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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The advanced characterization, post-irradiation examination, and materials informatics for the development of ultra high-burnup annular U-10Zr metallic fuel

U-Zr metallic fuel is a promising fuel candidate for Gen Ⅳ fast spectrum reactors. Previous experimental irradiation campaigns showed that the sodium thermal bonded U-10Zr fuel design can achieve a burnup of 10% fissions per initial heavy metal atom (FIMA). Advanced metallic fuel designs are pushing the burnup limit to 20% or even 30% FIMA. To achieve the higher burnup and eliminate the pyrophoric sodium, a prototypical annular fuel has been designed, fabricated, clad with HT-9 in the Materials and Fuels Complex, and irradiated in the Advanced Test Reactors of Idaho National Laboratory (INL) to a peak burnup of 3.3% FIMA. During irradiation, the mechanical contact between fuel and cladding acts as a thermal bond. The irradiation lasted for 132 days in the reactor. Recently, the archived fresh and irradiated fuel samples were characterized using advanced characterization capabilities in the Irradiated Materials Characterization Laboratory (IMCL) of INL. This article summarizes the results of advanced characterization and computer vision-based materials informatics to reveal the irradiation effects on U-Zr metallic fuel. Future work will focus on further implementation of advanced characterization and statistical data mining to improve the fidelity of fuel performance modeling and support U-Zr metallic fuel qualification for fast spectrum reactors.

Yao, Tiankai↗
NASA NTRS1983

Development of a Polysilicon Process Based on Chemical Vapor Deposition of Dichlorosilane in an Advanced Siemen's Reactor

Dichlorosilane (DCS) was used as the feedstock for an advanced decomposition reactor for silicon production. The advanced reactor had a cool bell jar wall temperature, 300 C, when compared to Siemen's reactors previously used for DCS decomposition. Previous reactors had bell jar wall temperatures of approximately 750 C. The cooler wall temperature allows higher DCS flow rates and concentrations. A silicon deposition rate of 2.28 gm/hr-cm was achieved with power consumption of 59 kWh/kg. Interpretation of data suggests that a 2.8 gm/hr-cm deposition rate is possible. Screening of lower cost materials of construction was done as a separate program segment. Stainless Steel (304 and 316), Hastalloy B, Monel 400 and 1010-Carbon Steel were placed individually in an experimental scale reactor. Silicon was deposited from trichlorosilane feedstock. The resultant silicon was analyzed for electrically active and metallic impurities as well as carbon. No material contributed significant amounts of electrically active or metallic impurities, but all contributed carbon.

Arevidson, A. N.↗
DOE OSTI2022

An experimental and kinetic modeling study of the pyrolysis of isoprene, a significant biogenic hydrocarbon in naturally occurring vegetation fires

Isoprene dominates the carbon flux emitted by vegetation and constitutes 40% of non-methane biogenic emissions worldwide. Despite pyrolysis experiments at temperatures above 1000 K showing a link between isoprene combustion and aromatic species formation, comprehensive mechanistic research on isoprene is scarce in the literature. Here, in this work, we carry out an experimental and theoretical study to build, for the first time, a chemical kinetic model describing isoprene pyrolysis. The formation of polycyclic aromatic hydrocarbon (PAH) precursor species, often observed in vegetation fire plumes, is partially explained by isoprene pyrolysis experiments and theoretical modeling. Molecular dynamics (MD) simulations unveil reaction pathways from allylic isoprenyl radicals to allene and cyclopentadiene (CPD) intermediates, two relevant species detected in the experiments. Rate constants for these identified pathways are calculated using variational transition state theory to update the kinetic model, which is validated against single-pulse shock tube (SPST), and jet-stirred reactor (JSR) experimental data in the temperature range of 850–1690 K. The kinetic model presents satisfactory agreement with the SPST experimental data, and a reaction pathway analysis shows that association of propargyl radicals results in benzene formation. The JSR pathway analysis also identifies the prominent reactions for CPD, benzene, styrene, and toluene formation. Our model does not reproduce the CPD experimental profiles, indicating that additional studies are necessary. Overall, our findings advance the understanding of isoprene pyrolysis and its related atmospheric pollutants in naturally occurring vegetation fires where smoldering and oxygen-deficient combustion processes are present.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Examination of Ac-225 production from Ra-226 using fast reactor JOYO

