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DOE OSTI2025

Reassessment of Boiling Water Reactor Rod Cusping Capability of VERA-MPACT

This technical report assesses the current modeling capabilities of the Virtual Environment for Reactor Applications (VERA) for minimizing control rod cusping in boiling water reactors. Assessment is performed on a 10 × 10 General Electric (GE)-14 assembly as well as a simplified version of the same assembly. Runs were performed using VERA for three available cusping treatments in the Michigan PArallel Characteristics Transport (MPACT) code: no treatment, subplane treatment, and fitted polynomial correction treatment. Subplane treatment is discussed, but specific results are not provided because of failures in the method of implementation for boiling water reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

High-Burnup LOCA Burst Susceptibility BISON Analysis in PWRs and BWRs

Accurately assessing high-burnup fuel behavior during loss-of-coolant accidents (LOCAs) is essential for understanding fuel fragmentation, relocation, and dispersal (FFRD) risks across the US light-water reactor fleet. This work updates previous Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program multiphysics LOCA analyses for a pressurized water reactor (PWR) and a boiling water reactor (BWR) by incorporating recent model and material property advancements in the BISON fuel performance code, including a high-burnup structure (HBS) model, revised cladding burst criteria, and updated thermal–mechanical correlations. This update was needed to support ongoing industry initiatives and upcoming regulatory changes. Full-core, rod-resolved operating histories generated using Virtual Environment for Reactor Analysis (VERA) and system-level LOCA conditions obtained from TRACE were applied to statistically representative rod samples in BISON to evaluate burst behavior and FFRD susceptibility. These calculations used two cladding burst correlations and three fuel pulverization models so that the predictions of these models could be compared. The updated PWR simulations show markedly improved numerical stability as the number of crashed simulations decreased by 95% compared to the previous study, and hence higher confidence in results. The updated PWR simulations predicted cladding bursts exclusively among once-burned, high-power rods, with two different cladding burst models identifying the same burst-susceptible population. Resulting FFRD susceptibility estimates are significantly reduced compared with earlier studies, driven by cooler predicted fuel and plenum temperatures, lower hoop strains, and reduced fission gas release in the updated models. In contrast, none of the BWR rods were predicted to burst under either burst criterion, reaffirming minimal BWR FFRD susceptibility even with updated HBS and material models. Comparisons between the PWR and BWR end-of-cycle predictions are made. Comparison with prior work highlights significant shifts in PWR fuel performance metrics and confirmation of earlier BWR conclusions. Overall, the updated results underscore the importance of having high-resolution detailed modeling capability and continuously integrating evolving material models and physics into high-resolution multiphysics simulations. The unified assessment presented here strengthens confidence in predicting high-burnup LOCA behavior by improving agreement between different cladding burst correlations. These results also provide an improved foundation for future BISON model development, FFRD susceptibility calculations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

NW-BRaVE T3 Hydroplane Project Close: Project Close-out for T3 Hydroplane Analysis

Thrust 3 of the Northwest Biopreparedness Research in a Virtual Environment was an expansive project including method development, sample collection and sequence analysis. The sampling occurred over a multi-year period to generate metagenomic datasets that inform cyanophage-picocyanobacterial interactions in the Salish Sea across a moderate timeframe and geographical range. Part of the thrust’s aim was to validate how much experimental structural and multiomics work in a model organism (Prochlorococcus Marinus, str. MED4) from thrusts 1 and 2 would carry over into a broader range of related organisms in the natural world, to address a fundamental question in scientific preparation for epidemics: whether and how much experimental information from known and experimentally tractable species can translate to actionable biological information in unknown species. In other words, thrust 3 aimed to find out whether the model organism experiments matter in terms of how organisms interact. This report updates work described in Johnson and Pollock 2025 (1).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Update on Radiochemical Assessment of High Burnup Commercially Irradiated Fuel

