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DOE OSTI2024

Pelletization with Spark Plasma Sintering and Characterization of Metal Iodides: An Assessment of Long-Term Radioiodine Immobilization Options

Four promising iodine “getter” materials (Ag, Cu, Bi, and Sn) for radioiodine capture were assessed in their pure metal-iodide (MI x ) pelletized forms to compare relative chemical durabilities. To study chemical durability, commercial MI x compounds of AgI, BiI 3 , BiOI, CuI, and SnI 4 were converted to dense monolithic pellets using spark plasma sintering. Semidynamic leach testing in the form of modified ASTM C1308 tests was then performed on the pellets in two different forms including unmounted (as-pressed) specimens (i.e., “U”) and epoxy-mounted specimens (i.e., “M”) with polished surfaces. The chemical durability results and sample characterizations showed that three of the five MI x compounds tested (i.e., AgI, CuI, and BiOI) displayed moderate to high leach resistances. Further, the remaining two MI x compounds (i.e., BiI 3 and SnI 4 ), which are both desirable iodine waste forms due to their high iodine loading capacities, readily decomposed during leach testing, indicated by crystallographic changes in the specimens as well as large amounts of iodine detected in the leachate solutions. The instabilities of BiI 3 and SnI 4 raise uncertainties for using the base metals/cations (i.e., Bi 0 /Bi 3+ and Sn 0 /Sn 4+ , respectively) as viable getters for radioiodine capture due to likely poor waste form chemical durabilities after capture and consolidation into waste forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Linking microstructure to creep behavior in vertically and horizontally built LPBF Haynes 282 compared with wrought material via θ -projection

Laser Powder Bed Fusion (LPBF) has emerged as a promising route for fabricating intricate geometries in high-performance alloys. Haynes 282 (H282) is a strong candidate for applications such as heat exchangers or engines due to its excellent creep strength and thermal stability; however, the long-term creep behavior of LPBF-processed H282 remains poorly understood. In this study, the θ -projection method is used to analyze and extrapolate the creep behavior of vertically built LPBF, horizontally built LPBF, compared to wrought H282 tested at 816 °C. Vertically built LPBF H282 exhibits the lowest minimum creep rate (MCR), while the horizontally built condition shows a higher MCR comparable to that of wrought H282. Despite these differences, both LPBF conditions exhibit significantly shorter rupture life and reduced rupture strain than the wrought material, with the most severe degradation observed in the horizontal builds, consistent with an earlier onset of tertiary creep and accelerated strain-rate evolution. Microstructural characterization reveals that both LPBF and wrought H282 exhibit abundant twin-related boundary character; however, their grain boundary topologies differ markedly. The wrought alloy contains a higher fraction of low-angle grain boundaries and continuous twin lamellae, whereas the LPBF microstructure is characterized by a suppressed low-angle boundary population and fragmented twin-related boundaries embedded within irregular high-angle grain boundary networks. Fractographic analysis further reveals predominantly intergranular cracking in LPBF H282, accompanied by grain-boundary-decorated carbides, Al 2 O 3 inclusions, and high-aspect-ratio pores. These results demonstrate that grain boundary topology, rather than minimum creep rate alone, plays a critical role in governing creep damage accumulation and rupture behavior in LPBF and wrought H282.

Creep↗

DOE OSTI2021

Consensus International Solar Resource Standards and Best Practices Development

Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.

ASTM↗

DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2024

Creating Continuous Integration Infrastructure for Software Development on U.S. Department of Energy High-Performance Computing Systems

The Exascale Computing Project (ECP) software deployment effort developed and advanced DevOps capabilities. One goal was to enable robust continuous integration (CI) workflows that span the protected high performance computing (HPC) environments found within many of the Department of Energy’s (DOE) national laboratories. This article highlights several challenges encountered with enabling automation, such as charging models for CI jobs, and meeting individualized security requirements that revolve around strongly associating running code with a human identity. Here, it also describes how the Jacamar CI tool evolved to meet latter requirements and became a key aspect of the solutions currently offered. Derived from this experience, we offer a conceptual framework for understanding current and future CI challenges at DOE facilities and offer suggestions for long-term solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Long-term drought promotes invasive species by reducing wildfire severity

