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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2020

Sulfur Pellets Responses to a Bare and Steel Reflected Pulse of the Oak Ridge National Laboratory Health Physics Research Reactor

The experiments analyzed in this report were conducted at the Health Physics Research Reactor (HPRR), also known as the $\textit{Fast Burst Reactor}$. The reactor was designed and built at Oak Ridge National Laboratory (ORNL) in 1961. The HPRR was an unmoderated, unshielded fast reactor that used highly enriched uranium and molybdenum alloy as fuel. The reactor was initially sent to the Nevada Test Site in 1962, where it was used to evaluate radiation doses received as a result of the Hiroshima and Nagasaki bombings during World War II. A few years later, the reactor was sent back to ORNL to be part of the Dosimetry Application Research (DOSAR) facility shown in Figure 1, which included a reactor building shown on the left (west) of the picture and a control and laboratory building in the upper right corner (northeast). The critical assembly was used for numerous technical studies, including systems calibration, dosimetry, radiobiology of plants and animals, testing of radiation alarms, as well as teaching and training in radiation dosimetry and nuclear engineering. Between 1963 and 1987, the HPRR was operated for thousands of hours, achieving criticality close to 10,000 times and motivating many publications. The HPRR was decommissioned in 1987. The goal of this effort was to use historical data from operation of the HPRR to create a criticality accident alarm system (CAAS) benchmark to be included in the $\textit{International Handbook of Evaluated Criticality Safety Benchmark Experiments}$ (ICSBEP Handbook). A thorough inspection was performed of all available documentation and information available. The most promising experiments that were selected for evaluation were those described in the 1987 ORNL report entitled $\textit{Health Physics Research Reactor Reference Dosimetry}$, ORNL-6240. The report includes reference dosimetry results of the shielded and unshielded configurations of the HPRR after burst operations. Because of changes to the reactor positioning and storage systems that were made in 1985, the previous dosimetry reports became obsolete, and the newly designed experiments were needed to create the HPRR’s adjusted dosimetry data. The various results reported in ORNL-6240 include reference doses and dose equivalents from different conventions at different distances and elevations as determined using the detected neutron fluence and conversion factors. The HPRR neutron fluence was obtained through different methods, including sulfur pellet analysis and threshold detector unit data. Information about the HPRR spectrum was also obtained through Bonner sphere measurements. This benchmark is focused on a part of the measured sulfur fluences reported in Appendix H of ORNL-6240. Standard commercial sulfur pellets were placed at different distances from the HPRR centerline during burst operation and were activated due to the 32 S(n,p) 32 P reaction. The resulting 32 P activity was then measured and the information about the corresponding sulfur fluence and/or neutron dose could be extracted. Many of those measurements have 7 been performed with the HPRR in its bare configuration or with different shields (combinations of Lucite, concrete, steel). All the necessary, precise information about material and/or dimensions of the different shields was not found, so it was decided to focus only on the unshielded and steel-shielded configurations to minimize the benchmark uncertainty. A total of 31 cases (24 unshielded and 7 shielded cases at different positions) of sulfur fluence were selected before evaluation to develop the benchmark.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ECAR-7514 Rev 0 Analysis of the MARVEL Central Insurance Absorber Actuator Components

This ECAR analyzes the MARVEL Central Insurance Absorber (CIA) rod actuator design. Functions of interest include general vertical position, velocity, and SCRAM performance when carrying the CIA rod payload. Assessed components include the motor, drive train, electromagnet, bearings used for alignment and SCRAM velocity control, limit switches, 2nd position indicator, and resolver.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2025

The Oak Ridge National Laboratory Glenn T. Seaborg Institute: 2025 highlights

The Glenn T. Seaborg Institute (GTSI) at Oak Ridge National Laboratory (ORNL) serves as a dedicated hub for advancing actinide R&D, aimed at strengthening the United States’ capabilities in actinide science. With a focus on medical, industrial, and national security applications, the ORNL GTSI seeks to foster the next generation of scientists and engineers. Collaborating closely with corresponding Seaborg Institutes at Lawrence Livermore National Laboratory (LLNL), Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), and Idaho National Laboratory, the ORNL GTSI leverages its unique strengths. These include unparalleled expertise in isotope production, handling of rare isotopes, and leadership in neutron and computational sciences. Notably, the ORNL GTSI is positioned as the nation’s cornerstone for actinide R&D, capitalizing on the exceptional capabilities of the High Flux Isotope Reactor—the premier reactor for isotope production—and the Hazard Category 2 Radiochemical Engineering Development Center, which supports advanced production, processing, and applications research. Furthermore, the GTSI is committed to serving as a national center of excellence for training across all levels of actinide science education. This report provides an overview of the activities, milestones, and accomplishments of the ORNL GTSI over the past year.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Evaluation of an Accident Tolerant Fuel Leak in the Advanced Test Reactor

