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This report investigates the response of the inverter under different terminal voltage and operating conditions. The goal is to understand the control objective of the inverter (e.g., injection of reactive current for voltage dips) based on the inverter’s response. No attempt is made to determine the exact control algorithms implemented in the inverter.
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The System Analysis Module (SAM) is under development at Argonne National Laboratory as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. This report summarizes recent progress under DOE-NE’s Nuclear Energy Advanced Modeling and Simulation program in SAM code development, demonstration, and validation to support transient safety analysis of Fluoride-salt-cooled High-temperature Reactors.
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This work investigates the projection of safe operation for grid-following inverters using a reference model. This work applies recursive-least square and model reference adaptive estimation techniques to estimate the unknown grid parameters. The estimated grid parameters are used in a dynamic reference model to project the safe operation of the inverter. This work also demonstrates that the controller nonlinearity and parameter variations can cause unsafe operations, such as unexpected instability at low power levels, namely hidden mode of instability. This instability issue in the nominal operating range may occur for given control parameters in a weak-grid condition. The dynamic reference model for PQ-controlled inverters is applied to analyze this instability issue using the estimated grid parameters. Both adaptive identification techniques can estimate the unknown parameters accurately, and the dynamic reference model can safeguard inverters from an unsafe operation, i.e., hidden instability, by examining the incoming new power setpoints before engaging them to the local controller. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a 12 kW NHR 9410 power grid emulator.
The SLIA Reference Architecture Models project, sponsored by the DOE CESER Energy CyberSense Program (Oct 2024–Sep 2025), advanced LLNL’s PySCES simulation tool to better support CyTRICS Prioritization and Initial Risk Assessment (PIRA) reference architectures. Key achievements include enhancements to the PySCES transmission substation facility model, expanded asset coverage, and enhancements to the PySCES code base. Software improvements reduced code complexity, migrated PySCES to Python version 3.11, introduced an object-oriented design, and added a schema database for easier updates and validation. New features support device criticality assessments and a more precise parametric simulation mode. Remaining gaps include model validation, workflow limitations, Monte Carlo convergence issues, full device criticality metric implementation, model fidelity, and general software improvements. Continued development is recommended to address these gaps and fully align PySCES with CyTRICS PIRA requirements.
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.
Reference inhalation dose models rely on deterministic biokinetics and reference computational phantoms, limiting their applicability to the variability present in population-specific exposures encountered in emergency response scenarios. Here, this study introduces REDCAL, a Python-based computational framework developed to propagate uncertainty in inhalation dose coefficients using the International Commission on Radiological Protection (ICRP) Publication 66 Human Respiratory Tract Model. REDCAL integrates ICRP deposition and clearance models, systemic biokinetics, and governing physics principles, and leverages Sandia National Laboratories’ Dakota toolkit for uncertainty quantification via Latin Hypercube Sampling. REDCAL was validated against DCAL, with biokinetic retention results differing by less than 1% and effective dose coefficients by less than 2% across all tested radionuclides. Stochastic sampling introduced variability in dose coefficients, with geometric standard deviations (GSD) in committed effective dose coefficients (CEDC) ranging from 1.0 to 1.5, based on lognormal distribution fits. Analysis demonstrated that variations in the activity median aerodynamic diameter (AMAD) notably influenced the computed CEDC values. Smaller particles (<1 µm) increased doses by 20–30% due to deeper lung deposition and prolonged retention for alpha emitting radionuclides, such as 241 Am and 239 Pu. Radionuclides with fast clearance, such as 133 I, demonstrated a dose reduction exceeding 50%, as AMAD increased beyond 5 µm due to upper airway deposition and rapid mucociliary clearance. The greatest GSD among the radionuclides reported in this study was for 241 Am. In most cases, the largest GSDs in the CEDC were associated with larger particle sizes, an expected outcome, as ICRP Publication 66 defines GSD in particle size as a function of AMAD, resulting in an extended tail of the lognormal distribution. The findings support improved inhalation dose assessments and enhance consequence management strategies for the U.S. Federal Radiological Monitoring and Assessment Center by quantifying uncertainty in dose coefficients and strengthening decision-making for emergency response scenarios.
