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At least 469 records · Page 26

Improved Modeling of Waste Isolation Pilot Plant Disposal Room Porosity

Disposal rooms at the Waste Isolation Pilot Plant (WIPP) contain waste and gas, and their porosity evolves over time. This report presents several improvements to the disposal room porosity model and presents new porosity predictions for use in future WIPP Performance Assessment activities. The improvements pertain to three sub-models: the geomechanical model, the waste compaction model, and the gas pressurization model. The impacts of each major improvement were quantified and the new porosity predictions were shown to be both mesh and domain size converged. Also, sensitivity studies on the disposal room horizon, clay seam friction coefficients, and homogenized waste representation were performed to support assumptions in the disposal room porosity model. To compare the legacy and new porosity predictions, the simulation results were plotted as a response surface, where gas pressure and time are inputs and porosity is the output. The new porosity response surface is insensitive to pressures beneath lithostatic pressure and highly sensitive to pressures above lithostatic pressure. The legacy porosity response surface, on the other hand, has moderate porosity gradients over all pressures. The new porosity response surface has a stronger scientific foundation than the legacy surface and may now be used for Compliance Decision Analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Arroyo Seco Improvement Project: Annual Report 2022

The Arroyo Seco Improvement Program (ASIP) is intended to provide active channel improvements and stream zone management activities that will reduce current flood and erosion risk while providing additional and improved habitat for critical species that may use the Arroyo Seco at the Sandia National Laboratories, California (SNL/CA). SNL/CA facility is operated by the National Technology and Engineering Solutions of Sandia, LLC (NTESS) under a contract with the U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA). The DOE/ NNSA’s Sandia Field Office (SFO) oversees the operations of the site.

54 ENVIRONMENTAL SCIENCES↗

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Improving Efficiency of Off-Road Vehicles by Novel Integration of Electric Machines and Advanced Combustion Engines

Modern off-road equipment will increasingly rely on electrified implements that will deliver precision control with a smaller footprint than hydraulics. The primary energy converter, however, will be an onboard reciprocating internal combustion engine because of the power density of hydrocarbon fuels in comparison to electrical energy storage and the remote locations where much of this equipment is deployed. Adding energy storage and electric machines creates opportunities to improve efficiency while reducing emissions. This program investigated approaches to take advantage of the extra flexibility that an enhanced electrical system to enable high-efficiency, low-emissions combustion technologies. The current program evaluated hybridization of both the torque application and air-handling systems to maximize efficiency while minimizing cost. This program designed, analyzed and tested a hybrid off-road vehicle consisting of a series electric powertrain with energy storage, an electrified air system, and a 33% downsized diesel engine. Detailed comparisons were made between the base powertrain and the developed powertrain using powertrain simulations, engine testing, and vehicle testing. The key results of the study are: • The resulting vehicle reduces fuel consumption by 5% to 15% at equal productivity. The primary improvements were due to recovery of regenerative braking losses for cycles where large transients are encountered. • The electrified air system was an enabler to allow engine downsizing. This was most important for duty-cycles where the engine primarily operated at moderate loads and had few low speed high torque conditions. • Engine level improvements showed re-optimization of the powertrain is possible when electrified air handing is available. Increased exhaust gas recirculation and advanced injection timing allowed up to a 15% reduction in brake specific fuel consumption at equal NOx and transient response. Compared to the larger engine, the downsized engine achieves up to an 18% reduction in brake specific fuel consumption over the non-road transient cycle. • The life cycle analysis and total cost of ownership study showed that the hybrid powertrain has the potential to reduce 5-year CO2 and total cost of ownership by ~6% over the baseline vehicle. The results also indicated that a pure battery electric vehicle is not feasible in this application and is likely to increase the CO2 emissions due to the CO2 from battery production. A hybrid powertrain with low carbon fuels shows the potential to substantially reduce CO2 and total cost of ownership.

