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DOE OSTI2024

Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.

Mohammed, Ahmed↗

DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗

DOE OSTI2025

Advances in Modeling Capabilities for Critical Mineral Separation Technologies: A PrOMMiS Overview

This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

critical minerals and materials↗

DOE OSTI2021

Information Entropy as Quantifier of Potential Predictability in the Tropical Indo-Pacific Basin

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Preparing Lamellae from Vitreous Biological Samples using a Dual-Beam Scanning Electron Microscope for Cryo-Electron Tomography

Presented here is a protocol for preparing cryo-lamellae from plunge-frozen grids of Plasmodium falciparum-infected human erythrocytes, which could easily be adapted for other biological samples. The basic principles for preparing samples, milling, and viewing lamellae are common to all instruments and the protocol can be followed as a general guide to on-grid cryo-lamella preparation for cryo-electron microscopy (cryoEM) and cryo-electron tomography (cryoET). Electron microscopy grids supporting the cells are plunge-frozen into liquid nitrogen-cooled liquid ethane using a manual or automated plunge freezer, then screened on a light microscope equipped with a cryo-stage. Frozen grids are transferred into a cryo-scanning electron microscope equipped with a focused ion beam (cryoFIB-SEM). Grids are routinely sputter coated prior to milling, which aids dispersal of charge build-up during milling. Alternatively, an e-beam rotary coater can be used to apply a layer of carbon-platinum to the grids, the exact thickness of which can be more precisely controlled. Once inside the cryoFIB-SEM an additional coating of an organoplatinum compound is applied to the surface of the grid via a gas injection system (GIS). This layer protects the front edge of the lamella as it is milled, the integrity of which is critical for achieving uniformly thin lamellae. Regions of interest are identified via SEM and milling is carried out in a step-wise fashion, reducing the current of the ion beam as the lamella reaches electron transparency, in order to avoid excessive heat generation. A grid with multiple lamellae is then transferred to a transmission electron microscope (TEM) under cryogenic conditions for tilt-series acquisition. A robust and contamination-free workflow for lamella preparation is an essential step for downstream techniques, including cellular cryoEM, cryoET, and sub-tomogram averaging. Development of these techniques, especially for lift-out and milling of high-pressure frozen samples, is of high-priority in the field.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Examples of State and Utility Actions on Proactive Planning and Investments

As states across the U.S. confront rising electricity demand, clean energy deployment, grid modernization imperatives, and the integration of large new loads, some regulators and utilities are shifting away from reactive, “just-in-time” investment approaches toward more proactive planning and investment frameworks. This report compiles examples of jurisdictional and utility actions that reshape planning processes, cost recovery mechanisms, and performance oversight to anticipate—rather than simply respond to—future grid needs. Several themes emerge from state actions examined in this report. First, legislatures and commissions are increasingly directing utilities to proactively upgrade their distribution and transmission systems, reflecting a shift toward a forward-looking system that aligns planning with state policy goals. Second, states are establishing long-term, iterative planning frameworks that often feature multi-year horizons, biannual or annual compliance reporting, structured opportunities for stakeholder engagement, and emphasis on collaboration among utilities, regulators, and stakeholders. Third, states are actively investigating innovative cost recovery mechanisms designed to support accelerated electrification and grid modernization, while balancing consumer advocates’ concerns regarding the ratepayer financial risks of premature investments. Fourth, performance metrics and reporting requirements are being developed to ensure transparency and accountability for proactive investments. Fifth, methodological improvements in planning—such as aligning load forecasting assumptions, incorporating sensitivities, and considering load management potential across building, vehicles, storage, and demand response—are recurring areas of stakeholder focus across jurisdictions. Overall, these developments signify a growing recognition among state regulators, utilities, and stakeholders that proactive planning—supported by clear definitions, consistent and transparent methodologies, robust performance metrics, and innovative cost recovery mechanisms—is a tool that can be used to address the scale and urgency of contemporary grid needs.

electricity market↗

DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

LA100 Equity Strategies. Chapter 4: Lessons Learned and Options for Community Engagement in Los Angeles

