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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

FISPACT-II: An Advanced Simulation System for Activation, Transmutation and Material Modelling

Fispact-II is a code system and library database for modelling activation-transmutation processes, depletion-burn-up, time dependent inventory and radiation damage source terms caused by nuclear reactions and decays. The Fispact-II code, written in object-style Fortran, follows the evolution of material irradiated by neutrons, alphas, gammas, protons, or deuterons, and provides a wide range of derived radiological output quantities to satisfy most needs for nuclear applications. It can be used with any ENDF-compliant group library data for nuclear reactions, particle-induced and spontaneous fission yields, and radioactive decay (including but not limited to TENDL-2015, ENDF/B-VII.1, JEFF-3.2, JENDL-4.0u, CENDL-3.1 processed into fine-group-structure files, GEFY-5.2 and UKDD-16), as well as resolved and unresolved resonance range probability tables for self-shielding corrections and updated radiological hazard indices. The code has many novel features including: extension of the energy range up to 1 GeV; additional neutron physics including self-shielding effects, temperature dependence, thin and thick target yields; pathway analysis; and sensitivity and uncertainty quantification and propagation using full covariance data. The latest ENDF libraries such as TENDL encompass thousands of target isotopes. Nuclear data libraries for Fispact-II are prepared from these using processing codes PREPRO, NJOY and CALENDF. These data include resonance parameters, cross sections with covariances, probability tables in the resonance ranges, PKA spectra, kerma, dpa, gas and radionuclide production and energy-dependent fission yields, supplemented with all 27 decay types. All such data for the five most important incident particles are provided in evaluated data tables. The Fispact-II simulation software is described in detail in this paper, together with the nuclear data libraries. The Fispact-II system also includes several utility programs for code-use optimisation, visualisation and production of secondary radiological quantities. Included in the paper are summaries of results from the suite of verification and validation reports available with the code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data

Spent fuel monitoring and characterization has been a pillar of safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting of large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also in for nuclear power facilities, as accounting can be a long and arduous process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel, or a partial defect detection. A second method, Gamma Emission Tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. This work identifies deficiencies in both methods, and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%respectively for DCVD and GET data on their own. Finally, these classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real-time while also maintaining the information barrier between output and measurement desired in safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often performed manually and on an as-needed basis to achieve a targeted cost saving. A holistic approach to the integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data warehouse results in direct and indirect cost saving because it automates the manual search for data, enables sharing and comparison of data from various tools of a single or multiple plants, enable digital transformation of data, increase frequencies of needed data that are sparse in nature (such as failures signatures), reduces the need for training on various tools for plant staff, enables a holistic staff perception of plant activities, reveals cost-saving opportunities, and improves the visual perception of all parts a plant necessary for operations and maintenance. One of the main challenges to deploy a data warehouse is associated with the integration of data sources into a consistent data model using a standard ontology that is developed by and for the nuclear power industry. This model is non-existent and is a necessity to enable the nuclear data warehouse role. A data model contains objects that make up a given domain along with their attributes and relationships. It provides domain standardization and maintainability, setting forth the objects, attributes, and relationships within that domain and acting as the guide by which the various applications within that domain will structure their data and interchange. An ontology is defined as “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them”. Ontologies consist of a taxonomy or directed rooted tree. A survey of the models and ontologies available to support a data integration aggregated model and ontology for nuclear deployment (DIAMOND) was performed and different standards that address specific aspects of the needed model and ontology were leveraged and integrated in this effort. In specific, the Basic Formal Ontology (BFO) and Lifecycle Modeling Language (LML) were leveraged for the base ontology. The ISO 4224 and 15926 were leveraged in the development of the overlapping nuclear and fossil data aspects. The electric power research institute (EPRI) power distribution common information model (CIM) was also reviewed and the experience gained was leveraged in the power generation scope of DIAMOND. The initial aim targeted by this effort was to develop the ontology definitions and model structure. This report documents the resulting model and ontology to develop DIAMOND. The report describes the various elements of the model development based on a growing tree development approach, i.e. DIAMOND’s tree structure is established with the core data aspects and is populated with a preliminary level of details. Fifteen sources of data in a nuclear power plant were considered and incorporated into DIAMOND at this stage of development, representing the majority of operations data sources in nuclear power plants. Additional data sources, scope, and a higher level of fidelity will be incorporated into DIAMOND next in collaboration with the nuclear industry and relevant stakeholders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

An Evidence-Based Critique of U.S. Nuclear Waste Policy Using Data from Reactor and Cyclotron Sites - 18568