02 PETROLEUM↗

Improved Cross Section Generation Capability of Griffin in FY22

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of deterministic steady-state transport solvers for fixed source, k-eigenvalue, adjoint, and subcritical multiplication as well as transient solvers for spatial dynamics with the improved quasi-static method. Griffin uses cross section data in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. In recent years, the MC 2 -3 modules have been added to Griffin for fast reactor cross section generation, and the self-shielding application programming interface (SSAPI) was implemented in the ISOXML module for thermal reactor cross section generation. The on-the-fly slowing down method and double-heterogeneity treatment have been implemented to SSAPI and verified against particulate fuel-bearing graphite-moderated thermal reactor problems with high accuracy. This year, work has been focused on improving the cross section generation capability of ISOXML and streamlining the cross section generation procedures. In addition, the form function data were added to ISOXML in order to support the pin power reconstruction capability that was newly implemented in Griffin in this fiscal year. To facilitate the cross section generation using MC 2 -3 and SSAPI in Griffin, the cross section generation workflows have been set up for both fast and thermal spectrum reactors. The MOOSE action system tool was devised for fast spectrum problems, and the MOOSE stochastic tool was adopted to the branch calculation procedure for thermal spectrum problems. Meanwhile, to ensure the accuracy of group-constants, the thermal up-scattering kernel calculator accounting for resonance scattering was implemented in ISOXML, demonstrating the accurate computation of a Doppler-broadened scattering kernel of any Legendre order within a reasonable timescale. Other aspects of ISOXML, such as deletion solver and data, documentation, ISOXML file management, and the interface for Mixture, were improved as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Usability and Optimization Improvements in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to MOOSE’s broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities were implemented in the same order as they are covered in this report: multiple nonlinear systems in the same input file, implementation of generic field transfers and other transfer system enhancements, support for stateful material property redistribution for adaptivity and distributed meshes, and dynamic linking and loading of individually compiled applications. These four additions are fundamental capabilities that will be leveraged by many NEAMS applications.

97 MATHEMATICS AND COMPUTING↗

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies

This report describes the overall findings of the research project “Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies”. The motivation and goal for this project were to increase the carbon utilization efficiency (CUE) and areal productivity for algal cultivations, thereby reducing CO 2 costs to cultivation operations and improving economics. This was achieved through a combination of enhanced delivery of inorganic carbon and improved strains of Nannochloropsis oceanica capable of higher rates of bicarbonate uptake and metabolism. The project succeeded in these goals. First, a bubble-free, membrane-based technology was developed for delivering CO 2 to cultivations, which increased the CUE from the 15-20% that is standard in the industry to more than 65%. In addition, N. oceanica was modified to express a bicarbonate transporter protein, BicA, which enabled the cells to grow more quickly. In addition, advances were made in the protein engineering of carbonic anhydrase (CA) for enhanced stability and catalytic performance, the computational fluid dynamics modeling of algal cultivation systems, and in the life-cycle assessment and technoeconomic analysis of algal production. Together, these outcomes contribute to advancing algal cultivation as an economically viable platform for production of fuels, materials, and other chemical products. The project results aid in addressing the dual challenges of reducing atmospheric CO 2 levels and achieving green energy solutions.

09 BIOMASS FUELS↗

A Comparison of Novel Woodstove Catalytic Combustors to Improve Low Temperature Light off and Catalyst Stability at High Temperatures and Corrosive Environments, Including Stability of Catalytic Combustors used in Coastal Environments

Wood heaters with catalytic combustors often emit higher levels of particulate matter (PM) and volatile organic compounds (VOCs) at startup before the catalytic combustor is at operational temperature. This project aims to advance wood heater catalytic combustor technology by studying degradation mechanisms in failed field catalysts and using those insights to develop an improved catalytic combustor. Findings will provide critical insights into the degradation mechanisms of wood heater catalytic combustors and inform the design of catalysts that deliver improved efficiency, durability, and performance compared to current commercial products. The outcomes of this project will not only improve catalytic combustor technology but also contribute to operational guidelines to extend catalyst lifetimes.

99 GENERAL AND MISCELLANEOUS↗

Addressing Investment Barriers by Improving Documentation of Sustainable Biomass Resources (Workshop Report)

On May 8, 2025, Oak Ridge National Laboratory (ORNL), in collaboration with IEA Bioenergy Task 43 and the Biofuture Platform, convened an international workshop in Vancouver, Canada to improve the Global Biomass Resource Assessment. This effort addresses investment barriers in the global bioeconomy by improving the transparency, consistency, and usability of biomass supply data. The workshop gathered 38 participants from 11 countries, representing government agencies, academia, and industry. Participants reviewed the status of the biomass dataset, tested the data-sharing platform, and provided direct input on priorities for improvement.