Anthropogenic climate change has increased the frequency of drought, wildfire, and invasions of non-native species. Although high-severity fires linked to drought can inhibit recovery of native vegetation in forested ecosystems, it remains unclear how drought impacts the recovery of other plant communities following wildfire. We leveraged an existing rainfall manipulation experiment to test the hypothesis that reduced precipitation, fuel load, and fire severity convert plant community composition from native shrubs to invasive grasses in a Southern California coastal sage scrub system. We measured community composition before and after the 2020 Silverado wildfire in plots with three rainfall treatments. Drought reduced fuel load and vegetation cover, which reduced fire severity. Native shrubs had greater prefire cover in added water plots compared to reduced water plots. Native cover was lower and invasive cover was higher in postfire reduced water plots compared to postfire added and ambient water plots. Our results demonstrate the importance of fuel load on fire severity and plant community composition on an ecosystem scale. Management should focus on reducing fire frequency and removing invasive species to maintain the resilience of coastal sage scrub communities facing drought. In these communities, controlled burns are not recommended as they promote invasive plants.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Operando carbon corrosion measurements in fuel cells using boron-doped carbon supports

Carbonaceous materials are the most common catalyst supports in proton exchange membrane fuel cell (PEMFCs), yet their corrosion is one of the limiting factors in achieving high durability. Herein, we doped carbon supports with boron (B) to increase the corrosion-resistance of the support. Two types of B-doped carbons were synthesized and studied as platinum support materials. Further, they varied in their morphologies, surface areas, and the types of boron species. The durability of Pt/B-doped carbon catalysts was investigated using the US-DOE catalysts’ supports accelerated stress test (AST) and a mass-spectrometer connected to the fuel cell effluent stream to quantify the mass of corroded carbon support in operando. The addition of boron to the carbon increased the stability of Pt catalysts in long-term usage of PEMFC. After 4000 AST cycles, more than 50% of initial current density was preserved for the boron-containing systems, while less than 30% of it remained with Vulcan carbon (Pt/V). Also, the Pt/B-doped carbon samples demonstrated better electrochemical active surface area (ECSA) stability when compared to Pt/V. Carbon loss measurements showed that B-doped carbons have higher resistance to electrochemical corrosion than unmodified carbon. Specifically, the substitutional boron-doped carbon demonstrated an extremely high stability and low corrosion rate.

25 ENERGY STORAGE↗

DOE OSTI2025

In-situ formation of stable interface towards Li-in anode for halide solid-state electrolyte

Halide-based solid-state electrolytes (SSEs) are promising candidates for next-generation all-solid-state lithium batteries (ASSLBs) due to their high ionic conductivity and chemical stability. However, their poor interfacial compatibility with lithium metal anode and Li-In alloy significantly hinder practical application due to the requirement for a protective interlayer. In this study, a novel approach to overcome this limitation is presented by introducing iron (Fe) doping into Li 3 InCl 6 (LIC), which enables direct and stable contact with lithium-indium (Li-In) metal without a protective interlayer. Thermodynamic and computational analyses identified Fe 3+ as a suitable dopant based on its similar reduction potential to In 3+ and structural compatibility within the halide lattice. The synthesized 10 at. % Fe-doped LIC exhibits high phase purity, retained ionic conductivity, and notably improved interfacial stability. Full-cell tests using Fe-LIC achieve over 300 cycles with 80 % capacity retention. At the same time, symmetric Li-In/ Fe-LIC/ Li-In cells sustain over 500 h of operation, representing the first reported long-term cycling of LIC-based ASSLB without a protective interlayer. In conclusion, this work establishes Fe doping as an effective strategy to stabilize halide SSEs of In system against Li-In alloy, thereby simplifying cell architecture and advancing the development of safer, high-performance halide-based solid-state electrolytes.

Halide-based solid-state electrolytes↗

DOE OSTI2024

Development of a Data Overflow Protection System for Super-Kamiokande to Maximize Data from Nearby Supernovae

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data.