In this study, the authors investigated the method of producing the radionuclide Ac{sup 225} used for targeted alpha therapy (TAT). Currently, Ac{sup 225} is mainly generated from ORNL's Th{sup 229} generator, and the annual production amount is limited to about 63 GBq, and methods for generating it using accelerators are under development in each country. The method using an accelerator has the advantage of being able to generate Ac{sup 225} from a small amount of target nuclides with high efficiency but has the disadvantage of not being able to irradiate a large amount of target nuclides at once due to the small irradiation area. Therefore, the authors investigated a method to generate Ac{sup 225} by neutron irradiation of Ra{sup 226} as a target nuclide using the experimental fast reactor JOYO, which has abundant neutrons and a large loading region. Irradiation of Ra{sup 226} with fast neutrons causes a (n, 2n) reaction to generate Ra{sup 225}, and then decay to produce Ac{sup 225}. In addition, although harmful Ac{sup 227} is also produced at the same time by the (n,γ) reaction, first of all the actinium isotope is chemically separated and eliminated. Since the remaining Ra{sup 225} collapses and Ac{sup 225} is produced, pure Ac{sup 225} can be extracted by performing chemical separation again. As a result of the analysis, 1 g of Ra{sup 226} is irradiated with JOYO for 60 days, and milking is performed 4 times every 17.5 days. By doing these three times a year, it was found that about 50 GBq of Ac{sup 225} was generated. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Model-based real-time surface heat flux and temperature estimation for the DIII-D tokamak

A control-oriented model for monitoring of wall power flux densities on the DIII-D tokamak has been successfully implemented and validated experimentally. Future reactors will have to withstand severe steady state high heat flux loads on plasma-facing components (PFCs). Due to the difficulty of directly-measuring local heat fluxes on these components, monitoring and protection of PFCs during the plasma discharge can benefit from simplified physics-based real-time (RT) functional models to estimate and guide heat load control. As a first step into the development, a control-oriented model for monitoring of wall power flux densities and temperatures on DIII-D tokamak has been successfully implemented. The paper discusses the experimental demonstration and comparison of the 2-D model-based wall heat flux algorithm on the DIII-Dinner wall limiter (IWL) against infra-red camera heat flux measurements for limited plasma configurations. The paper also reports on the benchmarking of the field line tracing environment, SMITER, developed at ITER organization on DIII-D tokamak against experimental IR diagnostic data and the derivation of the component shaping weighting factors for the 2-D model-based approach. Here, the extension of the model-based approach for surface temperature estimation on the DIII-D IWL is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advances in Metallic Fuel Database Development and Data Qualification

The Fuels Irradiation and Physics Database (FIPD [1]) is a comprehensive repository of data and documents related to Uranium-Zirconium based metallic fuel test pins. This database stores operational conditions of these pins, calculated using a suite of Argonne National Laboratory analysis codes developed during the Integral Fast Reactor (IFR) program. Key calculated data include axial distributions of power, temperature, fluence, burnup, and isotopic densities. Additionally, the FIPD holds post-irradiation examination (PIE) data such as fission gas release, gas chemistry measurements, and axial distributions derived from profilometry, gamma scanning, and neutron radiography. Complementing these data is an extensive archive of documents related to various pins and experiments. These include raw PIE records, design details, safety analyses, and operational reports. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The recent improvements to the database and the data QA status are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Specification of FIPD Fission Gas Chemistry Data

