Bayesian Model Selection Between Underdetermined Nuclear Reactor Models.
Abstract not provided.
Swiler, Laura Painton↗
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Web-Based Computational Model Builder For Nuclear Energy Advanced Modeling and Simulation (Final Report)
According to the World Nuclear Association, the U.S. is the largest producer of nuclear power worldwide. In fact, the U.S. provides over 30 percent of the world's nuclear generation of electricity. Although few new reactors have been built in the past 30 years, the association anticipates that four to six reactors may be built by 2020. The first of the reactors will be built in response to 16 license applications, which have been completed since 2007, to build 24 additional nuclear reactors. Currently, both government and industry are working to accelerate approval for the construction and design of new nuclear energy plants. The U.S. nuclear industry has already achieved significant developments in the implementation of nuclear power plants due to advancements in refueling, maintenance, and safety systems at current power plants. Meanwhile, changes in government policy, which have occurred since the late 1990s, have provided for noteworthy expansion in nuclear capacity. For example, the Energy Policy Act of 2005 evoked investment in electricity infrastructure such as nuclear power. To design future systems and continue operational improvements at existing U.S. plants, advanced modeling and simulation of nuclear reactors are crucial. We developed a Cloud/Web-based tool, HPCCloud, that enables nuclear energy firms, or other manufacturing and engineering companies, to innovate using advanced modeling and simulation. Through the Web-Based Computational Model Builder for Nuclear Energy Advanced Modeling and Simulation grant, we develop HPCCloud to simplify and democratize advanced modeling and simulation in the nuclear energy industry on a range of nuclear engineering applications. It leverages millions of investment dollars from the Department of Energy's Consortium for Advanced Simulation of Light Water Reactors (CASL) and the Office of Nuclear Energy's research and development in Nuclear Energy Advanced Modeling and Simulation (NEAMS). Our approach simplifies the workflow, alleviates the need for in-house computational science and engineering experts, and lowers the capital investments required for advanced modeling and simulation. The Cloud/Web-based simulation environment guides end-users and developers through the advanced modeling and simulation lifecycle. In addition to providing significantly improved, intuitive software, the environment offers reproducible workflows where one can save, share, and even publish the full path of data from input to final analyzed results. HPCCloud addresses identified challenges to the nuclear energy industry's use of advanced modeling and simulation. With this unique Cloud/Web-based environment, we enable an ecosystem of synergistic activities, fueling new research and development for nuclear energy. HPCCloud lays the groundwork for commercialization, through customization, technology integration, and advanced research and development services.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Multinucleon short-range correlation model for nuclear spectral functions: Theoretical framework
Not Available
Artiles, Oswaldo↗
The Origin of Chemical Elements: Connecting Laboratory Nuclear Astrophysics with Astronomical Observations through Nucleosynthesis Modeling
Nuclear astrophysics is a field at the frontier of nuclear science which continues to enjoy significant progress and exciting prospects for the future. It addresses the fundamental question of where and how the chemical elements are made. Except for hydrogen and helium, all other chemical elements are made either in stars or in cosmic explosions such as supernovae or neutron star mergers. To understand the formation of elements we need to understand the relevant nuclear physics processes that govern the interaction between nuclei. We need to understand the conditions in the star or the explosion under which these nuclear processes take place. And we need to understand how individual events of element formation contribute to the overall chemical enrichment of our Galaxy over time. Experimental facilities allow for terrestrial experiments on the nuclear physics governing the element synthesis. Earth-based and space-based telescopes probe the Galaxy around us and provide information about the conditions in stars and supernovae, and about the chemical element enrichment of these different environments. Numerical simulations of stars, supernovae, and other astrophysical phenomena allow us to probe the conditions of extreme densities and temperatures required for element synthesis. Nucleosynthesis research is ideally posed to integrate advances from all three of these areas and advance our understanding of