This work documents an effort to collect burnup measurements on a high burnup rod, designated 6XV, and first cycle accident tolerant fuel (ATF) rod, designated 47I, to enable benchmarking of fuel performance codes and neutronics codes. In addition to measurements, Virtual Environment for Reactor Applications (VERA) full-core-depletion analysis was also performed for the rods that were experimentally analyzed to provide an opportunity for code validation. This effort focuses on collecting data from rods irradiated at Byron Generating Station and shipped to the Oak Ridge National Laboratory (ORNL) hot-cells. This data will also anchor non-destructive examination evaluations of burnup of the various fuel rods undergoing postirradiation examination (PIE) at ORNL. Previous PIE of these fuel rods provides some guidance on the burnup trend across the fuel. Axial gamma spectroscopy scans provide a measure of relative changes in burnup across a fuel pin. Mass spectrometry based burnup measurements performed for this work at specific axial locations in the fuel are fully quantitative. By combining the mass spectrometry data with the gamma scans it is possible to more quantitatively evaluate axial variations in burnup across the entire fuel pin [1]. The combined set of burnup evaluations will be made available to other organizations that have an interest in high burnup radiochemistry data for validation of neutronic simulations and source term evaluation such as the Nuclear Regulatory Commission (NRC).

Harp, Jason [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗

DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

A High-Fidelity Model of the Peach Bottom 2 Turbine-Trip Benchmark Using VERA

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type.

BWR↗

DOE OSTI2022

Uncertainty Quantification in High-Low Dynamic System Coupling using RAVEN and TRANSFORM

This work demonstrates new functionality and applications stemming from the development of high-fidelity to low-fidelity (high-low) coupling for system simulations and to further explore the capabilities of the Risk Analysis Virtual Environment (RAVEN) in the performance of uncertainty quantification in this kind of high-low coupled system models. The work builds from previous work on high-low coupling that utilized COBRA-TF (CTF), the high-fidelity subchannel analysis code, with a low fidelity model built in ORNL’s TRANSFORM, the system analysis code, utilizing the Functional Mock-Up Interface (FMI). Steady-state and transient analysis examples using the high/low coupled models generated from CTF and TRANSFORM/FMI are investigated. The workflows for both steady-state and transient coupled simulations are described. A steady-state parameter sweep and uncertainty analysis of the primary flow rates and reactor power are demonstrated. Likewise, a transient pump trip and power ramp sensitivity studies are also demonstrated. This work elucidates some of the potential benefits and future needs of using RAVEN for high/low system coupling analysis of energy systems. It also shows some of the difficulties that can be encountered in coupling system simulations.

Williams, Wesley↗

DOE OSTI2023

Hydrogen Production, Grid Integration, and Scaling for the Future

The Hydrogen Production, Grid Integration, and Scaling for the Future project will inform clean hydrogen production deployment across the planning, procurement, and operation stages of hydrogen production on the grid. Hydrogen production from renewables is a clean source of fuel which is near zero for greenhouse gas emissions and criteria pollutants. The results from this project will inform entities looking to build clean energy projects that produce good paying jobs in manufacturing, installation, maintenance and operation of these facilities. This project provides system characterization examples, multiple configurations, optimizations, and suggested metering and custody transfer points through multiple scenarios and hardware testing of NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios. This work also creates a digital twin for emulation in the Advanced Research on Integrated Systems (ARIES) virtual environment and performs hardware-in-the-loop (HIL) testing of pilot-scale, decentralized, and centralized hydrogen systems.

centralized hydrogen production↗

DOE OSTI2023

Digital Twin for Optimizing Real-time Economy of the Integrated Energy Systems

Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.

25 ENERGY STORAGE↗

DOE OSTI2022

CTF Improved Drag Model and Flow Regime Transition Criteria

The demand for accurate prediction of two-phase flow behavior in a boiling water reactor (BWR) requires a comprehensive understanding of flow regime, void fraction, heat transfer, and pressure drop. The CTF subchannel code, which is used for the Thermal/Hydraulic (T/H) solution in the Consortium for Advanced Simulation of Light Water Reactors (CASL)-developed Virtual Environment for Reactor Application (VERA) core simulator, is being further developed for BWR applications. In support of this goal, the present work highlights some of the two-phase closure model developments towards improving the CTF void fraction prediction, especially for subcooled boiling. The drift-flux approach has been well-developed for upward dispersed two-phase flows and proven to be accurate in predicting void fraction in bubbly and slug flow regimes. In this work, these kinematic constitutive relations for the drift-flux velocity have been implemented into CTF to describe the interfacial drag of bubbly flow as an alternative to the existing model for better void fraction prediction. The success of these constitutive relations also relies on a good flow regime map that accounts for flow conditions and channel geometry. A more reliable flow regime transition criteria that account for the flow condition has also been implemented in this study for modeling the flow regime transition criteria. The newly implemented models are shown to give improved void fraction predictions in comparison to experimental data.

