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DOE OSTI2023

A Quick Look at the 3 GHz Radio Sky. II. Hunting for DRAGNs in the VLA Sky Survey

Active galactic nuclei (AGNs) can often be identified in radio images as two lobes, sometimes connected to a core by a radio jet. This multicomponent morphology unfortunately creates difficulties for source finders, leading to components that are (a) separate parts of a wider whole, and (b) offset from the multiwavelength cross identification of the host galaxy. In this work we define an algorithm, DRAGN HUNTER , for identifying double radio sources associated with AGNs (DRAGNs) from component catalog data in the first epoch Quick Look images of the high-resolution (≈3" beam size) Very Large Array Sky Survey (VLASS). We use DRAGN HUNTER to construct a catalog of >17,000 DRAGNs in VLASS for which contamination from spurious sources is estimated at ≈11%. A “high-fidelity” sample consisting of 90% of our catalog is identified for which contamination is <3%. Host galaxies are found for ≈13,000 DRAGNs as well as for an additional 234,000 single-component radio sources. Using these data, we explore the properties of our DRAGNs, finding them to be typically consistent with Fanaroff–Riley class II sources and to allow us to report the discovery of 31 new giant radio galaxies identified using VLASS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Simulating Atomic Dark Matter in Milky Way Analogs

Dark sector theories naturally lead to multicomponent scenarios for dark matter where a subcomponent can dissipate energy through self-interactions, allowing it to efficiently cool inside galaxies. We present the first cosmological hydrodynamical simulations of Milky Way analogs where the majority of dark matter is collisionless cold dark matter (CDM) but a subcomponent (6%) is strongly dissipative minimal atomic dark matter (ADM). The simulations, implemented in GIZMO and utilizing FIRE-2 galaxy formation physics to model the standard baryonic sector, demonstrate that the addition of even a small fraction of dissipative dark matter can significantly impact galactic evolution despite being consistent with current cosmological constraints. We show that ADM gas with roughly standard model–like masses and couplings can cool to form a rotating “dark disk” with angular momentum closely aligned with the visible stellar disk. The morphology of the disk depends sensitively on the parameters of the ADM model, which affect the cooling rates in the dark sector. The majority of the ADM gas gravitationally collapses into dark “clumps” (regions of black hole or mirror star formation), which form a prominent bulge and a rotating thick disk in the central galaxy. These clumps form early and quickly sink to the inner ∼kiloparsec of the galaxy, affecting the galaxy’s star formation history and present-day baryonic and CDM distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets

Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Arctic clouds↗

DOE OSTI2024

Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1 0 FeNi."

We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe 4 Ni 3 X and Fe 3 Ni 4 X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L1 0 order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization

Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decom-position strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installation and explore various decomposition techniques to enhance solution efficiency.

Ovalle, Daniel↗

DOE OSTI2023

Vaccination with mycobacterial lipid loaded nanoparticle leads to lipid antigen persistence and memory differentiation of antigen-specific T cells

Mycobacterium tuberculosis (Mtb) infection elicits both protein and lipid antigen-specific T cell responses. However, the incorporation of lipid antigens into subunit vaccine strategies and formulations has been underexplored, and the characteristics of vaccine-induced Mtb lipid-specific memory T cells have remained elusive. Mycolic acid (MA), a major lipid component of the Mtb cell wall, is presented by human CD1b molecules to unconventional T cell subsets. These MA-specific CD1b-restricted T cells have been detected in the blood and disease sites of Mtb-infected individuals, suggesting that MA is a promising lipid antigen for incorporation into multicomponent subunit vaccines. In this study, we utilized the enhanced stability of bicontinuous nanospheres (BCN) to efficiently encapsulate MA for in vivo delivery to MA-specific T cells, both alone and in combination with an immunodominant Mtb protein antigen (Ag85B). Pulmonary administration of MA-loaded BCN (MA-BCN) elicited MA-specific T cell responses in humanized CD1 transgenic mice. Simultaneous delivery of MA and Ag85B within BCN activated both MA- and Ag85B-specific T cells. Notably, pulmonary vaccination with MA-Ag85B-BCN resulted in the persistence of MA, but not Ag85B, within alveolar macrophages in the lung. Vaccination of MA-BCN through intravenous or subcutaneous route, or with attenuated Mtb likewise reproduced MA persistence. Moreover, MA-specific T cells in MA-BCN-vaccinated mice differentiated into a T follicular helper-like phenotype. Overall, the BCN platform allows for the dual encapsulation and in vivo activation of lipid and protein antigen-specific T cells and leads to persistent lipid depots that could offer long-lasting immune responses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Phase Formation in Nuclear Fallout