how and where elements are made. Nucleosynthesis research provides theory support for experiments and observations. And it makes predictions that can be tested in nuclear physics experiments and astronomical observations. The goals of this award were in three main areas: 1. Develop and implement an effective core-collapse supernova model that is computationally efficient and that captures the relevant physics for the scientific questions of interest 2. Advance our understanding of the synthesis of the lightest heavy elements 3. Increase the realism of supernova nucleosynthesis and determine its dependence on the properties of the exploding star As the projects progressed, an additional focus was added to this list: 1. Study the explosion properties and the signals from the most massive stars.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Web-Based Computational Model Builder for Nuclear Energy Advanced Modeling & Simulation (Phase I Final Report)
Today, the importance of nuclear power in the US is a geopolitical matter as much as it is an economic one. This is due to the notion that increasing the use of nuclear power reduces the US's reliance on imported oil and gas. In order to design future systems and continue operational improvements at existing US plants, advanced modeling and simulation of nuclear power reactors is crucial. A variety of challenges exist to using advanced modeling and simulation in practice, especially for the nuclear energy community and other organizations with fewer computational resources and limited expertise. There is an increasing need of access to highly capable compute, data, and software resources from network systems, which are becoming more administratively restricted from access through installation of traditional client-server applications. The nuclear energy community is not capable of maintaining exponential growth of computing capability and data storage coupled with the necessary end-to-end network bandwidth improvements. Applications that provide higher fidelity with increased physics and decreased assumptions, by their very nature, require more and more computational resources. We believe Web-based, Cloud-enabled, end-to-end simulation software for nuclear energy advanced modeling and simulation to be the solution to these challenges.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Nonempirical Interactions for the Nuclear Shell Model: An Update
The nuclear shell model has perhaps been the most signifcant conceptual and computational paradigm for the understanding of the structure of atomic nuclei. While the shell model has been used predominantly in a phenomenological context, there have been efforts stretching back more than half a century to derive shell model parameters based on a realistic interaction between nucleons. More recently, several ab initio many-body methods—in particular, many-body perturbation theory, the no-core shell model, the in-medium similarity renormalization group, and coupled-cluster theory—have developed the capability to provide effective shell model Hamiltonians. We offer an update on the status of these methods and investigate the connections between them and their potential strengths and weaknesses, with a particular focus on the in-medium similarity renormalization group approach. Three-body forces are demonstrated to be important for understanding the modifications needed in phenomenological treatments. We then review some applications of these methods to comparisons with recent experimental measurements, and conclude with some remaining challenges in ab initio shell model theory.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Precise Lifetime Measurements in Light Nuclei for Benchmarking Modern Ab-initio Nuclear Structure Models
A new generation of ab-initio calculations, based on realistic two- and three-body forces, is having a profound impact on our view of how nuclei work. To improve the numerical methods, and the parameterization of 3-body forces, new precise data are needed. Electromagnetic transitions are very sensitive to the dynamics which drive mixing between configurations. We have made a series of precise (< 3%) measurements of electromagnetic transitions in the A=10 nuclei {sup 10}C and {sup 10}Be by using the Doppler Shift Attenuation method carefully. Many interesting features can be reproduced including the strong α clustering. New measurements on {sup 8}Be and {sup 12}Be highlight the interplay between the alpha clusters and their valence neutrons.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Nuclear Fuel Cycle Simulator as a Means to Model a Nuclear Hybrid Energy System