Hizoum, Belgacem↗

DOE OSTI2022

VERA neutronics high-fidelity benchmark for a modern PWR core design

Nearly 20 years since the origin of the infamous 'Kord Smith Challenge', and after 10 years and hundreds of millions of dollars invested by the US Department of Energy into the development of the Virtual Environment for Reactor Applications (VERA), the capability to accurately simulate a modern pressurized water reactor (PWR) fuel cycle at the fuel rod level with high-fidelity Monte Carlo (MC) stochastic transport methods still evades us. The deterministic methods in VERA make approximations that allow it to be successfully benchmarked against measured data from hundreds of PWR fuel cycles, but none of these benchmarks provided information at the local fuel rod level or allowed quantification of these approximations' impact for modern PWR reload designs. Using MPACT to establish the estimated isotopic distributions and thermal hydraulic conditions in a recent and challenging reactor design, a 2D quarter-core benchmark problem was established and solved consistently with both MPACT, the 51-energy group deterministic transport solver in VERA, and Shift, the continuous-energy MC transport solver in VERA. Impacts of energy groups, ray spacing, and scattering treatment were evaluated. Though MPACT has been previously shown to perform well compared to MC methods for initial Cycle 1 cores with only fresh fuel, this is the first benchmark of MPACT for a challenging modern reload core with depleted fuel and burnable absorbers. This work establishes additional credibility for the methods in MPACT and highlights the efficiency of these methods relative to those in use by MC-based tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Status of the OECD/NEA Watts Bar unit 1 benchmark and calculations of local reactor core power data of the Zero Power physics tests

The paper aims to provide an update on the status of the Organization for Economic Cooperation and Development (OECD) / the Nuclear Energy Agency (NEA) Tennessee Valley Authority (TVA) Watts Bar 1 (WB1) Multi-Physics Multi-Cycle depletion benchmark and the results associated with Exercise 1. The benchmark relies on the set of benchmark progression problems developed by the Department of Energy (DOE) Consortium for the Advanced Simulation of Light Water Reactors (CASL) for the Virtual Environment for Reactor (VERA), which are based on real plant design and operational data. The OECD/NEA TVA WB1 benchmark is designed for validation of both traditional and novel high-fidelity multi-physics codes to analyze Pressurized Water Reactors (PWR) depletion cycles. The activities are conducted under the Expert Group on Reactor Systems Multi-Physics (EGMUP) at NEA/OECD. In this work, we analyze Exercise 1 of the benchmark: stand-alone Three-Dimensional (3D) neutronics at Start-up Zero Power Physics Test (ZPPT) at Hot Zero Power Conditions (HZP) and the resulting power maps. The code used to model the exercise is the continuous energy Monte Carlo code Serpent 2.1.31 along with the nuclear data library ENDF/B-VII.1. Serpent results are compared with the high-fidelity deterministic code MPACT, which is part of VERA. The paper presents three selected results from power calculations required output. The results compared are the normalized axially integrated radial core power maps, the normalized axial averaged core power shapes, and the normalized core hottest and coldest assemblies radial power maps. The Root Mean Square Deviation (RMSD) between Serpent and MPACT is 0.51 % for the axially integrated radial core power map, 1.41 % for the axial averaged core power shape, 2.42 % for the hottest assembly axial power shape, 0.88 % for the coldest assembly axial integrated hottest assembly radial pin power map, and 0.17 % for the axially integrated coldest assembly radial pin power map. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Pressurized-Water Reactor Core Design using Multi-Objective Plant Fuel Reload Optimization Platform

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimization of core design. The optimization of the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible options of core configuration, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization (PRLO) platform which supports artificial-intelligence-based reactor core designing is now fully capable of handling realistic problems. The PRLO Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual Environment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multi-objective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

42 ENGINEERING↗

DOE OSTI2024

Hydrogen Production, Grid Integration, and Scaling for the Future

The project will explore near and long-term visions towards the commercialization of grid integrated electrolysis systems to inform deployment across the planning, procurement, and operation stages of hydrogen production on the grid. It will leverage NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios, also creating a digital twin for emulation in the Advanced Research on Integrated Energy Systems (ARIES) virtual environment and performing hardware-in-the-loop (HIL) testing of pilot scale, decentralized, and centralized hydrogen systems.

electrolysis systems↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