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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Power-to-Gas Tool: User's Guide

PNNL, working in collaboration with ITM Power and HG&E, has developed a suite of products to support analysis of P2G in Massachusetts. These products include: (a) P2G model that can be accessed at eset.pnnl.gov, (b) P2G system valuation report that describes in detail the methodology underpinning the P2G model and presents the results of 82 scenarios run using the model, and (c) a P2G model user’s guide. This user’s guide accompanies the other products and is designed to simplify the experience of each user, providing useful instructions while guiding users as they input data and interpret results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Greenhouse Gas Production and Soil Chemistry in Anaerobic Soil Microcosm Incubations after Nitrogen Addition, Teller Road Site, Seward Peninsula, 2018-2019

This dataset provides the results of laboratory soil microcosm incubation experiments conducted under anaerobic conditions with and without the addition of ammonium chloride (NH4Cl). The microcosms contained soil collected in April 2017 from the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) near Intensive Site 9 (Toeslope) and Intensive Site 5 (Plateau). Incubations were conducted at 8°C for 55 days, with repeated measurements of headspace greenhouse gas concentrations. Microcosms were destructively harvested and extracted using ultrapure water and 0.1 M KCl for analysis of the porewater geochemistry. This dataset contains two csv files, a pdf user guide. and the raw data in a zip file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Calcination of kaolinite clay particles for cement production: A modeling study

Kaolinite rich clay particles calcined under certain conditions can attain favorable pozzolanic properties and can be used to substitute part of the CO{sub 2} intensive clinker in cement production. To better guide calcination of a clay material, a transient one-dimensional single particle model is developed, which fully addresses the conversion process of raw kaolinite particles suspended in hot gas. Particles are discretized into a number of spherical cells, on each of which mass, momentum, energy and species conservation equations are numerically solved by using the finite volume method. Reactions considered in the model include dehydration, dehydroxylation and various phase transformations. Thermogravimetric analysis is used to determine reaction kinetic data required as inputs in the model and to validate the model. Finally, model-based sensitivity analysis is performed, from which quantitative guidelines for calcination condition optimization are derived. - Highlights: • A general 1D mathematical model for single clay particle calcination is developed. • The model fully addresses momentum, heat and mass transfer and all the reactions. • Experiments are performed to determine kinetic data of the key reactions. • The model is verified by different means, including experimental results. • Sensitivity study is done to address key assumptions and derive useful guidelines.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Salts in Hot Water: Developing a Scientific Basis for Supercritical Desalination and Strategic Metal Recovery

Given the steady growth in world population and the ever-increasing fresh water demand, the only sustainable water supply option is desalination. Conceptually, desalination is very simple - just separate dissolved salts from water. While desalination research programs have existed since the 1960s and have resulted in a multitude of approaches, realizing affordable desalination has proven to be a challenge. Our goal is to meet this challenge using low cost heat sources to drive a liquid-discharge-free desalination process that can be co-mingled with the extraction of economically valuable co-products. High temperatures and pressures will be used to manipulate water’s properties, such as the dielectric constant, and hence its ability to solvate ions. Selective precipitation/recovery of valuable metal co-products creates new revenue streams (i.e., cost offsets). We will develop the scientific understanding necessary to control salt precipitation from supercritical seawater, inland brines, and water commonly co-produced with oil and gas. Atomistic simulations will be used to understand at a molecular level the changes in hydrogen bonding and ion solvation that occur as we increase the temperature and/or pressure and change the density and ion concentration. Our simulation results will also aid in the interpretation of experimental data and guide the development of applied thermodynamic models for engineering design calculations.

58 GEOSCIENCES↗
DOE OSTI2016

Lifecycle Industry GreenHouse gas, Technology and Energy through the Use Phase (LIGHTEnUP) – Analysis Tool User’s Guide

The LIGHTEnUP Analysis Tool (Lifecycle Industry GreenHouse gas, Technology and Energy through the Use Phase) has been developed for The United States Department of Energy’s (U.S. DOE) Advanced Manufacturing Office (AMO) to forecast both the manufacturing sector and product life-cycle energy consumption implications of manufactured products across the U.S. economy. The tool architecture incorporates publicly available historic and projection datasets of U.S. economy-wide energy use including manufacturing, buildings operations, electricity generation and transportation. The tool requires minimal inputs to define alternate scenarios to business-as-usual projection data. The tool is not an optimization or equilibrium model and therefore does not select technologies or deployment scenarios endogenously. Instead, inputs are developed exogenous to the tool by the user to reflect detailed engineering calculations, future targets and goals, or creative insights. The tool projects the scenario’s energy, CO 2 emissions, and energy expenditure (i.e., economic spending to purchase energy) implications and provides documentation to communicate results. The tool provides a transparent and uniform system of comparing manufacturing and use-phase impacts of technologies. The tool allows the user to create multiple scenarios that can reflect a range of possible future outcomes. However, reasonable scenarios require careful attention to assumptions and details about the future. This tool is part of an emerging set of AMO’s life cycle analysis (LCA) tool such as the Material Flows the Industry (MFI) tool, and the Additive Manufacturing LCA tool.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Mitigation of ventilation air methane (VAM) using novel methanotrophic coating materials: a technical analysis