09 BIOMASS FUELS↗

Improved Weld Residual Stress Modeling System in BlackBear

This report presents enhancements to the MOOSE-based BlackBear application aimed at improving its capability to simulate welding and other thermo-mechanical manufacturing processes. Two primary avenues of improvement are pursued. First, to enhance user accessibility, we introduce a centralized default block restriction mechanism that ensures coverage checks are performed within user-specified default blocks. This default setting is applied consistently to all block-describable objects, such as variables, kernels, and more. In addition, we develop a modular action for moving heat source simulations, which integrates path file parsing, subdomain modification, and heat source kernel enforcement into a single, streamlined configuration. Second, to improve solver robustness, we implement an alternative method for assigning initial conditions to the updated active domain during the simulation, thereby enhancing convergence behavior. To validate the framework, we design and conduct several benchmark simulations, including heat conduction with progressive material addition, linear elasticity with time-dependent material deposition, and viscoplasticity model with isotropic hardening under similar conditions. Finally, we demonstrate the effectiveness of the proposed framework through large-scale thermo-mechanical welding simulations in both two and three dimensions.

42 ENGINEERING↗

FY25 MOOSE Usability Improvements: 3D Meshing Capabilities, Initiation of Geometry Support for Monte Carlo Tools, and Enhancement of MOOSE/Workbench User Input Interactions

Usability improvements have been made to MOOSE and Workbench in FY25 to enhance usability and user workflows. Assorted enhancement have been made to MOOSE’s intrinsic meshing capabilities in order to enable more flexible and complex meshing of nuclear reactor systems, in particular for 3D applications. Mesh generators have been added to perform operations such as batch mesh generation, surface mesh generation, and creation of 3D transition layers. These mesh generation capabilities make it much easier to generate high quality non-extruded 3D meshes. Additionally, work to integrate Monte Carlo reactor physics simulations into MOOSE-based multi-physics workflows has reached another milestone with the implementation of the Constructive Solid Geometry (CSG) base framework. This framework lays the foundation for mesh generators to offer the user a generic CSG output option (as opposed to a finite element mesh). To support users, workshop on the MOOSE Reactor Module was delivered which featured hands-on examples using the NEAMS Workbench on INL’s High Performance Computing system. Recent updates to the NEAMS Workbench, WASP, and the MOOSE language server have introduced several improvements aimed at making MOOSE-based simulation setup and input management faster, more accurate, and easier to use. Key capabilities that have been added include multi-tab-stop autocompletion, visual input diagnostics, developer-directed data visualizations, upgraded ParaView integration, and Workspace-level file tracking. Together, these changes make it easier for users to build, validate, and manage complex MOOSE-based simulation models — especially those involving reusable components, included files, and datasets. The improvements are designed to save time, reduce input errors, and help users get to a successful simulation run faster, with more confidence in the results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Improvement and Verification of Online Cross Section Generation Capability of Griffin for TRISO-fueled Reactors

Griffin, a MOOSE-based reactor multiphysics code jointly developed by Idaho National Laboratory and Argonne National Laboratory under the DOE Office of Nuclear Energy’s NEAMS program, has pursued the development of an online multigroup cross section generation capability for a few years to enable high-fidelity, problem-dependent neutronics analyses of advanced thermal reactors. Recent advancements in Griffin’s online multigroup cross section generation capability have significantly improved the accuracy, robustness, and efficiency of self-shielding calculations for both prismatic and pebble-bed TRISO-fueled reactor applications. Key developments include a unified fuel self-shielding method applicable to both TRISO and annular compact/spherical shell fuel zone geometries; an advanced Dancoff Category-based Equivalence Theory using a bell function for non-fuel resonance treatment, achieving more than an order-of-magnitude speedup compared to the Tone method; an on-the-fly multigroup equivalence approach to mitigate group condensation errors; and a streaming correction method for pebble-bed homogenization. A proof-of-concept demonstration of on-the-fly group condensation with consistent P0 transport correction was also achieved. The method reproduced direct fine-group solutions with excellent accuracy (eigenvalue errors within 10 pcm and pin-power differences within 0.5%), but due to performance limitations of the current fixed-source solver, improvements to solver efficiency will be addressed in future work. Verification tests were performed on graphite-moderated TRISO-fueled two-dimensional core benchmark problems representing gas-cooled microreactors, heat pipe-cooled microreactors, gas-cooled pebble-bed reactors, and fluoride salt-cooled high-temperature reactors. Across all cases, Griffin showed excellent agreement with Serpent2 continuous energy Monte Carlo solutions: eigenvalue errors within 200 pcm, pin-power root-mean-square errors within 2%, and control rod and drum worth errors less than 2%. It should be noted that, for the benchmark problem, cross section generation contributed less than 3% of the total simulation times. These results demonstrate that Griffin’s online cross section generation capability delivers accurate and efficient reactor physics solutions across a wide spectrum of TRISO-fueled advanced reactor designs. With further improvements to the fine-group fixed-source solver and planned extensions to depletion, transients, and coupled neutron–gamma transport, Griffin will be well-positioned to become a powerful and comprehensive tool for advanced reactor analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Improving Runtime Performance of Tensor Computations using Rust From Python