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This work introduces a reference plant model for a generic monolithic heat-pipe-cooled microreactor. The model will serve as a springboard to develop future evaluation models in the licensing process of similar microreactor designs at the U.S. Nuclear Regulatory Commission. This model has been developed with the Comprehensive Reactor Analysis Bundle and its specifications are based on open literature publications for the eVinci TM design. BlueCRAB is the U.S. Nu- clear Regulatory Commission non-light-water reactor analysis system based on the Multiphysics Object-Oriented Simulation Environment framework, which can couple the Griffin, BISON, and Sockeye applications to resolve the various physics that are essential for the safety analysis of this type of reactor system. The core specifications includes tristructural isotropic fuel, graphite monolith, graphite reflectors, and drums composed of graphite and B 4 C. No moderator or burnable poison pins are used in the design. The fuel enrichment is reduced to control excess reactivity in the core. This core design is not optimized and only serves for testing purposes, since the primary objective of this work is to exercise the multiphysics coupling for this type of reactor system. A three dimensional (3D) core heterogeneous Griffin discrete ordinates (SN) transport model allows the precise calculation of the flux distribution and pin powers. Griffin transfers the power density distribution and obtains a temperature distribution to and from BISON. The BISON model com- putes the 3D core temperature distribution and is coupled to 876 Sockeye subapplications running a heat pipe model. This 3D conduction model is coupled to the various heat pipes via heat flux boundary conditions. The model includes a small gap between the heat pipe and the monolith. Convective heat transfer boundaries with either ambient temperature or condenser temperature as heat sinks are imposed at the model boundaries. The 2D Sockeye heat pipe model uses a vapor- only methodology, which provides the needed resolution for transient calculations and allows the determination of various heat pipe limits. This approach is superior to the superconductor model traditionally used in steady-state calculations. BlueCRAB computes steady-state power and temperature distributions that serve as the initial condition for a loss-of-heat-sink transient simulation. The steady-state results show significant peaking due to the position of the control drum, but this is a characteristic of the particular design used, which is not optimized at this stage. The transient results show the reactor power slowly stabilizing towards a 3% power level after the partial loss of secondary heat removal. Several recriticalities are observed due to cooling through the secondary system but the reactor is self-stabilizing and behaves as expected.
The Reference Model Project, supported by the U.S. Department of Energy, was developed to provide publically available technical and economic benchmarks for a variety of marine energy converters. The methodology to achieve these benchmarks is to develop public domain designs that incorporate power performance estimates, structural models, anchor and mooring designs, power conversion chain designs, and estimates of the operations and maintenance, installation, and environmental permitting required. The reference model designs are intended to be conservative, robust, and experimentally verified. The Backward Bent Duct Buoy (BBDB) presented in this paper is one of three wave energy conversion devices studied within the Reference Model Project. Comprehensive modeling of the BBDB in a Northern California climate has enabled a full levelized cost of energy (LCOE) analysis to be completed on this device.
Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.
Here, the repetitive rapid solidification that occurs in metal additive manufacturing (AM) processes creates microstructures distinctly different from wrought materials. Local variability in AM microstructures (either by design or unintentional) raises questions as to how AM structures should be modeled at the part-scale to minimize modeling error. The key goal of this work is to demonstrate a posteriori error estimation applied to an AM part. It is assumed that the actual microstructure is unknown and an approximate, spatially uniform material model is used. Error bounds are calculated for many reference models based on AM microstructures with elongated grain morphology and localized or global fiber textures during a post-processing step. The current findings promote confidence that a posteriori model form error estimation could be used effectively in mechanical performance simulations of AM parts to quickly obtain quantitative error metrics between an approximate model result and many microstructure-based reference models. The a posteriori error estimation introduces significant time savings compared to computing the full reference model solutions. Tight bounds on model form error are obtained when texture variations in the reference models occur on large length scales. For materials with property variation at small length scales, multi-scale error estimation techniques are needed to properly account for the many interfaces present between areas with different properties.
