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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A smart alarm for particle accelerator beamline operations

We present the initial results of a proof-of-concept 'smart alarm' for the Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify and diagnose unusual machine states is needed. Our approach leverages a trained neural network capable of alerting operators (a) when an anomalous condition exists in the beamline and (b) identifying the element setting that is the root cause. The tool is based on an inverse model that maps beamline readings (diagnostic readbacks) to settings (beamline attributes operators can modify). The model takes as input readings from the machine and computes machine settings which are compared to control setpoints. Instances where predictions differ from setpoints by a user-defined threshold are flagged as anomalous. Given data corresponding to 354 anomalous injector configurations, the model can narrow the root cause of an anomalous condition to three potential candidates with 94.6% accuracy. Furthermore, compared to the current method of identifying anomalous conditions which raises an alarm when machine parameters drift outside their normal tolerances, the data-driven model can identify 83% more anomalous conditions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗
DOE OSTI2025

New Results on Communication- and Memory-Aware Load Balancing Model and Algorithms

While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to this problem: a unified, reduced-order model that combines three key components to describe “work” in a distributed system: computation, communication, and memory. Our model enables an optimizer to explore complex tradeoffs in task placement, such as augmented parallelism, at the expense of data replication increasing memory usage. We propose a fully distributed, heuristic-based load balancing optimization algorithm, and demonstrate that it quickly finds close-to-optimal solutions. We formalize the complex optimization problem as a mixed-integer linear program, and compare it to our strategy. Finally, we show that when applied to an electromagnetics code, our approach obtains up to 2.3x speedups for the imbalanced execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Electric Grid Security (EGS) FY24 Annual Report

Sandia’s Electric Grid Security program advances a national vision of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY24 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • The advancement of energy storage technologies, including creation of a national Long Duration Energy Storage Consortium; • Applications of artificial intelligence and machine learning to enhanced grid operations and planning; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Minimizing Auxiliary Heat Use for Cold Climate Operation of Air-Source Heat Pumps

This paper investigates the auxiliary heat use for air-source heat pumps (ASHPs) operating in cold climate conditions. Twelve variable-capacity, central ducted ASHPs installed in single-family homes in cold climate regions (eleven in northwest United States and one in Denver suburb) were monitored for an entire winter season to collect data at cold temperatures. The methodology employed airside and power measurements that were taken every five-seconds, to calculate heat pump's capacity, coefficient of performance (COP), and auxiliary heat energy consumption. This paper provides valuable insights into the practical implications of auxiliary heat utilization in centrally ducted ASHPs and suggests opportunities to mitigate the usage of auxiliary heat, improving the overall system efficiency during cold climate operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

PolyODENet

Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space.

Wu, Qin [Brookhaven National Lab. (BNL), Upton, NY↗
DOE OSTI2020

The GLUEX DIRC program

The GLUEX experiment is located in experimental Hall D at Jefferson Lab (JLab) and provides a unique capability to search for hybrid mesons in high-energy photoproduction, utilizing a ~9 GeV linearly polarized photon beam. The initial, low-intensity phase of GLUEX was recently completed and a high-intensity phase has begun in 2020 which includes an upgraded kaon identification system, known as the DIRC (Detection of Internally Reflected Cherenkov light), utilizing components from the decommissioned BaBar DIRC. The identification of kaon final states will significantly enhance the GLUEX physics program, to aid in inferring the quark flavor content of conventional (and potentially hybrid) mesons. Here, we describe the installation of the GLUEX DIRC and the analysis of initial commissioning data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Foldy: An open-source web application for interactive protein structure analysis

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sulfate Conversion of Reillex HPQ Anion Exchange Resin for Disposal (Interim Report)