The current FIPD library contains two main sets of fission gas chemistry data. The first set is data collected during the Integral Fast Reactor (IFR) program from 1984 to 1994, using a gas mass spectrometry system located in the Analytical Laboratory (AL) at Argonne National Laboratory-West (Argonne-West). Throughout this period, numerous fission gas release and chemistry datasets were gathered from a variety of metallic fuel pins. The fission gas was sampled by the Gas Assay, Sample and Recharge (GASR) System in the Hot Fuel Examination Facility (HFEF) and transferred to the AL to perform gas composition and isotopic abundance analysis. The second set is data collected after the IFR program. The fission gas samples were also collected by the GASR system at the HFEF, but analyzed using a similar gas mass spectrometer located in Pacific Northwest National Laboratory (PNNL). Many fuel pins irradiated in Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF) were measured, including the fuel pins for the MFF series of experiments, designed to qualify metal fuel for use as driver fuel in the FFTF and X496 experiment. For either set of data, fission gas was sampled with the gas sampling line in GASR using sample bottles after the capsule/element volume has been determined and the system is still full of radioactive gas. The sample bottles were then transferred to the sample packaging cylinder or an approved storage location pending transfer to the AL or prepared for shipment to another laboratory (such as PNNL) for analysis of the collected gas as directed on the GASR data form, other approved form. The receiving laboratories (AL or PNNL) required their Analytical Service Request form to be completed prior to sample transfer. Typical sample transfer processes were initiated at HFEF by the principal or process engineer. The laboratories performing the analyses (AL or PNNL) use the sample bottle numbers as well as a sample number produced by the respective laboratory. HFEF and the responsible experimenter tracked the sample using the analysis number, the gas bottle number, and the fuel pin number.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2024

HIGH BURNUP FUEL-COOLANT INTERACTION ANALYSIS SUPPORTING FUEL SAFETY TESTING AT IDAHO NATIONAL LABORATORY

In the near future, experiments on HBu fuel under loss-of-coolant accident (LOCA) and reactivity-initiated accident (RIA) conditions will be performed within the Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL). These experiments will be performed using the Transient Water Irradiation System for TREAT (TWIST) experiment vehicle. To support these experiments, analysis of fuel-coolant interaction (FCI) energetics is underway. This paper discusses FCIs in the context of light water reactor (LWR) safety, differentiating between the severe accident focus of commercial reactors and experimental RIA test programs where FCIs have occurred. However, it is highlighted that as the nuclear industry aims for increased burnup limits, the FCI events observed in RIA test programs may become relevant to commercial LWR safety analysis. The paper then presents developments to the UW-FCI computer program to enable simulation of FCIs initiated by solid fuel particles dispersing into the coolant during RIAs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Development, verification, and validation of comprehensive acoustic fluid-structure interaction capabilities in an open-source computational platform

The acoustic fluid-structure interaction (FSI) formulation is a practical numerical approach for the seismic analysis of fluid-filled tanks. However, there are no verification and validation studies reported in the literature that demonstrate the ability of an acoustic FSI numerical model to predict responses important to structural and mechanical design for intense translational and rotational earthquake inputs. Herein, an acoustic FSI formulation is implemented in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE), and is formally verified and validated using analytical solutions and code-to-code verification, and experimental data, respectively. The analytical solutions are for small amplitude, unidirectional seismic inputs. The code-to-code verification utilizes a previously verified and validated Arbitrary Lagrangian-Eulerian (ALE) numerical model in the commercial finite element code LS-DYNA. The validation studies utilize a comprehensive data set assembled from results of 3D earthquake-simulator tests of a fluid-filled vessel. The acoustic numerical model in MOOSE is verified and validated for hydrodynamic pressures and support reactions except for cases that involve significant convective response. For small amplitude inputs, numerically predicted wave heights match those of the analytical solutions. The numerical model is not verified and validated for wave height calculations under intense 3D seismic inputs. The run times for the acoustic FSI simulations in MOOSE are an order of magnitude, or more, shorter than for the corresponding ALE simulations in LS-DYNA. The utility of the MOOSE acoustic FSI implementation is demonstrated by seismic analysis of a building equipped with a fluid-filled, advanced nuclear reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Engineering & Microstructure Scale PIE Report on EBR-II X441A Metallic Fuel Pins for the MORPH Experiment

