advantages including no powder feed stock, no post sintering process, very high deposition rates, and the capability to print various metals. This summer, I was tasked with improving my old system to process at much higher resolution, Increasing the capture rate, and implementing this diagnostic on various LMJ setups. This system provides a vital longitudinal study to understand individual droplets in the LMJ process.
This project conducts a comparative analysis of DNA LLM classification techniques using Evo2, Grover, and UTRML, focusing on intra-layer feature extraction in Evo2. By extracting features from multiple layers of Evo2 and integrating them into an autoencoder stack with a binary classification head, we evaluate its effectiveness in classifying genomic sequences compared to smaller DNA language models. My findings demonstrate that Evo2 outperforms Grover and UTRML in classification accuracy on a dataset provided by department 08625, CAO2021, while UTRML offers competitive performance with lower computational costs. This study highlights the potential of advanced embedding techniques in enhancing genomic data analysis and informs future research in bioinformatics.
The application of increased pressure during the curing process of B-stage epoxy has been hypothesized to enhance the adhesive properties and bond strength in printed wiring board (PWB) assembly. This study aimed to investigate the correlation between pressure application and the performance of B-stage epoxy.
At mm-wave frequencies, signals need to be supported by specialized, low-loss transmission lines such as waveguides. Substrate-integrated waveguides (SIWs) combine the good electrical properties of rectangular waveguides with the compactness of planar transmission lines. Additionally, SIWs can be fabricated using standard PCB manufacturing techniques and can be easily integrated with other mm-wave components. This study explores SIWs in the 50-75 GHz range, with designs optimized for simple fabrication.
At Sandia National Laboratories, there is a high level of importance placed on identifying and developing solutions to the nation’s current and future security problems. One of these problem would be hardware electronics validation and verification.
Cement production involves the decomposition of limestone (calcium carbonate) at high temperatures (~900°C) to produce CaO, a major constituent in Portland cement (60-70%). Our unique approach will eliminate the high temperature process and introduce a low-temperature electrochemical process to produce Ca(OH) 2 which can be converted into CaO through dehydration process. This program will allow SRNL to become a leading organization in an open and unexplored field that addresses many of the technical challenges.
Abstract not provided.
Fuel debris removal operations at the Fukushima-Daiichi Nuclear Power Station (1F) present significant complexities in many engineering disciplines this presentation will cover this issue among others.
Abstract not provided.
Nuclear data (ND) is vital to predictive simulations such as those completed with MCNP®. ND sensitivities are used to optimize the design of benchmark experiments. Diverse benchmarks (including neutron noise) are required to further our understanding of ND. Measured and simulated data of a 4.5-kg Pu sphere was used to calculate neutron noise parameters and associated ND Sensitivities
Nuclear data (ND) underpins predictive neutron transport simulations like MCNP. The current nuclear data pipeline is time intensive and iterative. PARADIGM aims to reduce uncertainties by restructuring the ND Pipeline.
he project PARADIGM (PARallel Approach of Differential and InteGral Measurements) answers this question by selecting via machine learning (ML) an optimal combination of differential and integral experiments to reduce 239 Pu nuclear data uncertainties from 1-600 keV by 50%..