In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API—e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor kernels of increasing complexity, from simple kernels involving sums of products over data accessed through single and nested loops to more advanced tensor multiplication kernels that are key in low-rank tensor decomposition and tensor regression algorithms. In numerical experiments involving synthetically generated tensor data of various sizes and these tensor kernels, we demonstrate consistent improvements in runtime performance when using Rust from Python over 1) using Python alone, 2) using Python and the Numba just-in-time Python compiler (for loop-based kernels), and 3) using the NumPy Python package for scientific computing (for pyttb kernels).

97 MATHEMATICS AND COMPUTING↗

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Improvements to MOOSE user workflow through polyhedral elements, automation, and concise physics syntax

The MOOSE framework is a foundational capability used by the NEAMS program to create over 15 different simulation tools for advanced nuclear reactors. Due to MOOSE's broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of NEAMS tools and includes: addition of support for polyhedral elements, incorporation of mesh smoothers for mesh repair, integration of the Physics and ActionComponents systems, expansion of the Convergence system, and exploration of automated input file generation. These five areas of development are fundamental capabilities that will be leveraged by many NEAMS applications.

97 - MATHEMATICS AND COMPUTING↗

Performance Improvements of the Griffin Solvers in FY24

The Griffin code is a MOOSE-based reactor physics application jointly developed by Idaho National Laboratory and Argonne National Laboratory under the Department of Energy Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation Program. This fiscal year, we have made significant efforts to improve the performance of transport solver options and cross-section generation for the efficient use of Griffin in advanced reactor applications. For the HFEM-PN solver, the residual evaluations of HFEM kernels were optimized by utilizing the pre- computed averaged cross sections for individual elements. Numerical integration involving the evaluation of basis functions at quadrature points was bypassed by facilitating precomputed element mass matrices for response matrices. Red-black iterations were improved by introducing a new generalized minimum residual based solver. The memory usage of response matrix storage was significantly reduced by applying basis function rotations on interfaces and calculating volumetric odd-parity moments on the fly. Additionally, the adjoint flux and transient calculation capabilities of the HFEM-PN solver were successfully implemented and verified using the TWIGL benchmark problem. For the DFEM-SN solver, memory footprint and computation time were significantly reduced by not treating angular flux vectors as the MOOSE nonlinear system vectors. Specifically for IQS, scalar adjoint weighting was introduced to further eliminate angular adjoint flux storage in the MOOSE auxiliary system. It was demonstrated through the three-dimensional Advanced Burner Test Reactor core problem that the memory usage for transient calculations with the IQS method was reduced by over 7.5× compared to before the optimizations. For the self-shielding application programming interface, a new double-heterogeneity treatment method, named the Bell Function-Based Analytic Two-Region Slowing Down Method, was developed to efficiently flux-volume homogenize TRISO particles with the matrix. Additionally, optimizations were made to hyper- fine group (HFG) slowing down calculations by pretabulating collision probability coefficients and grouping isotopes, significantly reducing the computational time for calculating scattering sources per HFG. Lastly, the pin power reconstruction module was extended to account for temporal behavior in a microreactor analysis problem, specifically for a control drum transient. Verification tests for each of these improvements demonstrated significant performance enhancements and memory reduction.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