Nuclear Fission Data: from Fundamental Science to Applications
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Talou, Patrick↗
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Results for “Nuclear data”
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FITNESS FOR PURPOSE: OVERVIEW OF ASTM GUIDES FOR QUALITY ASSURANCE OF DATA FROM NUCLEAR ANALYTICAL LABORATORIES
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Brisson, Mike↗
Status of (α,n)-reaction data for nuclear safeguards
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Favalli, Andrea↗
Monte-Carlo Simulations of the Nuclear Energy Deposition Inside the CARMEN-1P Differential Calorimeter Irradiated into OSIRIS Reactor
The nuclear heating measurements in Material Testing Reactors (MTRs) are crucial for the study of nuclear materials and fuels under irradiation. The reference measurements of this nuclear heating are especially performed by a differential calorimeter including a graphite sample material. These measurements are then used for other experimental conditions in order to predict the nuclear heating and thermal conditions induced in the irradiation devices. Nuclear heating is a great deal of interest at the moment as the measurement of such heating is an important issue for MTRs reactors. This need is especially generated by the new Jules Horowitz Reactor (JHR), under construction at CEA/Cadarache 'French Alternative Energies and Atomic Energy Commission'. This new reactor, that will be operational in late 2019, is a new facility for the nuclear research on materials and fuels. Indeed the expected nuclear heating rate is about 20 W/g for nominal capacity of 100 MW. The present Monte Carlo calculation works belong to the IN-CORE (Instrumentation for Nuclear radiation and Calorimetry On line in Reactor): a joint research program between the CEA and Aix- Marseille University in 2009. One scientific aim of this program is to design and develop a multi-sensors device, called CARMEN, dedicated to the measurements of main physical parameters simultaneously encountered inside JHR's experimental channels (core and reflector) such as neutron fluxes, photon fluxes, temperature, and nuclear heating. A first prototype was already developed. This prototype includes two mock-ups dedicated respectively to neutronic measurements (CARMEN-1N) and to photonic measurements (CARMEN-1P) with in particular a specific differential calorimeter. Two irradiation campaigns were performed successfully in the periphery of OSIRIS reactor (a MTR located at Saclay, France) in 2012 for nuclear heating levels up to 2 W/g. First Monte Carlo calculations reduced to the graphite sample of the calorimeter were carried out. A preliminary analysis shows that the numerical results overestimate the measurements by about 20 %. A new approach has been developed in order to estimate the nuclear heating by two methods (energy deposition or KERMA) by considering the whole complete geometry of the sensor. This new approach will contribute to the interpretation of the irradiation campaign and will be useful to improve the out-of-pile calibration procedure of the sensor and its thermal response during irradiations. The aim of this paper is to present simulations made by using MCNP5 Monte-Carlo transport code (using ENDF/B-VI nuclear data library) for the nuclear heating inside the different parts of the calorimeter (head, rod and base). Calculations into two steps will be realized. We will use as an input source in the model new spectra (neutrons, prompt-photons and delayed-photons) calculated with the Monte Carlo code TRIPOLI-4{sup R} inside different experimental channels (water) located into the OSIRIS periphery and used during the CARMEN-1P irradiation campaign. We will consider Neutrons- Photons-Electrons and Photons-Electrons modes. We will begin by a brief description of the differential-calorimeter device geometry. Then the MCNP5 model used for the calculations of nuclear heating inside the calorimeter elements will be introduced. The energy deposition due to the prompt-gamma, delayed-gamma and neutrons, the neutron-activation of the device will be considered. The different components of the nuclear heating inside the different parts of the calorimeter will be detailed. Moreover, a comparison between KERMA and nuclear energy deposition estimations will be given. Finally, a comparison between this total nuclear heating Calculation and Experiment in graphite sample will be determined. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Reactor physics benchmark experiments at the JSI TRIGA MARK II reactor - Current status and future outlook
Full text of publication follows. With the development of new high-fidelity computational methods, improvement of nuclear data, and multiphysics modelling, there is an increased need for benchmark experiments to experimentally validate the models, methods and input data. Many of the nuclear facilities designed to perform reactor physics benchmark experiments have been shut down. Therefore, research reactors offer a great opportunity for benchmark experiments, if they are well designed and performed with great care and accuracy. In this presentation we provide an overview of the past and ongoing activities related to benchmark experiments at the Jozef Stefan Institute TRIGA Mark II research reactor. The following experiments have been performed: criticality with fresh fuel, {sup 197}Au(n,γ) and {sup 27}Al(n,α) reaction rates in irradiation channels, absolute and relative {sup 197}Au(n,γ), {sup 235}U(n,f) and {sup 238}U(n,f) reaction rates in the core, burnup, kinetic parameters, control rod worth, isothermal reactivity coefficient, self-shielding, slow and fast (pulse) transients, nuclear heating, delayed and prompt gamma ray production, temperature profiles for multi-physics. Since the existing fleet of research reactors is ageing very rapidly and new experiments are needed, new research reactors should be designed and built to meet the needs of future advanced reactors, education and training, and other technologies in the coming years. We will review planned activities at the JSI TRIGA reactors and plans for the new research reactor in Slovenia. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