This report details the progress of Idaho National Laboratory (INL) in creating a reference plant multiphysics model for the Advanced Burner Test Reactor (ABTR). This model was developed under Task 13 of the U.S. Nuclear Regulatory Commission project “Development and Modeling Support for Advanced Non-Light Water Reactors,” and is an extension of the reference plant model developed in Task 4b, which was improved upon in the following ways. (1) The discrete ordinates method was used in lieu of the super-homogenization (SPH)-corrected diffusion approximation in order to better capture the anisotropic scattering contribution and the neutron leakage change due to thermal expansion. (2) The novel neutronic spatial discretization approach, termed the ring-heterogeneous (RH) approximation, was conceptualized and introduced to capture the differential expansion of the materials in the core. This new technique proved capable of preserving fission rates and maintaining the eigenvalue within 2.5% and 266 pcm with 9 neutron energy groups, respectively. Separating the different materials in the core enables the differential expansion of materials to be explicitly accounted for, eliminating the need for problem-specific cross-section functionalization techniques. (3) The SAM model for the core and system thermal-hydraulics analysis was updated to include 61 channels instead of just four representative ones. This enables users to obtain improved spatial resolution for sodium temperature and density scalar fields. (4) All the mesh files were created via the Multiphysics Object-Oriented Simulation Environment (MOOSE) Reactor module, eliminating all reliance on external tools for mesh creation. (5) Finally, the fuel axial expansion now leverages the HT9 and UPuZr material properties that have been validated against experimental data. The reference plant model was used to perform a full-core unprotected loss of flow (ULOF) transient calculation, including neutronics, thermal and mechanical feedback mechanisms. Future work will be devoted to further enhancements of the model. Potential improvements to the model include the addition of the control rod driveline expansion feedback and the upgrading of the support plate model so as to explicitly include 3D effects. Additionally, a Nuclear Energy Advanced Modeling and Simulation funded parallel effort has completely automated the creation of the ring-heterogeneous (RH) mesh from the fully heterogeneous (FH) geometry, thus maximizing user friendliness for the sodium fast reactor sodium-cooled fast reactor (SFR) workflow and will be incorporated in future work.
This work presents the latest improvements to, and investigations performed with, the pebble-bed high-temperature gas-cooled reactor (PB-HTGR) reference plant models for the United States Nuclear Regulatory Commission. These models serve as the foundation for the future development of detailed design evaluation models based on license applications. The reference plant models have been developed with the Comprehensive Reactor Analysis Bundle, or BlueCRAB, which is the code suite proposed for non-light-water reactor systems safety analysis. It incorporates various simulation tools developed by the Nuclear Energy Advanced Modeling and Simulation program, including the Griffin code for reactor physics, the Pronghorn and SAM codes for core thermal fluids, the BISON code for solid conduction and fuel performance, and the SAM code for system analysis. The primary objective of the work that was performed was to assess BlueCRAB’s level of readiness for modeling a PB-HTGR. To do so, we first developed numerical models in BlueCRAB that include the key physics for this technology to ensure an adequate level of fidelity for modeling PB-HTGR core performance and for performing multiphysics simulations for equilibrium core conditions and different accident scenarios. Then we simulated transient scenarios, including depressurized and pressurized loss of forced cooling accidents, over-cooling, and control rod withdrawal events with delayed and prompt supercritical reactivity insertions. The analysis in this report includes comparisons of the 2D thermal fluid porous media models in Pronghorn and SAM, and comparisons of coupled SAM/Griffin/SAM and coupled Pronghorn/Griffin for depressurized and pressurized loss of forced cooling, over-cooling, and control rod withdrawal events. In addition, we compare 3D, 2D, and 0D/PKE neutronic models for the two control rod withdrawal scenarios with coupled Pronghorn/Griffin. The comparisons show that the BlueCRAB models lead to physically intuitive solutions for the scenarios examined. The changes in the various scalar and vector fields, such as neutron flux, power, temperature, density, pressure, and velocity, are within the expected ranges, and their distributions can be explained by the system response of the transients and the geometric and material variations. Several comparisons suggest that the porous media models in Pronghorn and SAM can lead to similar solutions, even though they are based on different methodologies. This work further highlights the need for flexible tools with various levels of fidelity to cover the breadth and depth of needs that may arise in future technical evaluations of the PB-HTGR. We believe that the BlueCRAB capabilities will be a significant asset for confirmatory analyses in order to resolve important safety questions.