National Technical Information Service

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

United States electricity markets, planning mechanisms, and operational procedures are currently evolving in concert with three key trends. First, a range of new resources—solar, wind, energy storage, hybrid co-located storage, and distributed energy—are coming online and require new solutions to ensure they are efficiently integrated into existing systems. Second, consumers now face more opportunities to participate in markets by providing demand response and engaging in two-way interactions with the grid. Third, there is an increasing need for enhanced coordination between generation and transmission planning as well as across transmission and distribution systems.

This report is prepared for the U. S. Department of Energy (DOE) Office of Legacy Management (DOE-LM) by the Worker Safety & Environment (WSE) Division at Argonne National Laboratory (Argonne). Beginning in 2024, Argonne drafted a combined report containing the annual inspection results for the current year and the annual surveillance data/discussion from the previous year. The annual inspection results will be contained within an appendix at the end of the surveillance report. The results of the environmental monitoring program at Site A and Plot M and an assessment of the impact of the site on the environment and the public are presented in this publication as well as findings and assigned corrective actions as a result of the Site A/Plot M annual inspection. Funding to support this program was provided by DOE-LM.

Computational Fluid Dynamics combined with a Discrete Element Method is one of the computational methods that can be used to model multiphase flows. In this method various phases, gas and liquid or solid, are present in the same computational domain. The local averaged Navier–Stokes equations determine the flow of the continuous phase fluid and are solved using the traditional CFD finite volume approach. DEM is based on a Lagrangian formulation, which solves the equations of motion, expressed in ordinary differential equations, for representative particles as they move in space and time. The interactions between the continuous fluid phase and discrete solid phase are modeled with the use of Newton’s laws of motion via drag force. The particles interact with each other and with the boundaries of the fluid continuum, and the resulting contact forces are included in the equations of motion. The properties of solid particles and boundaries are treated as elastic bodies, with specified density, elastic modulus, and Poisson’s ratio. Particle shapes may vary from single spherical particles to more complex-shaped composite particles. The particles may be introduced into the domain by random or structured injection at a point, surface, or volume, depending on the application. More details on the formulation can be found in the Simcenter STAR-CCM+ User’s Manual and OpenFOAM website.

Argonne performed thermodynamic modeling to predict eutectic compositions in the LiF-NaF-UF4 and LiF-NaF ThF4 systems. Three compositions each of LiF-NaF-UF4 and LiF-NaF-ThF4 molten salts were produced and the thermal properties between 400 and 600 °C were measured. One composition from each salt system was selected for measurements of heat capacity and thermal diffusivity across temperature ranges suggested by FEI experts as beneficial to industry.

An effort has been dedicated to developing a reference model for multi-physics coupled simulation of the pebble bed high-temperature gas-cooled reactor (PB-HTGR) with SAM and Griffin computer codes for safety analysis and licensing purpose.The reference problem of a prototypical reactor design serves as the foundation for the U.S. NRC (Nuclear Regulatory Commission) to verify the adequacy of computer codes and evaluation models for specific reactor types.In this work, a SAM model of the HTR-PM reactor has been developed based on publicly available design information and the multi-dimensional Pronghorn model developed by Idaho National Laboratory.The SAM HTR-PM model includes a multi-dimensional core region and 0-D/1-D fluid components.Additionally, a simplified air RCCS loop is modeled for decay heat removal.The Griffin model is based on a recent work by Idaho National Laboratory.The coupling between the models is achieved through the MOOSE MultiApp system.Both steady-state and transient scenarios were simulated to demonstrate the coupled model's capability for multi-physics simulations.

The integration of Griffin into Workbench, by way of the PyGriffin code package, was initiated by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY-2022.PyGriffin was developed as a Python wrapper to Griffin to streamline the complex workflow involving mesh, cross section, and Griffin input generation, code execution, and results postprocessing, by improving user experience with high fidelity neutronics analysis through the Workbench GUI interface.PyGriffin can be used as a standalone application or through the PyARC code suite.FY-2023 saw the continued development of PyGriffin to expand its capabilities including advanced post-processing of simulation results and initial development of the Monte Carlo (MC) cross section generation workflow with the Shift MC code.PyGriffin was also approved for open-source software status in FY-2023, initiating plans to move the code repository into an open location.In addition to PyGriffin development, there were many improvements and developments made in PyARC leading to several software releases (latest version is v2.3.0), but the focus of this report is PyGriffin.

The FOEHN critical experiment, which has been carried out at Cadarache (France) in the reactor EOLE in the early 70s, was designed to verify RHF design analyses. This study focuses on the impact of the FOEHN experiment uncertainties on the calculation of the energy deposition. The latter has been obtained by the Monte Carlo codes Serpent and MCNP. In the calculation of the energy deposition, the first code relies on KERMA factors whereas the latter code relies on Q-values. The two Monte Carlo codes share the same geometry and material specifications. In addition to the design and experiment parameters uncertainties, nuclear data also impacts the calculation of the energy deposition. In this study, the MCNP simulations use three different nuclear data library sets: ENDF/B versions VII.0, VII.1, and VIII.0. Finally, the ksens card of MCNP, using perturbation theory, has been used to identify the cross sections and isotopes with largest impact on the effective multiplication factor. By showing that the use of different software and computational methodologies and design parameters uncertainties do not significantly impact the core power distribution, this work demonstrate the appropriateness of the tools and methods employed for the conversion of involute-plate reactors.

One of the challenges of a high-renewable, low-carbon system is that grid operators will no longer be able to count on fossil-fired generation should wind miss its forecast or modeling errors create shortage issues, and this change has operational and reliability implications that affect variable renewable energy integration. Hydropower can help in that it can provide many of the services that have been traditionally provided by fossil generation. However, getting the most from hydropower can be challenging in that its capabilities vary with time and are affected by other water users' needs, two complexities that are not well represented in most of today's production cost models (PCMs). This work is an early step in addressing these gaps and making the most of hydropower's capabilities.

Argonne National Laboratory (the Contractor) and TerraPower, LLC, (the Participant), entered into a Cooperative Research and Development Agreement (CRADA) to extend the Plant Dynamics Code (PDC) to model the Pascal reactor plant. PDC is the system-level computer code developed at Argonne, primarily for design and transient analysis of supercritical carbon dioxide (sCO2) energy conversion systems for various power plants. Pascal is a supercritical carbon dioxide (sCO2)-cooled direct-cycle nuclear power reactor under development by TerraPower. New features were added to the code to model components and systems specific to the Pascal plant and to make the code readily available as a tool for design, control, and safety analysis for this and other direct-cycle power plants. TerraPower provided design information of the Pascal reactor and the requirements for the development of the new PDC features. Argonne modified the PDC capabilities to introduce the new features.