With a growing concern for how greenhouse gas (GHG) emissions impact climate change, there is a clear value in minimizing reliance on fossil fuels. Fluctuating renewable energy sources continue to penetrate the grid at higher levels. One means of ensuring a reliable electricity supply, while maintaining low overall GHG emissions, is to couple a nuclear power plant to the fluctuating renewable electricity source. Nuclear hybrid energy systems (NHES) focus on combining renewable and nuclear generation with industrial processes to ensure reliable energy generation as well as profitability of the system as a whole. This paper analyzes the current state of NHES models, and suggests how a nuclear fuel cycle simulator (NFCS) could be used to quickly model a nuclear hybrid energy system to address long term policy concerns. NFCSs are used to model the nuclear fuel cycle, which consists of uranium mining, enrichment, the reactor, reprocessing, and long term storage of waste. The simulations can span over hundreds of years to demonstrate different approaches to managing costs, waste disposal, and proliferation concerns. Current NHES models, on the other hand, focus on shorter time periods, primarily on a daily to weekly scale, and answer questions surrounding feasibility and costs for both normal and extreme scenarios. First, this paper will discuss the different criterion that are important to modeling a NHES. Then, we will break down the characteristics of a NFCS, analyzing how the characteristics of NHES and the fuel cycle overlap. After discussing the current modeling approaches for NHES and NFCS, this paper will analyze the benefits of using a NFCS to model a NHES. This paper will conclude that a NFCS, if appropriately augmented to account for the dynamic aspects of a NHES, can produce a flexible, high level NHES model quickly.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Nuclear Energy Model Intercomparison Project
This document summarizes the current status and future plans of the Nuclear Model Intercomparison Project. The objectives of this project are to understand how issues central to nuclear energy are modeled in long-term capacity expansion models, to investigate how model structures and input assumptions impact projections for nuclear’s role, to refine model representations of nuclear energy, and to communicate findings to the research community and decision-makers. High-level goals are discussed for each of the four participating model groups: the U.S. Energy Information Administration (EIA), U.S. Environmental Protection Agency (EPA), Electric Power Research Institute (EPRI), and National Renewable Energy Laboratory (NREL). This document summarizes scenarios and assumptions for the model comparison, outcomes from the first workshop, and next steps.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Latest development of the combinatorial model of nuclear level densities
The combinatorial model of nuclear level densities has now reached a level of accuracy comparable to that of the best global analytical expressions. We have recently improved the treatment using simultaneously the single particle levels and collective properties predicted by the newly derived D1M Gogny interaction. In addition for deformed nuclei, the transition to sphericity is coherently taken into account on the basis of a temperature-dependent Hartree-Fock calculation which provides at each temperature the structure properties needed to build the level densities. This new method is described and shown to give good results with respect to available experimental data.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model
Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB (“Boltzmann-Langevin One Body”), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also 12 C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of 12 C with 12 C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter (b) training a classifier of b jointly with the VAE. Results: In this work, the distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Nuclear Data Impact on Key Metrics for a Representative Molten Chloride Fast Reactor Model
Nuclear data are an essential component of the foundation on which all modeling and simulation methods and tools are relying upon, from the front end to the back end of the nuclear fuel cycle. In this study, the impact of uncertainties in nuclear data is investigated for a representative molten chloride fast reactor, for several important metrics, including eigenvalue, reactivity differences, and nuclide inventories in fuel at 5-yr irradiation. Uncertainty of keff for a full core model was found to be similar between the fresh fuel and the irradiated fuel states (1.7-1.8%), with its primary driver being the uncertainty in the 235U (n,γ) cross section. The results obtained for the reactivity differences show large uncertainties, of over 100%, in elastic scattering sensitivities of several nuclides, which led to large uncertainties of temperature reactivity differences for cladding and reflector. These results provide evidence that the currently applied methods may not be sufficiently adequate for ensuring the reliable determination of such metrics.