F20 Instrumentation and technique↗

DOE OSTI2020

A Fast Flexibility-Driven Generation Portfolio Planning Method for Sustainable Power Systems

We are witnessing an acceleration in the uptake of renewable energy in power systems. Because of the associated variability and uncertainty of renewables, power systems need to have an adequate supply of flexibility to allow for suitable management of short-term operations. So far most of the work in this area has neglected how flexibility needs associated with renewables are fulfilled as part of dispatchable generation capital investments decisions. To address this challenge, we propose an approach to plan the dispatchable generation mix of a power system as needed to counteract variability and uncertainty associated with significant shares of variable renewable generation. The approach exploits the linear time-invariant feature of variable generation variability using historical phase planes of capacity (in MW) and ramp (in MW/min) to bridge the gap between long-term capacity planning and short-term intra-hour flexibility needs. This approach is much more computationally tractable than other proposals, while also being able to capture adequately short-term operational features like ramping and net load variability. Numerical tests are performed on realistic datasets to substantiate the effectiveness of the approach.

bulk power system planning↗

DOE OSTI2025

Managing technical debt across large-scale control systems

This presentation provides a comprehensive overview of technical debt in the context of control systems for large-scale physics facilities. We will explore its various forms, common causes, and potential consequences on system reliability, maintainability, and upgradability. Drawing on experiences from multiple projects, we will present a range of strategies for proactively managing technical debt, including best practices in design, development, testing, and documentation, as well as reactive approaches for identifying and mitigating existing issues. The presentation will also emphasize the importance of a collaborative approach and the use of modern tools, like fault tracking systems, to ensure the long-term health and success of control systems in our field.

Watts, Adam [Fermilab]↗

DOE OSTI2021

Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent unit (GRU) networks have similar prediction performance in constitutive modelling problems, and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path. Furthermore, the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

42 ENGINEERING↗

DOE OSTI2025

Accelerating effects of galvanic corrosion and dissimilar materials on the corrosion of 316H in NaCl-MgCl2 salt

Corrosion of materials presents a significant challenge for the long-term operation of molten salt reactors. This study aims to identify the most effective techniques for evaluating the corrosion performance of materials in molten salts, with a focus on the effects of galvanic corrosion and dissimilar materials. A reliable testing methodology for assessing material corrosion in molten chloride salts has been successfully developed. The corrosion of Alloy 316H in molten NaCl-MgCl2 salt was found to be significantly accelerated by galvanic corrosion. Additionally, the presence of dissimilar materials resulted in a slight increase in the corrosion rate of Alloy 316H in NaCl-MgCl2 salt. Microstructural characterization was utilized to understand the corrosion behavior of test samples under different conditions. Common trends observed across samples include chromium depletion and iron enrichment near corroded surfaces. Molybdenum enrichment along grain boundaries and corrosion surfaces was also frequently noted.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Comprehensive Land Use and Environmental Stewardship

Comprehensive Land Use and Environmental Stewardship (CLUES) Report serves as a summary document of the land use and environmental stewardship activities occurring on the Idaho National Laboratory (INL) Site (Desert) and the Research and Education Campus (REC) within Idaho Falls. Land and facility use planning and decisions at the INL Site are guided by a comprehensive planning process in accordance with the United States (U.S.) Department of Energy (DOE) Order 430.1C, “Real Property Asset Management,” which states "Establish a data-driven, risk-informed, performance-based approach to the life-cycle management of real property assets that aligns the real property portfolio with DOE mission needs; acquire, manage, positively account for, and dispose of real property assets in a safe, secure, cost-effective, and sustainable manner; and ensure the real property portfolio is appropriately sized, aligned, and in the proper condition to support efficient mission execution." Land use planning, like Campus Master Planning and 5-Year Facility Planning, provides a means for better, more sustainable use of the INL Site in a coordinated effort to ensure current and future mission needs are met, including acquisition, recapitalization, maintenance, disposition, real property utilization and long-term stewardship. This document and all functions of INL are guided by the DOE Vision for INL and the INL Mission and builds on the baseline established in the FY 2015 CLUES Report. However, it delivers a revised structure with focus on new resource management zones, which provide organization of key management considerations and access restrictions to optimize land use and environmental stewardship. A new set of thematic Guiding Principles presented in this report provide the intent and sustainable management direction to protect the INL Site natural environment. They demonstrate that the mission and vision of DOE and INL can be realized with inclusion of first of a kind technology, private sector development, and globally recognized testing and demonstration. This CLUES Report encourages comprehensive management decision-making with additional resource discussions for sustainability, the built environment, and the ecological landscape at the INL Site. Enhanced resource considerations and trends emphasize the importance of air, land use, environmental, subsurface, and cultural resources. The INL Site supports exceptional and interdependent resources. A diversity of bat species are accommodated by culturally significant caves distributed around the Site. Big game species like elk, deer, and moose seek vegetation communities which are protected by long-term stewardship and monitoring programs.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Corrosion of U233-Doped Uranium Oxide using Microfluidics Methods