An understanding of the physical and chemical process occurring in a nuclear explosion enables predictions of the effects of nuclear weapons, including characteristics of radioactive fallout resulting from the explosion. Near-surface nuclear explosions are of particular interest due to the potential for significant amounts of environmental material to interact with and alter the physical and chemical behavior of the fireball. Such interactions have the potential to affect the distribution of radioactive species in the fireball and subsequently become incorporated into fallout through a process known as radiochemical fractionation. Studying variations in fallout formed in different historical testing environments allows us to understand the influence of local environments on fallout formation processes. In particular, constraining variations in thermal evolution and redox conditions during the evolution of the fireball can be useful to understanding how sensitive fallout radiochemical fractionation may be to the local explosion environment. However, untangling these conditions in complex, multicomponent fallout is a challenge. Here we present one method of constraining and interpreting fallout formation conditions by relating computationally derived phase stability predictions to observations in historic fallout. Development of such approaches will help improve physics-based models of fallout formation and radiochemical fractionation in complex, near surface nuclear detonations.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Spatially-Resolved Characterization Techniques and Their Implications for Nuclear Debris Formation.

Debris from nuclear tests has a complex formation process and can inform multiple fields of study such as geology, atmospheric science, shock physics, and chemistry under extreme conditions. Macroscopic nuclear debris forms as the result of fireball interaction with surrounding materials (e.g., structural and environmental component), which rapidly undergo melting and vaporization followed by condensation, convective/diffusive mixing, and solidification over the course of seconds. In atmospheric events, some of this material may disperse over long distances, but much of the material is deposited close-in to ground zero, often in multicomponent, partially (or entirely) amorphous debris formations. During the U.S. nuclear testing program, this material was collected and analyzed, particularly for radionuclide composition. Fallout formation models were developed from nuclear test data based on such radionuclide compositional analyses. These models were not just used to understand fallout dispersion (i.e. the spread of radioactivity over geographical regions) but also guide radiochemical interpretations of historical nuclear tests.6 Developments in analytical techniques over the past several decades have made it possible to make new analyses on historical debris, some of which may be more than a half-century old. For example, inductively-coupled plasma mass spectrometry (ICP-MS) has been used to look at the trace elements in glassy fallout material from aboveground nuclear tests (including the Trinity test). Advanced analyses using X-ray absorption allowed for the measurement of oxidation state of fallout constituents, including actinides. The ability to measure trace actinides and their subsequent oxidation state is important to understanding how the surrounding environment may have influenced the resultant fallout composition and may have implications for why certain fractionation trends have been observed. Here we present data from historic nuclear test debris illustrating the power of spatially resolved methods to connect interaction of the near-field environment with the explosion and provide new insights into nuclear debris formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Carbon dioxide capture system and spectroscopic evaluation thereof

A chemical processing system for removing carbon dioxide from a gas mixture using a multicomponent amine-based scrubbing solution includes a spectroscopic evaluation system with a liquid contact probe for spectroscopic investigation, an energy source connected with the liquid contact probe to provide the spectroscopic stimulation energy to the probe, a spectrometer connected with the liquid contact probe to detect the spectroscopic response energy to the probe and to output spectral data corresponding to the spectroscopic response energy, and a machine learning spectral data analyzer connected to the spectrometer for evaluation of the spectral data to determine a concentration value for each of water, amine component and captured carbon dioxide in the scrubbing solution, the machine learning spectral data analyzer being trained for each such component over a corresponding trained concentration range, and optionally over a trained temperature range to provide a temperature-compensated concentration value.

Panaccione, Charles↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency).

Per- and poly-fluorinated alkyl substances (PFAS),↗

DOE OSTI2023

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

The multicomponent nature of and variability of biomass makes chemical upgrading via a single process stream to a single end use infeasible for most feedstocks. A more promising approach is to identify upgrading strategies (encompassing bioprocessing, catalysis, and separations) which valorize varied biomass fractions to distinct products to which each is best suited. A robust procedure to carry out this goal should incorporate comparison of various upgrading procedures as well as suitability of products to a slate of identified end uses. We applied this strategy to one biomass-derived feedstock, volatile fatty acids (VFAs) derived from wet waste arrested anaerobic digestion, by developing a computer program, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE) which evaluates VFA catalytic upgrading to liquid transportation fuels. VULTURE considers multiple separations, catalysis (ketonization, hydrogenation), and fuel application options, generating hundreds of candidate scenarios for a given VFA stream, then selects several promising strategies that optimize bio-content of products with properties best suited for target fuel types. We find that VFAs are upgraded most efficiently when separate light alcohol (C3-6) and heavy hydrocarbon or alcohol (C7-13) fractions are targeted to create gasoline and heavy-duty (diesel or jet) fuels or fuel blends. Surrogate property testing of VULTURE-recommended fuels reveals that most predictive models employed are robust, while rigorous process simulation shows that the simple unit operation assumptions used in VULTURE are largely accurate, especially for heavy-duty fuel synthesis. Techno-economic and life-cycle analyses show that VFA-derived fuels are profitable and have dramatically (=57%) lower carbon intensities than fossil analogs.