Procop, Germina [ORNL] (ORCID:0000000342226393)↗
Parametrized uncertainties in the spectral function model of neutrino charged-current quasielastic interactions for oscillation analyses
A substantial fraction of systematic uncertainties in neutrino oscillation experiments stem from the lack of precision in modeling the nuclear target in neutrino-nucleus interactions. Whilst this has driven significant progress in the development of improved nuclear models for neutrino scattering, it is crucial that the models used in neutrino data analyses be accompanied by parameters and associated uncertainties that allow the coverage of plausible nuclear physics. Based on constraints from electron scattering data, we propose such a set of parameters, which can be applied to nuclear shell models, and test their application to the Benhar [] spectral function model. The parametrization is validated through a series of maximum likelihood fits to cross section measurements made by the T2K and MINERvA experiments, which also permit an exploration of the power of near-detector data to provide constraints on the parameters in neutrino oscillation analyses. Published by the American Physical Society 2024
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A review of fuel performance modelling
Nuclear fuel performance codes or models exist for many fuel types and are used for a variety of purposes ranging from design optimization, experiment planning and interpretation to safety analysis. In the current review, we focus on the most relevant simulation tools for the nuclear industry and disregard those that were developed only for very specific applications. As a result, we consider only codes for LWR fuel, since they represent the dominant form for civil energy production.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
EMPIRE: A Reaction Model Code for Nuclear Astrophysics
The correct modeling of abundances requires knowledge of nuclear cross sections for a variety of neutron, charged particle and γ induced reactions. These involve targets far from stability and are therefore difficult (or currently impossible) to measure. Nuclear reaction theory provides the only way to estimate values of such cross sections. In this paper we present application of the EMPIRE reaction code to nuclear astrophysics. Recent measurements are compared to the calculated cross sections showing consistent agreement for n-, p- and α-induced reactions of strophysical relevance.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A roadmap toward scaling, reasoning and self-evolving foundation models for nuclear and particle physics
Foundation models have revolutionized artificial intelligence, with Large Language Models demonstrating unprecedented capabilities in multimodal understanding, reasoning and tool use. Nuclear and particle physics stands at a critical juncture where similar transformative potential awaits realization. The field generates exabytes of experimental data, exascale simulations, and decades of theoretical insights — yet these remain largely disconnected from modern Artifical Intelligence (AI) capabilities, with most physics AI applications confined to narrow, task-specific models that suffer from domain shifting when applied to real experimental data. We present a roadmap for FM4NPP (Foundation Model for Nuclear and Particle Physics), systematically scaling from current proof-of-concept models to trillion-parameter architectures capable of autonomous discovery. Our approach advances three critical frontiers: unified data infrastructure integrating detector data, scientific knowledge and computational tools across global facilities; multi-facility foundation models enabling cross-experiment knowledge transfer and accelerated discovery; and agentic AI capabilities for reasoning and autonomous tool use. The resulting self-evolving FM4NPP will transform physics research by converting time-intensive data analysis, theory derivation and computational bottlenecks into rapid AI–human collaborative discovery. This paradigm shift promises to fundamentally accelerate scientific progress in nuclear and particle physics, enabling researchers to focus on high-level insights while AI handles routine analysis and explores vast parameter spaces beyond human capacity.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Kriging-Based Inverse Uncertainty Quantification of BISON Fission Gas Release Model
Nuclear reactor fuel performance analysis studies the thermo-mechanical behavior of fuel rods and verify their compliance with safety criteria under both normal operation and accidental conditions. One of the major concerns in fuel performance analysis is the behavior of the fission gases xenon and krypton in uranium dioxide fuel, which significantly affect the thermo-mechanical performance of the nuclear fuel rods employed in current LWRs. Previously, the modeling of fission gas release (FGR) in nuclear fuel performance codes have been relying on empirical models which are not applicable beyond their range of calibration. Recently a more efficient and flexible physics-based FGR and swelling model that can describe a wider range of reactor operation conditions have been implemented in fuel performance code BISON. In this work, we performed Bayesian calibration to inversely quantify the uncertainties associated with the 5 tuning parameters in BISON FGR model, based on the time-dependent FGR measurement data from Riso-AN3 benchmark. It takes around 1 hour and 40 minutes for BISON to simulate the Riso-AN3 benchmark with a moderate mesh size using 32 processors. As hundreds of thousands of BISON simulations are needed during Markov Chain Monte Carlo (MCMC) sampling, running BISON directly is not a viable solution. Therefore we propose to use Kriging-based metamodels to replace BISON during MCMC sampling. Surrogate model is an approximation of the input/output relation of a computer code/model. It is also called metamodel, response surface, emulator. Surrogate models usually take much less computational time than the original model (like BISON) while maintaining the input/output relation of the original model to a desirable accuracy. Constructing the metamodels normally requires small number of executions of the original model. Once validated, metamodels can be used to perform uncertainty and sensitivity analysis, Bayesian calibration, optimization, etc.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Validating a Dynamic PWR Safety and Security Model?
Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.
98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