This report describes preliminary data to validate the Savannah River Plutonium Processing Facility’s (SRPPF) flowsheet for conversion of used Reillex HPQ anion exchange resin from the nitrate form to the sulfate form. The nitrate form is an oxidizer and therefore does not meet acceptance criteria for disposal at the Waste Isolation Pilot Plant (WIPP). The purpose of this study is to develop data to support acceptance for this disposition pathway. Due to the challenges characterizing the nitrate concentration on solid resin, the data developed to date are based upon indirect analysis of the ion exchange column effluent by ion chromatography. These challenges are discussed and two methods for quantification of nitrate directly on the resin are recommended for further development: TGA-MS and permanganate digestion followed by IC. The resin used for this work was provided in the chloride form; this is the form in which resin is supplied by the manufacturer. However, it had to be converted to the nitrate form, which is the form that will be used in SRPPF’s ion exchange process, prior to use in the sulfate conversion experiments. The chloride-form resin was characterized. A lab-scale procedure for the conversion of Reillex HPQ resin from the chloride to nitrate form was validated. The nitrate-form resin was assessed for particle size and chloride concentration to ensure it met SRPPF’s facility specifications. The baseline sulfate conversion flowsheet was tested. However, nitrate was still detectable in the effluent after approximately 10 bed volumes of 1 M sodium sulfate had been passed through the resin bed. Additional experiments were performed to assess the effect of increasing the feed volume, reducing the flowrate, the use of 2 M sulfuric acid instead of sodium sulfate, and the use of irradiated resin. The sulfuric acid test was the only one which provided a nondetectable nitrate concentration (<0.002 M) in the effluent. Detectable nitrate in the column effluent suggests that nitrate is still present on the resin itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

CTGAN-TVAE

SAND2026-18914O CTGAN-TVAE (Conditional Tabular Generative Adversarial Networks-Tabular Variational Autoencoders) generates extensive sets of variable generation data through a hybrid framework. It enhances latent space representation by combining TVAE's robust feature-embedding with CTGAN's ability to condition categorical variables such as time. CTGAN-TVAE employs a fully connected neural network within a conditional generative adversarial network framework to manage continuous and categorical data effectively, capturing complex feature interactions without needing sequential modeling. This was developed as part of NNSA-MSIPP: Minority Serving Institution Partnership Program, Grant Number DE-NA0004016. 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.

Newlun, Cody [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2021

Measurement of deeply virtual Compton scattering off 4 He with the CEBAF Large Acceptance Spectrometer at Jefferson Lab

We report on the measurement of the beam spin asymmetry in the deeply virtual Compton scattering off $^4$He using the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab using a 6 GeV longitudinally polarized electron beam incident on a pressurized $^4$He gaseous target. We detail the method used to ensure the exclusivity of the measured reactions, in particular the upgrade of CLAS with a radial time projection chamber to detect the low-energy recoiling $^4$He nuclei and an inner calorimeter to extend the photon detection acceptance at forward angles. Our results confirm the theoretically predicted enhancement of the coherent ($e^4$He$~\to~e'$$^4$He$'\gamma'$) beam spin asymmetries compared to those observed on the free proton, while the incoherent ($e^4$He$~\to~e'$p$'\gamma'$X$'$) asymmetries exhibit a 30$\%$ suppression. From the coherent data, we were able to extract, in a model-independent way, the real and imaginary parts of the only $^4$He Compton form factor, $\cal H_A$, leading the way toward 3D imaging of the partonic structure of nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Perspectives for artificial intelligence in bioprocess automation

Recent advances in artificial intelligence (AI) have rapidly changed the lab automation landscape, promoting self-driving laboratories (SDLs) that enable autonomous scientific discovery. These trends are increasingly applied in bioprocess development, yet bioprocessing faces unique challenges - biological complexity, regulatory and safety requirements, and multiscale experimentation - that distinguish it from other automation domains. Rather than pursuing full autonomy, we foresee that hybrid SDLs, combining AI-driven decision-making with sustained human oversight, represent the most practical near-term trajectory. This review examines three interconnected perspectives: (i) hybrid human-machine decision-making for bioprocessing; (ii) laboratory design considerations in the era of AI; and (iii) scale-up challenges when transitioning from screening to manufacturing. We highlight critical gaps in data standardization and the required community efforts necessary to realize autonomous bioprocess innovation.

Helleckes, Laura Marie↗
DOE OSTI2024

Isolating Signatures of Cyberattacks under Stressed Grid Conditions

In a controlled cyber-physical network, such as a power grid, any malicious data injection in the sensor measurements can lead to widespread impact due to the actions of the closed-loop controllers. While fast identification of the attack signatures is imperative for reliable operations, it is challenging to do so in a large dynamical network with tightly coupled nodes. A particularly challenging scenario arises when the cyberattacks are strategically launched during a grid stress condition, caused by non-malicious physical disturbances.