The objectives of this project are to increase fundamental understanding of irradiation induced metallic U-Pu-Zr fuel behavior and to obtain data needed for the development of irradiation models for metallic fuels in MARMOT. The requested metallic fuel pins are from the X441A experiment irradiated in Experimental Breeder Reactor (EBR)-II and will be provided by the PIs for the duration of the experiment. The purpose of the pins irradiated in the X441A assembly was to vary Zr composition and fuel slug diameter in order to provide data for the metallic fuel performance code “LIFEMETAL” [ANL-IFR-125]. The fuels of interest to this project are metallic fuels with several Zr compositions (in wt.%): U-19Pu-6Zr, U-19Pu-10Zr, and U-19Pu-14Zr, which were irradiated to a peak burnup of approximately 11 at.%. The cladding was the same for all three fuel pins (austenitic stainless steel D9), which allows investigation of fuel-cladding interaction (FCI) phenomena. Varying Zr content in fuel pins enables investigation of the effect of Zr on fuel restructuring and fuel-cladding compatibility. Engineering and microstructure scale PIE activities will be focused on investigation of fundamental aspect of fuel performance such as species diffusion and migration, fission product behavior, and constituent redistribution. Obtained microstructural information will be used as the basis for the development of MARMOT models of U-Pu-Zr fuel performance at the mesoscale. Experimental data obtained through this proposal will be used to provide this fundamental understanding which will serve as the foundation of the development of radiation models for U-Pu-Zr in MARMOT. It will also provide a starting point for the design of new experiments to provide data for the validation of these MARMOT models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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 OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Treatment of Problematic Reactive Metal Wastes Using the GeoMelt{sup R} In-Container Vitrification (ICV{sup TM}) Process - 20326

Decommissioning of sodium-cooled reactors and fast reactor technologies has generated a number of reactive metal waste configurations that are problematic to treat and typically lack cost effective treatment methods and disposition options. As a result, Veolia Nuclear Solutions, under contract with Idaho National Laboratory (owned by the U.S. Department of Energy and managed and operated by Battelle Energy Alliance, LLC) demonstrated its GeoMelt{sup R} In-Container Vitrification (ICV){sup TM} technology to safely convert sodium metal to a non-reactive vitrified oxide form. The demonstration project, supported by glass formulation and crucible testing, consisted of a series of ICV{sup TM} melts that processed elemental sodium into stable non-reactive glass. INL is currently implementing GeoMelt{sup R} technology as a means to safely and reliably convert radioactive reactive metal residues that contaminate sodium cooled reactor components into waste forms that comply with existing disposition pathways. Reactive metal wastes require treatment in order to remove the Resource Conservation and Recovery Act (RCRA) reactivity and ignitability characteristics to comply with land disposal restrictions. GeoMelt{sup R}, which is an alternative to other potential treatment approaches, provides a robust approach that chemically converts the reactive metals to an inert oxide while also immobilizing radionuclides in a vitrified waste form with durability equal to or better than vitrified nuclear fuel reprocessing wastes (very robust and inert waste forms). Most other treatment approaches generate hydrogen gas which is problematic. In 2016, Veolia Nuclear Solutions first demonstrated the effectiveness of the GeoMelt{sup R} ICV{sup TM} process in deactivating reactive sodium metal. Crucible, bench-scale, and engineering-scale demonstrations were conducted on several surrogate waste configurations with various ratios of sodium metal and glass formers. Each ratio and configuration demonstrated complete deactivation of the surrogate sodium metal. Follow-on work in 2017 demonstrated the deactivation of reactive sodium by GeoMelt{sup R} ICV{sup TM} at a higher waste loading relative to previously demonstrated work performed in 2016; the higher waste loading optimized glass chemistry while enhancing the economical full-scale treatment of reactive metals. Additionally, follow-on demonstration testing in 2018 and 2019 focused on more complex shapes and other reactive-metals (mocked up Experimental Breeder Reactor II [EBR-II] subassembly, sodium filled heat exchanger, and a can containing sodium potassium alloy) which were all performed at engineering scale. Veolia Nuclear Solutions designed, installed, and commissioned in September 2018, at Perma-Fix Northwest in Richland Washington, a 10-metric ton full-scale GeoMelt unit (GeoMelt{sup R} Richland) for the treatment of reactive metal wastes. As of September 2019, over 900 55-gallon drums containing a total of around 3,500 lb of sodium with low levels of radioactivity have been treated at GeoMelt{sup R} Richland, with resulting glass monoliths disposed at the Nevada National Security Site (NNSS). A full-scale radiological demonstration melt on an actual EBR-II subassembly has also been performed using the full-scale melter in 2019. The GeoMelt{sup R} technology is a proven radioactive waste treatment technology capable of immobilizing radioactive wastes, including bulk rubble such as drums and other steel vessels usually without pretreatment. Utilizing the GeoMelt{sup R} technology to treat reactive metals eliminates pretreatment steps resulting from having to separate the reactive metal from steel containers or jackets as GeoMelt{sup R} can easily operate at temperatures sufficient to melt the steel and expose the reactive metal for treatment. Eliminating handling steps of reactive metals is a significant safety advantage since reactive metals are pyrophoric. The results generated as a part of the 2018-2019 demonstration program are presented in the paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Utilization of ACE nuclear data file toolkit ACEtk to calculate relative sensitivity coefficients of point-kinetics parameters