Abstract Ventilation air methane (VAM) is a potent greenhouse gas source originating from geological wells, current and extinct mineshafts and other terrestrial conduits venting methane to the atmosphere, contributing to global methane emissions and disproportionate warming potential. Herein, we introduce the concept of the methanotrophic material as an engineering solution. Such materials should be capable of converting methane at ambient temperatures and pressures to a binder product, capturing and permanently sequestering the methane while simultaneously restricting its further emission. While such materials are currently under research development, this goal is supported and facilities by the mathematical framework, introduced and used herein, to evaluate the ability to convert methane, using currently published activity data. We include a case study of the conversion of a characteristic stream of VAM (0.6% methane in air, 1.7 × 10 8 l hr −1 equivalent to 100 000 standard cubic feet per minute). We show that when appropriately designed, such systems require a surface coverage of less than 1000 m of mine tunnel length (equivalent to 20 000 m 2 areal coverage) in order to reduce the methane emission from this stream by over 99%. Finally, we highlight formaldehyde as a reactive intermediate of methane oxidation which may itself be incorporated into these coating materials. As a component of binders and polymers already used ubiquitously in commercial products, this intermediate ultimately allows these systems to sequester the carbon from methane in a stable and solid form. The results presented here are easily extended to the treatment of other methane streams—either more concentrated or dilute—and the results herein will guide the design and development of a new class of carbon-negative materials.

Environmental Sciences & Ecology↗
DOE OSTI2024

Bubble Mass Transport Measurement in the Large-Scale Test Loops at Oak Ridge National Laboratory

Molten salts are complex fluids that incorporate multi-phase behavior depending on the chemistry and the physical properties of the entrained components. These components include the carrier salt, the actinide fuel, and fission, activation, and corrosion products, the concentrations of which depends on the burnup history of the salt. Radionuclide transport from the salt into the cover gas / off-gas system depends on volatility as predicted by thermochemistry, but data from the Molten Salt Reactor Experiment (MSRE) conducted in the late 1960s suggest that bubble formation and transport are also important. Anomalously high fractions of noble metals were found in the off-gas system and were attributed to transportation with parent salt aerosols and their association with rising noble gas bubbles. The prediction of such phenomena requires coupled neutronic, thermal hydraulic, and chemical equilibrium calculations, the framework of which is being developed within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supported by the US Department of Energy (DOE) Office of Nuclear Energy (NE). However, separate effects tests and models of experiments using tools such as SAM and Thermochimica can guide model developers through important processes. Conversely, model development guides the choice of experiments and systems to provide data that are relevant for validation. This report describes several experiments that tracked gas transport in molten salts, ranging from small-scale systems to large-scale loops. Gas transportation in a molten salt, LiCl-KCl, has been studied using the shadowgraph technique. Sensors such as residual gas analysis, Raman spectroscopy, and laser-induced breakdown spectroscopy have been tested for off-gas measurements. These data were used to interpret how the gases move through the upstream salt / cover gas and the interface between them. Differential pressure measurements were able to detect individual gas bubbles as they popped at the liquid–gas interface. Salt aerosols were collected on a cascade impactor. Their formation was also observed directly via the shadowgraph method, and most of these aerosols were launched ballistically into the cover gas. Fine mists could also be observed. Convection currents through the salt were visualized and can be used to calculate the thermophysical properties of the salt itself. The apparatuses described in this report and in a previous work (McFarlane et al. 2023) have been commissioned and are available for use in making further measurements of salt/surrogate fission product behavior. Novel approaches using neutron imaging are planned for the study of fluoride salts, which cannot be contained in quartz, so shadowgraph visualization is not available. Bubble transport in convective flow and in a slow-moving salt column are planned. The mobile laser-induced breakdown spectroscopy (LIBS) system is available for several applications, including iodine capture in a molten hydroxide scrubber, H2 transport though molten salts to complement Raman analysis, and online tracking of salt aerosol generation, transport, and deposition. This report summarizes the findings from FY24 and the plans for FY25. The work completes milestone M2AT-24OR0702013 of the DOE-NE Advanced Reactor Technology, Molten Salt Reactor Campaign, DOE-NE-5.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