BIOMASS FUELS↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach - 20080

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Tracking Radioactive Isotopes in HVAC and Application for Hot Cell Analyses - 20155

In recent years, the surge in the number of isotope production facilities under design has increased the need to analyze isotope migration in facility Heating, Ventilation, and Air Conditioning (HVAC) during normal operations and accident scenarios. These facilities are used to produce isotopes for medical, security, and industrial applications and as such are subject to license and regulatory requirements 10CFR20, 10CFR30, 10CFR50, and 10CFR70. GOTHIC, a general-purpose thermal-hydraulics software package, includes the ability to model isotopic tracers, radioactive decay and isotope migration as well as HEPA and charcoal filters for isotope retention. A GOTHIC model was developed by Zachry Nuclear Engineering (ZNE) to examine the effect of negative room pressure, HEPA filtration, and HVAC fluctuations on radiation areas, hot cells and gloveboxes. Radioactive tracers were used to simulate the concentration of spills within contaminated areas, track the migration of isotopes of interest, and determine the isotopic retention and buildup on facility HEPA filters. A variety of isotopes with concern to dose (e.g., Kr-85, Sr-90, I-131, etc.), including their decay and progeny, are included in the analysis. Negative pressures are maintained in the regions of interest by a representative central HVAC system equipped with a volumetric fan that exhausts to the environment after a series of isolation valves and HEPA filters. GOTHIC is an industry trusted tool for providing engineering solutions for a variety of applications, including fission product tracking, aerosol and particulate transport and ventilation assessments. The software provides an integrated analysis environment that includes a graphical user interface (GUI) for constructing analysis models, a numerical solver that includes parallel processing capabilities and a post-processor for evaluating simulation results. It solves the conservation equations for mass, momentum and energy for multicomponent, multi-phase flow in lumped parameter and multi-dimensional geometries (1, 2, or full 3D), including the effects of turbulence, diffusion and buoyancy. It has been developed and maintained under a Quality Assurance program in compliance with the requirements of 10CFR50 Appendix B and applicable portions of ASME NQA-1 since 1995. GOTHIC has been used for assessing both forced and natural convection conditions for a wide range of applications, including: - Tracking concentration of hazardous gases and chemicals for habitability and safety assessments - Determining ventilation and filtration requirements and optimizing location and arrangement of these systems - Room heat-up, including diverse and Flexible coping strategies for Extended Loss of AC Power (FLEX/ELAP) - Equipment Qualification (EQ) A distinctive feature of GOTHIC is the ability to track many different fields/substances in a simulation, including user defined tracer elements, in the liquid, vapor and droplet fields as well as surfaces and filters. This capability allows GOTHIC to model fission product transport and release or the removal of particulates or harmful toxins from exhaust gases using a spray scrubber or other types of filtration systems. GOTHIC also includes models for engineered equipment, such as fans, filters, charcoal filters, dryers/demisters, dampers, etc. The aerosols and other filtered material are removed or accumulated in these components. The range of aerosol and radiological applications that GOTHIC has been used for includes: - Source Term: Primary Coolant (Equilibrium) Activity; Non-Water Coolant Source/Leakage. - Conditions for Iodine Re-evolution: Sump/Suppression Pool Conditions and pH; - RWST Conditions and pH. - Isotope Removal Mechanisms: Containment Sprayed and Unsprayed Region Mixing; Charcoal Filter Heating due to Iodine decay. - Radionuclide Transport and Decay: Post-LOCA Release in containment; Transport between connected Compartments and vent systems; Groundwater transport of radionuclides. - Non-Newtonian Fluid modeling for sludge, waste tanks, etc. ADAMS ML071581053 (titled 'Best Practice Guidelines for the use of CFD in Nuclear Reactor Safety Applications') poses guidelines for applying single phase CFD codes in nuclear reactor safety problems and GOTHIC is listed as a 'tool for 3D flows' and 'dispersal and deposition of radionuclides.' The Nuclear Quality Assurance (NQA) pedigree of GOTHIC is an important aspect for applications in the nuclear industry. The fundamental tracer models (convective transport, molecular and turbulent diffusion, removal mechanisms, etc.) have been verified using analytical solutions and validated against applicable separate effects tests. Also, GOTHIC has been benchmarked to many integrated effects tests, including Phebus FP (Fission Product). GOTHIC gives good agreement for the buildup and decay of fission products in Phebus Test 3. The model developed by ZNE demonstrates GOTHIC's applicability and acceptability for use in analyzing the migration and retention of radioactive isotopes and their progeny in normal operation and accident scenario analyses for isotope production facilities, hot cells, and gloveboxes. The tracer activities calculated by GOTHIC can then be used in downstream radiation transport and shielding codes like RADTRAD-NAI{sup C}, MCNP{sup R}, and MicroShield{sup R} to determine on-site and of-site doses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Efficient Mesofluidic Separation of Large Particles in nuclear slurries - 20408