GALE Insights on Nuclear-Powered Data Centers: Implications for SMR and Microreactor Deployment
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Hauck, Gale [ORNL] (ORCID:0000000290065339)↗
Status of FUDGE [Slides]
This presentation discusses the nuclear data management code FUDGE (For Updating Data and Generating Evaluations). While FUDGE is designed to support GNDS, the presentation also states that ENDF-6 and ENDL data files are also supported but must first be translated into GNDS; translators are included with FUDGE. It also states that FUDGE supports plotting, manipulating, checking for physical content, resonance reconstruction, Doppler broadening, etc. FUDGE also supports processing for Monte Carlo and deterministic transport. FUDGE is open source, and the latest public release was FUDGE-4.2.3 with support for GNDS-1.9. In summation, FUDGE capabilities include translating older formats into GNDS, translating GNDS back to ENDF-6, and visualizing, modifying, checking, and processing GNDS data. It also states that a new version of FUDGE is coming soon. The plan is to support GNDS-2.0 specification, but it may be released sooner if v2.0 is delayed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NDMAS Data Management Capabilities for Metallic Fuels
This poster summarizes the capabilities of the Nuclear Data Management and Analysis System and its applicability to the Metallic Fuels program in the Advanced Fuel Campaign.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Technical Language Processing of Nuclear Power Plants Equipment Reliability Data
Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.
97 MATHEMATICS AND COMPUTING↗
Uncertainty Quantification of Calculated Temperatures for the AGR 3/4 Experiment
A series of Advanced Gas Reactor (AGR) irradiation experiments are being conducted within the Advanced Reactor Technology (ART) Fuel Development and Qualification Program. The main objectives of the fuel experimental campaign are to provide the necessary data on fuel performance to support fuel process development, qualify a fuel design and fabrication process for normal operation and accident conditions, and support development and validation of fuel performance and fission product transport models and codes (PLN 3636, “Technical Program Plan for INL Advanced Reactor Technologies Technology Development Office/Advanced Gas Reactor Fuel Development and Qualification Program”). The AGR 3/4 test was inserted in the Northeast Flux Trap position in the Advanced Test Reactor (ATR) core at Idaho National Laboratory (INL) in December 2011 and successfully completed irradiation in mid-April 2014, resulting in irradiation of the tristructural isotropic (TRISO) fuel for 369.1 effective full-power days (EFPDs) during approximately 2.4 calendar years. The AGR 3/4 data, including the irradiation data and calculated results, were qualified and stored in the Nuclear Data Management and Analysis System (NDMAS). To support the U.S. TRISO fuel performance assessment and to provide data for validation of fuel performance and fission product transport models and codes, the daily as run thermal analysis has been performed separately on each of twelve AGR 3/4 capsules for the entire irradiation as discussed in ECAR-2807, “AGR 3/4 Daily As Run Thermal Analyses”. The ABAQUS code’s finite element-based thermal model predicts the daily average volume average (VA) fuel temperature (FT), peak FT, and graphite matrix, sleeve, and sink temperature in each capsule. The JMOCUP simulation codes were also created to perform depletion calculations for the AGR 3/4 experiment (ECAR-2753, “JMOCUP As-Run Daily Physics Depletion Calculation for the AGR 3/4 TRISO Particle Experiment in ATR Northeast Flux Trap”). This depletion analysis provides fast fluence and fission heat rate data for all components (fuel compacts, graphite rings, stainless steel retainer, etc.), which are used as inputs for the thermal analysis codes. The graphite temperatures from thermocouples (TCs) in each capsule were used to calibrate these thermal analysis codes. However, given the high rate of TC failure under the harsh irradiation and thermal conditions in the AGR capsules, the thermal analysis results are very useful in aiding TC data qualification, increasing the confidence in delineating failures of the measuring instruments (TCs) from physical mechanisms that may have shifted the system thermal response.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Informing nuclear physics via machine learning methods with differential and integral experiments
Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Discovery of Nuclides Project
The Discovery of Nuclides project compiles detailed discovery information for all naturally occurring and artificially produced nuclides. The short abstracts for each nuclide include the authors, institution, country, year, method of discovery, and reference. The database contains over 3300 nuclides and is continuously updated as new nuclides are discovered. The work is performed at the Facility for Rare Isotope Beams (FRIB) and is supported by the U.S. Department of Energy, Office of Nuclear Physics under Grant No. DE-SC0016948 and Michigan State University. The FRIB Nuclear Data group is responsible for coordinating and updating the database.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
GNDS v2.0 release and future developments [Slides]