Accident Tolerant Fuels (ATF), which are nuclear fuel sources designed to withstand operational irregularities and incidents, have been a topic of interest in the nuclear industry for several decades. Interest in ATF technology surged following the 2011 accident at Fukushima Daiichi in Japan. At the Advanced Test Reactor (ATR), one of Idaho National Laboratory’s (INL) four operating nuclear reactors, the ATF program is a collaborative effort between the national laboratory and various stakeholders within the nuclear industry. This program focuses on the research and development of novel fuel compositions, cladding, and component materials with enhanced accident-resistant properties. During one of ATR’s 60-day operating cycles in 2024, the reactor experienced five unplanned shutdowns. Following the fifth shutdown, radiation monitors detected an increase in radiation levels coming from the loop piping. Subsequent water samples confirmed the cause was a leak of fission products from the ATF experiment, designated as ATF-2C. The source of the leak was identified as the instrumented section of the test train. The primary discussions in this paper are 1) the design of the ATF test train, 2) the operating parameters leading up to and following the detection of the leak, and 3) the quantification and characterization of the released fission products.

Accident Tolerant Fuels↗
DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Health Physics Research Reactor Criticality Accident Alarm System Benchmark Overview [Slides]

This presentation discusses the Health Physics Research Reactor (HPRR) or Fast Burst Reactor (FBR) and how available data from Health Physics Research Reactor (HPRR) operation to can be used to create a benchmark report for inclusion in the ICSBEP, as a Criticality Accident Alarm System (CAAS) shielding benchmark. The evaluation of experimental data is also presented, specifically four experimental candidates are considered of potential value for the benchmark: 1. Neutron source estimation from a HPRR pulse (energy spectrum, fission yield), 2. Threshold Detector Unit (TDU) measurements at different distances from a HPRR pulse, shielded and unshielded, 3. Sulfur pellet activation at different distances from a HPRR pulse, shielded and unshielded, and 4. Total neutron fluence from a HPRR pulse measured by Bonner Sphere Spectrometry, shielded and unshielded. Additionally, a benchmark model overview is discussed as are sample calculation results.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

As-Run Neutronic Calculations of HFIR Mini-Fuel Experiments: Guidelines for Analysts

This report provides guidelines for analysts performing radiation transport calculations for mini-fuel experiments irradiated in the High Flux Isotope Reactor (HFIR). Procedures, practical recommendations, and modeling conventions which are essential to ensuring consistency and high fidelity in as-run simulations have been compiled. The content is intended to be a portion of the official training material for new staff in this area, offering both technical references and practical guidance derived from previous experience. This report is a living document and will be periodically updated to incorporate new developments, methodological improvements, and lessons learned from ongoing and future irradiation campaigns.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Advanced Reactor Supply Chain Assessment (GAIN Report)