Efficient and effective particle separation is essential to cleanup of many nuclear wastes. For example, particle separation may be used to accelerate settle-decant bottlenecks that limit the throughput necessary to achieve the U.S. Department of Energy waste cleanup mission in a timely manner. Particle separation may be used to protect downstream processing equipment from pulses of solids that may be encountered near (within a few feet of) liquid-solid interfaces during waste transfer operations, thereby preventing work stoppages. Effective particle separation may permit efficient sludge washing. These are only a few of the ways in which particle separation is important. Yet, efficient and effective particle separation in nuclear processing environments remains challenging. For example, settle-decant operations permit larger and heavier particles to settle, leaving smaller, lighter particles suspended. However, settle-decant operations are slow, convection currents may resuspend solids, hindered settling of multicomponent slurries remains incompletely understood, and predictive models for settling in graduated cylinders fail to match limited observations of settling in large waste tanks. Additionally, pumping operations can cause turbulent resuspension of particles when the pump intake is close to the settled solids layer. Filtration techniques that use membranes or partially permeable barriers retain larger particles, permitting smaller particles to permeate. However, filters, including dead-end filtration, are prone to clogging and caking, operate at elevated pressures due to minimal void volume, and increase in pressure during operations, which require more control systems. Therefore, the need for high throughput particle separation techniques that operate with modest pressure drops persists. A novel mesofluidic separator presents the opportunity to effectively and efficiently accelerate the waste cleanup mission. The separator separates large particles from process streams across a broad range of particle sizes and has no moving parts or media to replace, regenerate, or clean. This separator design has an unusually large void volume, permitting operation at much higher flow rates (and lower pressures) than traditional filtration (e.g., dead-end filtration). Industrial-scale flow rates have been demonstrated. In performance testing, mesofluidic separators operate at might flow rates (>90 gpm (0.0006 m{sup 3}/s) in piping 3 inches (0.08 m) in diameter; Re>10{sup 5}), with modest pressure drop (∼25 psi (170 KPa) in testing). In complex, aggregating waste simulants, the separator loses <25% of flow projected over months without back pulsing or chemical cleaning. Mesofluidic separation presents infrastructure advantages, reduces risk, and provides mission impact. Infrastructure advantages include implementation within existing transfer systems, plug and play without facility modifications to safety systems, and operation at low pressures. The separator is flexible in location, may be positioned within or outside of waste tanks, and may replace or augment dead-end and cross-flow filters. Mesofluidic separation reduces risk by minimizing waste (no media to replace or regenerate and no cleaning chemicals to handle and dispose) and by minimizing filter change-out consistent with as low as reasonably achievable (ALARA) exposure to workers. The potential mission impact of these separators is substantial. The separator has potential to support, simplify, and accelerate in-farm transfers and waste feed delivery. Furthermore, the separator can free up settling and holding tanks in the Direct Feed Low-Activity Waste (DFLAW) mission by reducing or eliminating post-transfer settling and wait times with the potential to free up 1 Mgal (4000 m{sup 3}) of double-shell tank space. This paper quantitatively compares mesofluidic separation to dead-end filtration, discusses scale-up results, and considers the separator's potential to efficiently and effectively reduce the long-term environmental ability of particulate-rich nuclear wastes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multiphysics Time-Integration for Turbulent Combustion at the Exascale

Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.

chemistry↗

DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Self-Driving Microscopy for AI/ML-Enabled Physics Discovery and Materials Optimization

Materials are the bedrock of economy and foundation for all real-world technologies. The viability of space travel, grid energy storage, solar to fuels conversion, methane removal, and photovoltaic energy solutions hinge on the discovery and optimization of novel materials and rapid scaling toward manufacturing. The last 20 years have seen an exponential growth in the theoretical predictive capability for crystalline materials and small molecules. However, it is only in the last five years that we have seen the rapid expansion of high-throughput synthesis enabled by laboratory robotics and microfluidics, as well as a resurgence of combinatorial synthesis (Abolhasani and Kumacheva 2023; Epps and Abolhasani 2021; Jiang et al. 2022; Rajan 2008; Soldatov et al. 2021; Szymanski et al. 2023). Combinatorial synthesis, microfluidics, and ultimately dip-pen megalibraries have demonstrated the ability to “write” multicomponent nanomaterials at high throughput scale, generating millions of material examples in the 3D, 4D, and 5D composition spaces (Chen et al. 2016, 2019; Jibril et al. 2022).

36 MATERIALS SCIENCE↗