MINOS Infrasound Analysis Synopsis

This report was written as a guide to working with infrasound data collected as part of the Multi-Informatics for Nuclear Operations Scenarios (MINOS)project, an NA-22 funded venture. The main purpose of overall MINOS project is the combination of multiple, disparate data modalities to characterize the operations at a nuclear facility, specifically instrumenting and studying the High-Flux Isotope Reactor (HFIR) and Radiochemical Engineering Development Center (REDC) locate at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are constructed, processed, and dissolved at REDC. REDC also hosts other glove-box and hot-cell type activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Energy Efficient Material Processing through Automated Process Monitoring and Controls

Smart manufacturing is bound to play a crucial role in reducing global energy consumption while accelerating economic development. This phenomenon is evident in advanced sensing technology developments, data analytics and machine learning/AI, automated controls, cloud computing, etc. The immediate opportunity for smart manufacturing is to improve the energy efficiency of manufacturing operations through innovations in processes and controls. The manufacturing industry still consumes about 30% of total global energy production, which is significant. This program focused on the recommendation of heterogeneous sensors to monitor the Chemical Vapor Infiltration (CVI) process parameters, states, and key performance indices and utilize cloud computing to sort and analyze the real-time data. Learning through data analytics helps guide the control parameters affecting energy usage. The target process in the program is the CVI process at the Honeywell South Bend facility, which is one of the most energy-intensive manufacturing industries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’.

abiotic stress↗
DOE OSTI2017

Improvements to Modeling Capabilities of ATF Concepts in the BISON Fuel Performance Code

Research into accident tolerant materials for the fuel and cladding in Light Water Reactors has been at the forefront of the nuclear fuel research community since the events that oc- curred at the Fukushima Daiichi nuclear power plant in March 2011. Accident tolerant materials are defined as those that provide significantly increased response time in the event of an accident while providing similar or improved performance as the conventional UO2/zirconium-based cladding fuel rods dur- ing normal operation [1]. In particular, qualitative attributes for materials with enhanced accident tolerance include im- proved reaction (e.g., oxidation) kinetics with steam, resulting in slower hydrogen (or other combustible gases) generation rate, while maintaining acceptable thermo-mechanical prop- erties, fuel-clad interactions, and fission-product behavior [1]. Through its Office of Nuclear Energy the United States De- partment of Energy (U.S. DOE) has accelerated research in this area through the Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). The goal of the AFC Accident Tolerant Fuel (ATF) program is to guide se- lection of promising concepts for insertion into a commercial reactor as part of a lead test rod or assembly in 2022. Given the aggressive development schedule, it is impossi- ble to perform a comprehensive set of experiments to provide material characterization data. Therefore, the AFC is utilizing computational analysis tools in an effort to understand the proposed accident tolerant materials. The two materials of interest in this work are iron-chromium-aluminum (FeCrAl) cladding and U3Si2. From a fuel performance standpoint, the correlations available to describe the behavior of these two materials is limited. Therefore, as additional experimental data becomes available and correlations are updated, the fuel performance models must be updated to represent the current state of the art understanding of these materials. In this paper we demonstrate the importance of utilizing the latest state-of-the-art models for fuel performance simula- tions by comparing old and new thermal conductivity models for U3Si2 fuel and old and new oxidation models for FeCrAl cladding alloys using the BISON [2, 3] fuel performance code under normal operating conditions. The results indicate that the latest thermal conductivity model for U3Si2 results in fuel centerline temperatures that are on average approximately 3% higher than the previous model. For the oxidation behavior of FeCrAl alloys the new model predicts observable but small ox- ide thickness (~6 µm) compared to the previous model which predicted zero oxide scale formation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Carbon Calculator for Land Use Change from Biofuels Production (CCLUB). Users' Manual and Technical Documentation

The Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) calculates carbon emissions from land use change (LUC) for four different ethanol production pathways including corn grain ethanol and cellulosic ethanol from corn stover, miscanthus, and switchgrass. This document discusses the version of CCLUB released May 31, 2012 which includes corn, as did the previous CCLUB version, and three cellulosic feedstocks: corn stover, miscanthus, and switchgrass. CCLUB calculations are based upon two data sets: land change areas and above- and below-ground carbon content. Table 1 identifies where these data are stored and used within the CCLUB model, which is built in MS Excel. Land change area data is from Purdue University's Global Trade Analysis Project (GTAP) model, a computable general equilibrium (CGE) economic model. Section 2 describes the GTAP data CCLUB uses and how these data were modified to reflect shrubland transitions. Feedstock- and spatially-explicit below-ground carbon content data for the United States were generated with a surrogate model for CENTURY's soil organic carbon sub-model (Kwon and Hudson 2010) as described in Section 3. CENTURY is a soil organic matter model developed by Parton et al. (1987). The previous CCLUB version used more coarse domestic carbon emission factors. Above-ground non-soil carbon content data for forest ecosystems was sourced from the USDA/NCIAS Carbon Online Estimator (COLE) as explained in Section 4. We discuss emission factors used for calculation of international greenhouse gas (GHG) emissions in Section 5. Temporal issues associated with modeling LUC emissions are the topic of Section 6. Finally, in Section 7 we provide a step-by-step guide to using CCLUB and obtaining results.

