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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

MINING NUCLEAR TRANSIENT DATA THROUGH SYMBOLIC CONVERSION

Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

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↗
DOE OSTI2022

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↗
DOE OSTI2011

Resonance Region Cross-Section Data Advancements for Nuclear Criticality Safety Applications and ENDF/B-VII.1

ORNL has completed new resonance region cross-section evaluations with covariance data to support nuclear criticality safety applications. This paper summarizes some of the most recent cross-section evaluations that have been completed and will be made available to process into nuclear data libraries for use with radiation transport codes. Specifically, the paper summarizes the work that has been completed for 35 Cl, 37 Cl, 50 Cr, 52 Cr, 53 Cr, 54 Cr, 39 K, 41 K, 19 F, 55 Mn, 58 Ni, 60 Ni, 235 U, 238 U, and 239 Pu. Many of the new cross-section evaluations are based on new differential data measurements in the resonance region. Furthermore, corresponding covariance data analyses have been performed in conjunction with the resonance evaluation effort thereby providing a consistent set of covariance data to complement the resonance region cross-section evaluations. As a result, the new cross-section evaluations with covariance data can be used to support sensitivity/uncertainty analyses for criticality safety applications.

19-F↗
DOE OSTI2020

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↗
DOE OSTI2025

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↗
DOE OSTI2011

Prompt fission neutron spectra from fission induced by 1 to 8 MeV neutrons on {sup 235}U and {sup 239}Pu using the double time-of-flight technique

Prompt fission neutron spectra from {sup 235}U and {sup 239}Pu were measured for incident neutron energies from 1 to 200 MeV at the Weapons Neutron Research facility (WNR) of the Los Alamos Neutron Science Center, and the experimental data were analyzed with the Los Alamos model for the incident neutron energies of 1-8 MeV. A CEA multiple-foil fission chamber containing deposits of 100 mg {sup 235}U and 90 mg {sup 239}Pu detected fission events. Outgoing neutrons were detected by the Fast Neutron-Induced {gamma}-Ray Observer array of 20 liquid organic scintillators. A double time-of-flight technique was used to deduce the neutron incident energies from the spallation target and the outgoing energies from the fission chamber. These data were used for testing the Los Alamos model, and the total kinetic energy parameters were optimized to obtain a best fit to the data. The prompt fission neutron spectra were also compared with the Evaluated Nuclear Data File (ENDF/B-VII.0). We calculate average energies from both experimental and calculated fission neutron spectra.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

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↗
DOE OSTI2015

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↗