Ghosh, Sanchita [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2025

mvBayesR

SAND2025-11559O The mvBayesR tool performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis, and visualization. The tool compiles state-of-the-art methodology into one easy-to-use package. 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 OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗
DOE OSTI2024

Design and Optimization of Processes for Recovering Rare Earth Elements from End-of-Life Hard Disk Drives

In this poster, we first provide motivation for why rare earth elements as rare earth permanent magnets (REPM) are increasing in demand. We then highlight some of the recent work that has been done by several national labs (National Renewable Energy Laboratory (NREL), Environmental Protection Agency (EPA), Critical Minerals Institute (CMI)) on recycling rare earth elements from end-of-life hard disk drives (EOL). Then, we mention our long-term plan to design a feedstock agnostic process to recover rare earth elements as rare earth oxides from many different EOL products at once. Next, we discuss how we quantified the rare earth elements available for recycling from EOL hard disk drives from consumer desktops and laptops. We then discuss how we used superstructure optimization to design the optimal pathway. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Costing data from the literature was used to inform this model whenever possible. However, due to the novelty of this research area, data were often unavailable, thus requiring the generation of flowsheets implemented in Aspen Plus.

Laliwala, Chris↗
DOE OSTI2024

Energy Systems Integration Facility (ESIF): World-Class Systems Integration Capabilities and Research

The Energy Systems Integration Facility (ESIF), located at the National Renewable Energy Laboratory (NREL) South Table Mountain campus, is a world-renowned user facility for research and development of modern, advanced, and clean energy technologies. ESIF is distinguished by its continuously evolving, highly integrated systems that span throughout the building, connecting research capabilities across multiple laboratories and test areas. The primary ESIF research systems include: [1] data, cyber, and control networks, [2] research electrical distribution buses (REDB), [3] thermal integration infrastructure, and [4] hydrogen systems. The data, cyber, and control networks provide monitoring, control, communication, automation, visualization, and time series data storage and tagging capabilities for research projects and ESIF systems, including facility safety functions. The REDB system consists of four dedicated AC and DC electrical power networks that can connect devices located across the facility through versatile, automatic circuit configuration to support complex power electronics experiments up to the megawatt-scale. The thermal integration infrastructure consists of three temperature-conditioned water loops that provide heating and cooling interfaces and capabilities for thermal energy research. The hydrogen systems provide megawatt-scale hydrogen production, drying, compression, high-pressure storage, and delivery to laboratory end uses, including hydrogen fuel cell vehicle fueling. The ESIF research systems interconnect and extend throughout the various lab areas of the facility to create elaborate networks composed of diverse technologies for cutting-edge research. The ESIF capabilities are operated and stewarded by the ESIF Research Operations group, who also actively upgrade and advance the systems to ensure they remain ahead of anticipated research - enabling the success of many pioneering energy integration projects. The poster, created by members of the ESIF Research Operations team, highlights and summarizes the four core integrated systems at ESIF. The poster was first presented at the internal NREL Energize Forum on May 13th, 2024, and received the "Best Poster" award.

capabilities↗
DOE OSTI2022

Commissioning of a replacement subatmospheric cold box for Jefferson Lab’s Central Helium Liquefier

Jefferson Lab’s Cryogenics Department has designed, fabricated, installed, and commissioned a new subatmospheric cold box to replace one of the two existing units within our Central Helium Liquefier (CHL). The replacement cold box, dubbed SC1R, pumps saturated helium vapor at 0.0385 atm from Jefferson Lab’s continuous electron beam accelerator facility (CEBAF) cryomodules to maintain an operating temperature of nominally 2.1 K. This is accomplished using a five-stage cryogenic centrifugal compressor (cold compressor) system and a brazed aluminum plate-fin heat exchanger operating between 2.1 K and 4.5 K. In this paper we will describe our experience commissioning the SC1R cold box. We will discuss pump-down of the system to 2.1 K and steady-state operation at the cold compressor design flow rates of 170, 200, and 250 g/s. Performance of the heat exchanger and cold compressors has been mapped across a range of flow rates and optimized for CEBAF operations. This commissioning data will be used to monitor future performance and adapt to changing load requirements.

Mastracci, B.↗