Several net-zero scenario evaluations predict a rapid ramp up of nuclear energy in the coming decades. If this materializes, it will most likely strain the supply chains associated with the potential advanced reactor concepts awaiting deployment. To help assess the current and potential capacities of the various advanced reactor supply chains, the Gateway for Accelerated Innovation in Nuclear (GAIN) conducted a survey of companies able to produce components for advanced reactors in the near future (namely for sodium, gas-cooled, and molten salt reactors). Using an aggressive nuclear deployment scenario, the objective was to assess the ability of the various supply chains to meet the considerable demand projections for certain key components (vessels, heat exchangers, pumps, graphite, and sensors) and identify potential challenges. While individual companies were unable to meet the most optimistic nuclear deployment rate projections, it was found that on aggregate, a United States-based supply chain projected that expansion could be ramped up to meet a larger future demand of these components. However, meeting projected demand for several more complex items (namely gas or salt heat exchangers) was found to be more challenging. Deploying new reactors at scale necessitates the production of more and more supply chain components, requiring a ramp up in production. Supply chain companies were surveyed, and respondents appeared less able to meet short term demand (next year) versus longer-term demand projections (5 and 10 years). This reflects the need to obtain orders with adequate lead times (can range from 3 to 30 months). Future demand will need to be met by expanding existing capacity. These expansions will require suppliers to raise capital or secure other types of support (federal loans or grants) to invest in facilities, equipment, and workforce. Individual suppliers indicated financial investments could be in the range of $\$ $100 million to $\$ $1 billion for their own facilities (depending on the type of facility). The biggest risk, according to respondents, related to general uncertainties surrounding the future nuclear industry and whether the potential demand projections will materialize into real demand that is actionable from a business perspective. Businesses do not seem willing to take investments risks without clear orders. If businesses are not able to invest to expand the supply, it will either delay the deployment of advanced nuclear technology, or the supply chain will be met by suppliers outside of the United States. This report only focuses on the domestic supply chain’s ability to meet the various projections stipulated here for the specific assessed components (vessels, heat exchangers, pumps, graphite, and sensors). The report does not cover all reactor designs or all components that may ultimately be needed for any one reactor design. It also does not address whether any specific aspect of the supply chain will be cost competitive in the global market, nor how potential state-backed entities could affect the expansion of a United States-based supply chain. The largest challenges in ramping up capacity among respondents appear to be workforce related. This includes workforce availability, experience, training, and turnover. In addition to facility investment, suppliers will also need to invest heavily in long-term workforce training to meet production goals. This issue is not nuclear-specific, and the expansion of any supply chain will likely face similar challenges. While suppliers evaluated expected normal business demand from other markets outside of nuclear, it is possible that other market segments could expand more than predicted and compete for the same suppliers. One potential market that may compete for the same supplier resources is the United States military, as many of these suppliers support both the commercial nuclear sector as well as the Navy with reactors and components. In summary, suppliers in the United States believe that there is a way to increase production in order to begin meeting the demand which will exist for advanced reactors—as long as appropriate investments can be made in the supply chain in an appropriate timeframe. Based on the capacity projections and lead times, investment will be needed to meet the 5-year and 10-year production targets. Therefore, if significant nuclear deployment is to occur in the 2030s, investment and ramp up of the advanced nuclear supply chain will need to begin in the near future for the United States to successfully deploy these advanced reactors with domestic supply chains.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Cylindricity Sensitivity Thermal Model of the AGR-5/6/7 Experiment in the Advanced Test Reactor

The AGR-5/6/7 experiment is currently being irradiated in the Advanced Test Reactor (ATR) at the Idaho National Laboratory and is approximately 70% complete. Several fuel and material irradiation experiments have been planned for the U.S. Department of Energy Advanced Gas Reactor Fuel Development and Qualification Program, which supports the development and qualification of tristructural isotropic (TRISO) coated particle fuel for use in high-temperature gas-cooled reactors. The goals of these experiments are to provide irradiation performance data to support fuel process development, qualify fuel for normal operating conditions, support development of fuel performance models and codes, and provide irradiated fuel and materials for post-irradiation examination and safety testing. Originally planned and named as separate fuel experiments, but subsequently combined into a single test train, AGR-5/6/7 is testing low-enriched uranium oxycarbide TRISO fuel. The AGR-5/6/7 test train has 5 capsules with thermocouples and independent gas control mixtures. Unique to this paper is a sensitivity study concerning the cylindricity of the graphite holders containing the fuel compacts and their eccentricity in relation to the stainless-steel capsule walls. Each capsule has small nubs on the outside used for centering the graphite holder inside the stainless-steel capsule with a small gas gap used to control temperature. Due to machining tolerances of these nubs and vibration wearing the nubs down when the experiment is running in the reactor, the possibility exists that the holder may move around radially. Each capsule is equipped with several thermocouples placed at various radii and depths within each graphite holder. This paper will show the sensitivity of offsetting the graphite holder for various radii in 45-degree increments around the circle with the objective of minimizing the difference between the measured thermocouples and the modeled thermocouple temperatures. Separate gas mixtures of helium/neon are introduced into this gas gap between the holder and capsule wall and changed as necessary to maintain the desired thermocouple temperatures to keep the fuel compacts at constant temperature as the nuclear reactor conditions change. The goal of the sensitivity study is to find a radius and an angle to offset the holder from perfectly centered for each of the five capsules separately. The complex thermal model includes fission heating, gamma heating, radiation heat transfer, and heat transfer via conduction and radiation across the control gaps. Subroutines linked to the thermal model offer an easy method to offset the graphite holder from the capsule walls without remeshing the entire model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