09 BIOMASS FUELS↗
DOE OSTI2011

Sustainable LED Fluorescent Light Replacement Technology

Ilumisys and the National Center for Manufacturing Sciences (NCMS) partnered on a three-year project awarded by the United States (U.S.) Department of Energy (DOE), to quantify the impacts of LED lamps, incandescent lamps and fluorescent benchmark lamps over a product lifecycle – i.e. to develop a sustainable design and manufacturing strategy that addresses product manufacturing, use, recycling and disposal scenarios for LED-based lighting. Based on the knowledge gained from extensive product tear-down studies of fluorescent and screw-in lighting products, lifecycle assessment tools, and accelerated lifecycle testing protocols, an interactive Sustainable LED Design Guide has been developed to aid architectural and lighting designers and engineers in making design decisions that consider three important environmental impacts (greenhouse gas emissions, energy use and mercury emission) across all phases of the life of an LED lighting product. Critical information developed for the lifecycle analysis and product feature comparisons is the useful life of the lighting product as well as its performance. The Design Guide is available at www.ncms.org, and was developed based on operational and durability testing of a variety of lighting products including power consumption, light output, and useful life of a lamp in order to allow a more realistic comparison of lamp designs. This report describes the main project tasks, results and innovative features of the lifecycle assessment (LCA)-based design tools, and the key considerations driving the sustainable design of LED lighting systems. The Design Guide incorporates the following three novel features for efficiently evaluating LED lighting features in value-chains: Bill-of-Materials (BOM) Builder – Designers may import process data for each component and supply functional data for the product, including power, consumption, lumen output and expected useful life: Environmental Impact Review – Designs are comparable across lifecycle phases, subsystems, and environmental impact category, and can be normalized to a user-defined functional unit: Drill-down Review – These provide an in-depth look at individual lamp designs with the ability to review across subsystem or lifecycle phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Earth Filter Mathematica Toolbox V4 User’s Guide: Simplifying, Visualizing and Quick Modeling of All Fission Products from 60 Nuclear Sources

The Earth Filter Mathematica toolbox is a set of user-friendly routines that accurately calculate the ingrowth and decay of any of the 908 fission products for an infinite combination of sources over a large dynamic range and large time scales. This toolbox was developed by Dr. W. P. Brug at Los Alamos National Laboratory and eliminates the need for the user to define and solve differential equations by automatically scanning the ENDF-349 dataset and creating a complete set of differential equations (including delayed neutron branches to both the ground and metastable states) for every fission product. Additionally, this code allows the user to choose the data source for half-lives from several key references (i.e., TORI, IAEA, ENDF or NUDAT.) The complete set of ENDF-349 independent and cumulative fission yields for all 60 isotopic targets and 4 different neutron spectrums are included. This allows nearly any nuclear fission source to be accurately modeled as a linear combination of endpoint sources. The Earth Filter Toolbox has no limit as to the length of split list, and split lists of 5 or more endpoint sources can be quite common.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Study of quasielastic scattering using charged-current ν μ -iron interactions in the MINOS near detector

Kinematic distributions from an inclusive sample of 1.41×10 6 charged-current ν μ interactions on iron, obtained using the MINOS near detector exposed to a wide-band beam with peak flux at 3 GeV, are compared to a conventional treatment of neutrino scattering within a Fermi gas nucleus. Results are also used to guide the selection of a subsample enriched in quasielastic ν μ Fe interactions, containing an estimated 123,000 quasielastic events of incident energies 1 < E ν < 8 GeV, with < E ν > = 2.79 GeV. Four additional subsamples representing topological and kinematic sideband regions to quasielastic scattering are also selected for the purpose of evaluating backgrounds. Comparisons using subsample distributions in four-momentum transfer Q 2 show the Monte Carlo model to be inadequate at low Q 2 . Its shortcomings are remedied via inclusion of a Q 2 -dependent suppression function for baryon resonance production, developed from the data. A chi-square fit of the resulting Monte Carlo simulation to the shape of the Q 2 distribution for the quasielastic-enriched sample is carried out with the axial-vector mass M A of the dipole axial-vector form factor of the neutron as a free parameter. Furthermore, the effective M A which best describes the data is 1.23 +0.13 -0.09 (fit) +0.12 -0.15 (syst) GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