The LA100 Equity Strategies project synthesizes community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. Grounded in the analysis of past and ongoing energy inequities and engagement with underserved communities, the project presents community-guided strategies that aim to operationalize recognition and procedural justice. Building on the community-identified problems and solutions, and the analysis of the 11 strategies described in Chapter 3, this chapter continues to focus on the solution space through the lens of recognition and procedural justice. It centers the role of community engagement in energy utility planning and project development with a specific focus on how the Los Angeles Department of Water and Power (LADWP) can engage and work equitably with Los Angeles communities to cocreate a clean and just energy future for LA. LA100 Equity Strategies is rooted in the crucial role community engagement plays in restructuring the energy systems of cities, states, and nations. Scholarship on wind, solar, and other transitional energy technologies and projects has documented that such engagement is commonly used as a top-down mechanism for adapting social practices to fit new technological innovations. Yet, understanding how the clean energy transition - with related changes in technologies, infrastructures, practices, and costs - will fit equitably into the existing socio-political context is a challenge that requires substantive collaboration with local communities. Any form of community engagement opens up government officials and utilities to opposition from their public. Meaningful engagement methods turn such dissent into a strength, embracing critical feedback - particularly from communities historically excluded from decision-making - as contributing to more grounded design and effective implementation. Leveraging this collaborative model to further rectify past and ongoing inequities in the social, cultural, and institutional scaffolding of LA, this chapter presents options and methods to support LADWP in launching a just and equitable clean energy transition. We approach community engagement as a critical process linking recognition, procedural, and distributional justice, outlining how LADWP could learn from past engagement, coordinate such knowledge organization-wide, and use engagement as a key tool for achieving energy justice and equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

LA100 Equity Strategies. Chapter 5: Low-Income Energy Bill Equity and Affordability

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Rare Earth Permanent Magnets: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. This report focuses on the supply chain for rare earth permanent magnets, specifically sintered neodymiumiron-boron (NdFeB) magnets, used in clean energy technologies. Sintered NdFeB magnets are the strongest magnets commercially available and provide a host of benefits to wide-ranging applications in consumer and industrial electronics, especially in advanced motor and drive systems. Within the Energy Sector Industrial Base, and clean energy in particular, NdFeB magnets are key intermediate components of permanent magnet synchronous (direct drive) generators in wind turbines (especially for offshore turbines) and electric synchronous traction motors for propulsion systems in battery and hybrid electric vehicles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

An accurate and efficient fragmentation approach via the generalized many-body expansion for density matrices

With relevant chemical space growing larger and larger by the day, the ability to extend computational tractability over that larger space is of paramount importance in virtually all fields of science. The solution we aim to provide here for this issue is in the form of the generalized many-body expansion for building density matrices (GMBE-DM) based on the set-theoretical derivation with overlapping fragments, through which the energy can be obtained by a single Fock build. In combination with the purification scheme and the truncation at the one-body level, the DM-based GMBE(1)-DM-P approach shows both highly accurate absolute and relative energies for medium-to-large size water clusters with about an order of magnitude better than the corresponding energy-based GMBE(1) scheme. Simultaneously, GMBE(1)-DM-P is about an order of magnitude faster than the previously proposed MBE-DM scheme [F. Ballesteros and K. U. Lao, J. Chem. Theory Comput. 18, 179 (2022)] and is even faster than a supersystem calculation without significant parallelization to rescue the fragmentation method. For even more challenging systems including ion–water and ion–pair clusters, GMBE(1)-DM-P also performs about 3 and 30 times better than the energy-based GMBE(1) approach, respectively. In addition, this work provides the first overlapping fragmentation algorithm with a robust and effective binning scheme implemented internally in a popular quantum chemistry software package. Thus, GMBE(1)-DM-P opens a new door to accurately and efficiently describe noncovalent clusters using quantum mechanics.

Chemistry↗

DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗

DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗

DOE OSTI2022

Initiating a Roadmap for Solar Fuels R&D: Imagining Beyond Thermochemical Cycles

Sandia National Laboratories in collaboration with the National Renewable Energy Laboratory outline a framework for developing a solar fuels roadmap based on novel concepts for hybridizing gas-splitting thermochemical cycle s with high-temperature electro chemical steps. We call this concept SoHyTEC, a Solar Hybrid Thermochemical-Electrochemical Cycle. The strategy focuses on transforming purely thermochemical cycles that split water (H 2 O) and carbon dioxide (CO 2 ) to produce hydrogen (H 2 ) and carbon monoxide (CO) , respectively, the fundamental chemical building blocks for diverse fuels and chemicals , by substituting thermochemical reactions with high-temperature electrochemical steps. By invoking high-temperature electrochemistry, the energy required to complete the gas-splitting cycle is divided into a thermal component (process temperature) and an electrical component (applied voltage). These components, sourced from solar energy, are independently variable knobs to maximize overall process efficiency. Furthermore, a small applied voltage can reduce cycle process temperature by hundreds of degrees , opening the door to cost-effective solar concentrators and practical receiver/reactor de signs. Using the SoHyTEC concept as a backdrop, we outline a framework that advocates developing methods for automating information gathering, critically evaluating thermochemical cycles for adapting into SoHyTEC, establishing requirements based on thermodynamic analysis, and developing a model-based approach to benchmarking a SoHyTEC system against a baseline concentrating solar thermal integrated electrolysis plant. We feel these framework elements are a necessary precursor to creating a robust and adaptive technology development roadmap for producing solar fuels using SoHyTEC. In one example, we introduce high-temperature electrochemistry as a method to manipulate a fully stoichiometric two-step metal oxide cycle that circumvents costly separation processes and ultra-high cycle temperatures. We also identify and group water-splitting chemistries that are conceptually amenable to hybridization.