The aim of work this year has been to investigate the role of alpha (α)-radiation and hydrogen (H 2 ) on the corrosion of uranium oxide (UO 2 ) using a microfluidic device. The microfluidic device, termed the Particle-Attached Microfluidic Electrochemical Cell, (PAMEC), enables monitoring of the UO 2 electrochemical corrosion potential (E corr ) under different environments, including de-aerated conditions and in the presence of dissolved H 2 . The Si 3 N 4 window allows us to study morphological and chemical changes under an electron microscope. We have previously demonstrated that the PAMEC matches the results from bulk electrochemical tests with UO 2 [1]. We used the high specific activity uranium (U) isotope, 233 U, (t 1/2 = 160,000 years) incorporated into UO 2 , to generate a localized a-field. The objective of the experiments were to mimic the radiation environment that would be experience at the surface of aged spent nuclear fuel (SNF) during long-term geologic disposal under anoxic conditions. Wittman et al. [2] predicted that in the presence of a pure a-radiation field and under H 2 conditions, the concentration of the radiolytic oxidant H 2 O 2 would be suppressed or even eliminated. In a UO 2 corrosion experiment this would be exhibited through a lowering of the measured UO 2 corrosion potential compared to identical conditions in the absence of dissolved H 2 . We found that the predictions of Wittman and co-workers were supported and that the corrosion potential of the 233 U-doped UO 2 in solution lowered with presence of H 2 gas and increased in the absence of H 2 , when under anoxic conditions. The PAMEC experiments indicate that H 2 O 2 has been eliminated in a solution sparged with H 2 while exposed to an a-radiation field. The corrosion potential of the a-doped 233 U(10%)- 238 UO 2 in a solution sparged with Ar/H 2 matched the corrosion potential of 238 UO 2 in solution sparged with air. This clearly demonstrated that the H 2 O 2 had been eliminated and that the only oxidant present was O 2 in this system in complete agreement with the modeling results of Wittman et al. These results point to the need to improve the Fuel Matrix Degradation (FMD) Process Model training data set that is being used in the FMD surrogate model that is being developed for the repository program. The incorporation of realistic radiation chemistry will improve the scientific basis for the FMD Model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A Quench Detection and Monitoring System for Superconducting Magnets at Fermilab

A quench detection system was developed for protecting and monitoring the superconducting solenoids for the Muon-to-Electron Conversion Experiment (Mu2e) at Fermilab. The quench system was designed for a high level of dependability and long-term continuous operation. It is based on three tiers: Tier-I, FPGA-based Digital Quench Detection (DQD); Tier-II, Analog Quench Detection (AQD); and Tier-3, the quench controls and data management system. The Tier-I and Tier-II are completely independent and fully redundant systems. The Tier-3 system is based on National Instruments (NI) C-RIO and provides the user interface for quench controls and data management. It is independent from Tiers I & II. The DQD provides both quench detection and quench characterization (monitoring) capability. Both DQD and AQD have built-in high voltage isolation and user programmable gains and attenuations. The DQD and AQD also includes user configured current dependent thresholding and validation times. A 1 st article of the three-tier system was fully implemented on the new Fermilab magnet test stand for the HL-LHC Accelerator Up-grade Project (AUP). It successfully provided quench protection and monitoring (QPM) for a cold superconducting bus test in November 2020. The Mu2e quench detection design has since been implemented for production testing of the AUP magnets. A detailed description of the system along with results from the AUP superconducting bus test will be presented.

monitoring↗

DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