This presentation begins by discussing the major changes in v2.0 of GNDS and that the GNDS-2.0 formatted libraries are now available. It also examines how code support for GNDS is growing with processing codes, APIs and transport codes, and evaluation/testing codes. The presentation states that while migrating to GNDS can be a major effort it worthwhile, especially for anyone who is just getting started as nuclear data creator or user. Additionally, this presentation outlines the established milestones that the EG-GNDS (the group in charge of GNDS specs) has for the next GNDS release and elaborates on the first goal, which is to migrate GNDS specifications to the new JSON schema language.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Uncertainty Quantification of Calculated Temperatures for the U.S. Capsules in the AGR-2 Experiment
A series of Advanced Gas Reactor (AGR) irradiation experiments are being conducted within the Advanced Reactor Technology (ART) Fuel Development and Qualification Program. The main objectives of the fuel experimental campaign are to provide the necessary data on fuel performance to support fuel process development, qualify a fuel design and fabrication process for normal operation and accident conditions, and support development and validation of fuel performance and fission product transport models and codes (PLN-3636). The AGR-2 test was inserted in the B-12 position in the Advanced Test Reactor (ATR) core at Idaho National Laboratory (INL) in June 2010 and successfully completed irradiation in October 2013, resulting in irradiation of the TRISO fuel for 559.2 effective full power days (EFPDs) during approximately 3.3 calendar years. The AGR-2 data, including the irradiation data and calculated results, were qualified and stored in the Nuclear Data Management and Analysis System (NDMAS) (Pham and Einerson 2014). To support the U.S. TRISO fuel performance assessment and to provide data for validation of fuel performance and fission product transport models and codes, the daily as-run thermal analysis has been performed separately on each of four AGR-2 U.S. capsules for the entire irradiation as discussed in (Hawkes 2014). The ABAQUS code’s finite element-based thermal model predicts the daily average volume-average fuel temperature and peak fuel temperature in each capsule. This thermal model involves complex physical mechanisms (e.g., graphite holder and fuel compact shrinkage) and properties (e.g., conductivity and density). Therefore, the thermal model predictions are affected by uncertainty in input parameters and by incomplete knowledge of the underlying physics leading to modeling assumptions. Therefore, alongside with the deterministic predictions from a set of input thermal conditions, information about prediction uncertainty is instrumental for the ART program decision-making. Well defined and reduced uncertainty in model predictions helps increase the quality of and confidence in the AGR technical findings.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal
Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The value of archived data
The rapid progress of atomic and nuclear physics in the twentieth century altered the way scientific results are documented, preserved and disseminated. Boris Pritychenko discusses how atomic and nuclear data tables have become a central resource for the community.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Validation of Thermal Scattering Laws for Light Water at Elevated Temperatures with Diffusion Experiments
Light water is the most important neutron moderator in many reactor applications. Thermal neutron scattering kernels for H bound in H 2 O are represented by ENDF-format thermal scattering laws (TSLs) evaluated at discrete temperatures T . Nuclear data evaluations are conventionally validated utilizing a combination of critical benchmarks and experimental cross section data. Existing public critical benchmarks may be inadequate for testing water TSLs over the full range of T of interest in reactor applications, and experimental thermal scattering cross section data for water is sparse at elevated T . In this work, MC21 is used to simulate the decay of the equilibrium thermal neutron flux in light-water spheres of several radii. The fundamental-mode time eigenvalue $\mathcal{a}$ is calculated at 22 °C and 227 °C (at saturation pressure) for each sphere using three different H-H 2 O TSLs. Fitting $\mathcal{a}$ to a polynomial function of geometric buckling allows calculation of the thermal neutron diffusion length L . Extrapolation lengths are treated as a function of the transport mean free path and geometry. Experimental L data from 25 publications at 49 temperatures (from 10 °C to 295 °C), computed by several different time-dependent and space-dependent decay methods, is used to develop an empirical fit of L vs. T . The MC21-calculated L for the TSLs tested are within ±1% of the predicted values at 22 °C and 227 °C. This validation approach, which may be repeated at arbitrary T , constitutes an integral benchmark specific to and characterizing the detailed physics of the thermal scattering kernel applied.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Preliminary Development of a Nuclide Inventory Code for HTGR
Nuclide inventory calculation for a reactor core is the basis of radiation protection, shielding design and radioactive environmental impact assessment of the power plant. HTR-PM (high-temperature gas-cooled reactor pebble-bed module) under construction in China is one of the candidates for Gen-IV advanced nuclear power system.1 In the design stage of HTR-PM, KORIGEN has been utilized in the inventory calculation.2 However, the current version of KORIGEN was developed several decades ago. In order to improve the design capability of the analysis code package for HTR-PM, a new computer code for nuclide inventory calculation called NUIT (Nuclide Inventory Tool) is being developed based on the DEPTH3 code. The NUIT code tries to utilize accurate and efficient burnup solvers, incorporate well-validated and most recent nuclear data libraries, and provide user-friendly IO interface. This paper presents the preliminary work in the development of NUIT.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