Sensitivity and uncertainty methods are quintessential for nuclear criticality safety and experiment design. This type of analysis relies on calculations of sensitivity coefficients; sensitivity coefficients of the effective neutron multiplication factor with respect to some nuclear data are predominantly calculated and used. As a part of the Laboratory Directed Research & Development project EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) at Los Alamos National Laboratory, sensitivity coefficients of many radiation detector measurement responses with respect to nuclear data were investigated. Specifically, this paper outlines a method to calculate point-kinetics parameters relative sensitivity coefficients with respect to nuclear data. Point-kinetics parameters such as the prompt neutron decay constant, effective delayed neutron fraction, and neutron generation time are especially important to experimenters and reactor operators designing systems with dynamic neutron populations. This method couples capabilities of the ACE (A Compact ENDF) nuclear data file toolkit, ACEtk, with the ability to load cross sections into the radiation transport code Monte Carlo N-Particle (MCNP). In conclusion, key aspects of optimizing this method for a particular application and sensitivity profiles of the Jezebel criticality experiment are examined and discussed.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Implementation of high-speed data acquisition at DIII-D

Research at the DIII-D National Fusion Facility in San Diego focuses on short pulse plasma discharges that specialize on various shaping profiles. High-speed data collection is a critical component for the operation of many of DIII-D’s diagnostics and is fundamental for capturing high-resolution data used in experimental data analysis. Differing techniques enable the plasma control system (PCS) to perform complex real-time feedback control on microsecond time scales. This work presents a comprehensive overview of data acquisition, focusing on the hardware and software used in reliable data acquisition at DIII-D. The robust nature of the data acquisition system allows for various techniques to coexist seamlessly. However, as modern systems capable of nanosecond resolution become more common, existing architectures will need to be modified. Here, by addressing the key challenges of high-speed data acquisition, DIII-D is able to provide real-time data used in plasma operation and has the ability to acquire high fidelity data needed for future experimental fusion reactors, such as ITER.

Control↗
DOE OSTI2020

A CALPHAD-informed approach to modeling constituent redistribution in Zr-based metallic fuels using BISON

Here, a CALPHAD-informed (Computer Coupling of Phase Diagrams and Thermochemistry) constituent redistribution model was developed for Zr-based metallic fuels and incorporated into the BISON fuel performance code. Three uncertain model parameters associated with β and γ phase kinetics were calibrated using integral test data from U-Zr fuel elements irradiated in Experimental Breeder Reactor II. The calibrated constituent redistribution model was shown to predict the behavior of U-Zr fuels with excellent accuracy. Model predictions for U-Pu-Zr fuels were physically reasonable but less accurate. Reduction of uncertainties in the ternary phase transition temperatures and collection of kinetic data for the ζ phase are expected to improve the model’s ternary predictions. Finally, the new model was coupled to existing thermomechanics models in BISON to simulate irradiation of an entire U-Zr fuel element, demonstrating its ability to accurately predict the behavior of U-Zr fuels with realistic geometries and mesh resolutions at the engineering scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