14 SOLAR ENERGY↗

DOE OSTI2022

Mechanical Characterization of Stacked Single-Crystal of Polyethylene and Monolayer MoSe 2

Polymer single crystal (SC) is a key building block of semicrystalline polymers. However, direct experimental measurement of freestanding mono-lamella polymer SC has not been demonstrated. This is, in large part, due to the difficulties associated with manipulating freestanding individual mono-lamella SC because of its extremely low rigidity and low robustness. Here, we demonstrate a new strategy to successfully suspend and test a polymer SC by using a 2D material as backing. In particular, mono-lamella polyethylene (PE) SC is stacked on monolayer MoSe 2 , and the hybrid stacks can be suspended over microholes. Nanoindentation is used to probe the suspended PE-SC/MoSe2 stacks and MoSe 2 monolayers. The results suggest the first experimentally-measured in-plane moduli of PE-SC and 2D MoSe 2 as 32 ± 3 and 237 ± 15 GPa, respectively. Such a stacked unit represents an ultrathin structure of polymer-ceramic laminate and the idea can be applied to other laminated composite systems. Therefore, this research will pave the way to accurately measure the mechanical properties of polymer SCs and their composites, as well as provide a key insight on designing composite structures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Weapon Material Program: FY24 Annual Report

The Weapon Material Program (WMP) framework was started in July of Fiscal Year (FY) 2022, along with other Mission Support organizations. The first year was about building the organization with the right people, identifying needs, and developing a vision for expectations. It was also a year to move forward with new equipment and technology for weapon material processes. With numerous improvements, WMP is building world-class systems for production, warehousing, and analytical testing. FY 2023 was a year of development. The WMP was a new organization defining its identity, its focus, and its mission. At the end of FY 2023, WMP evaluated its current position and its immediate needs along with its future goals. The entire organization set out on this FY 2024 journey to success. The lack of funding, staffing, and visibility dramatically impacted weapon material operations, testing, and qualifications. This neglect required numerous areas to be addressed and improvement plans to be developed and implemented. WMP processes, procedures, and overall business operations were dramatically in need of updating, revision, and formal documentation. This included all areas: technology, equipment, facility modifications/upgrades, and testing improvements. To ensure organizational improvement and forward momentum in FY 2024, WMP executed a multi-faceted strategic vision. Action items included the following: • Filling open positions with capable personnel who would contribute to a more robust organization • Establishing more formal operations and problem-solving techniques • Improving departmental procedures to align with expanding scope • Improving training • Establishing collaboration meetings with other departments/organizations • Focusing on the importance of identifying and mitigating concerns with At-Risk Materials (@RM) • Leading modernization eff orts for Blending and Packaging (B&P) We are on the verge of a transformational improvement in all of our processes in support of the mission. The following area achievements outlined in this report reflect the hard work toward achieving the goal of “being the material Subject Matter Experts in the nuclear enterprise”.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

High-order partitioned spectral deferred correction solvers for multiphysics problems

We present an arbitrarily high-order, conditionally stable, partitioned spectral deferred correction (SDC) method for solving multiphysics problems using a sequence of pre-existing single-physics solvers. This method extends the work in [1], [2], which used implicit-explicit Runge-Kutta methods (IMEX) to build high-order, partitioned multiphysics solvers. We consider a generic multiphysics problem modeled as a system of coupled ordinary differential equations (ODEs), coupled through coupling terms that can depend on the state of each subsystem; therefore the method applies to both a semi-discretized system of partial differential equations (PDEs) or problems naturally modeled as coupled systems of ODEs. The sufficient conditions to build arbitrarily high-order partitioned SDC schemes are derived. Based on these conditions, various of partitioned SDC schemes are designed. The stability of the first-order partitioned SDC scheme is analyzed in detail on a coupled, linear model problem. We show that the scheme is conditionally stable, and under conditions on the coupling strength, the scheme can be unconditionally stable. We demonstrate the performance of the proposed partitioned solvers on several classes of multiphysics problems with moderate coupling strength. They include a stiff linear system of ODEs, advection-diffusion-reaction systems, and fluid-structure interaction problems with both incompressible and compressible flows, where we verify the design order of the SDC schemes and study various stability properties. We also directly compare the accuracy, stability, and cost of the proposed partitioned SDC solver with the partitioned IMEX method in [1], [2] on this suite of test problems. The results suggest that the high-order partitioned SDC solvers are more robust than the partitioned IMEX solvers for the numerical examples considered in this work, while the IMEX methods require fewer implicit solves.

97 MATHEMATICS AND COMPUTING↗