Concrete at nuclear facilities becomes radioactive when it is exposed to a neutron flux, and when such facilities are vacated, decommissioning activities are enacted to make the site safe for reuse. These activities generate wastes that the public expects to be managed in their best interest. This paper provides side-by-side comparisons of concrete bio-shielding encountered at typical reactor and cyclotron decommissioning projects. Three sites are considered, and results from characterization samples are used to demonstrate that the wastes generated by such sites are similar in terms of content and risk. First, the dose potential represented by each waste is determined from mass-based radioactivity concentration with the RESRAD-BUILD computer code. The resulting doses are translated into population-risk estimates by applying dose-response predictions from the National Academy of Sciences' 2006 BEIR VII report. In terms of possible adverse health outcomes, the risk of solid cancer or leukemia associated with the wastes this paper examines is 8 to 50 extra cases per 100,000 persons. The regulatory framework applicable to each site is also reviewed, and the waste management strategies used at each site are contrasted. This paper demonstrates that the wastes it examines are virtually indistinguishable. Contrary to this finding, the wastes are regulated, managed, and disposed in very different manners according to current U.S. nuclear waste policy. The value of this paper is that the side-by-side comparisons it presents provides an opportunity to examine the risk-benefit of existing policies in terms of the benefits or disparities they represent. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

TNSL support in GNDS 2.0 and beyond [Slides]

This presentation begins by discussing the TNSL format options that went through a major overhaul in GNDS-2.0 and it examines the changes in 2.0. Additionally, it discusses three issues with further changes that should be considered. The first issue is that the project needs some guidance on what to expect when evaluations are performed with coherent inelastic. The second issue is that GNDS-2.0 does not provide a way to clearly specify in the evaluation how to switch to ‘standard’ incident neutron evaluations for energies or temperatures outside the TNSL domain. The third issue is that when GNDS-2.0 was designed, it was assumed that S(α,β) would always be given on a uniform interpolation grid. The presentation concludes by discussing how New JENDL-5 TNSL evaluations have some complications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

The problem analysis of data processing performance in nuclear power plant computerized information and control system - 288

This paper is based on the data processing performance problems of operating command delay and system display not updating real-time for nuclear power plant computerized information and control system. The plant computerized information and control system is responsible for monitoring and controlling the plant operation, and it is a key part in the plant DCS system. Its data processing performance will affect the operation stability of nuclear power plant. The architecture, hardware configuration and data communication for plant computerized information and control system among the different levels in DCS are described. The main reasons affecting data processing performance in different signal channels are analyzed according to the plant load test results. The CPU load curves for real time server and central processing server are displayed, although the detailed cause for operating command delay and system display not updating realtime are not found, central processing server load is heavier than real time server which remind us to analyze the data processing of software resource scheduling deeply. So the resource distribution problem of software execution task is also analyzed according to CPU load test results. This paper provides the optimization analysis for data processing performance, and it is available and effective according to the latest test results. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Implementation of TSL Evaluations Beyond the Incoherent Approximation [Slides]

This presentation discusses the motivation behind the project, which is to facilitate the production of TSL libraries that contain high fidelity physics including coherent inelastic effects, such as graphite and beryllium. High fidelity TSL libraries are of interest in various applications, for example in relation to neutron scattering instrument response analysis and directional biases (reflection) in neutronic analysis. In summation, the removal of typical approximations, in terms of incoherent, cubic, and atom site are examined. The application of generalized equations to crystalline graphite and beryllium TSL evaluations and the validation of data and data formats is also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Applications of Integral Benchmark Data

The International Reactor Physics Experiment Evaluation Project (IRPhEP) and the International Criticality Safety Benchmark Evaluation Project (ICSBEP) provide evaluated integral benchmark data that may be used for validation of reactor physics / nuclear criticality safety analytical methods and data, nuclear data testing, advanced modeling and simulation, and safety analysis licensing activities. The handbooks produced by these programs are used in over 30 countries. Five example applications are presented in this paper: (1) Use of IRPhEP Data in Uncertainty Analyses and Cross Section Adjustment, (2) Uncertainty Evaluation Methods for Reactor Core Design at JAEA Using Reactor Physics Experimental Data, (3) Application of Benchmarking Data to a Broad Range of Criticality Safety Problems, (4) Cross Section Data Testing with ICSBEP Benchmarks, and (5) Use of the International Handbook of Evaluated Reactor Physics Benchmark Experiments to Support the Power Industry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Fast Neutron Evaluations for Advanced Lead Systems [Slides]

This presentation touches on fast neutron evaluations for advanced lead systems. This lecture covers project goals to evaluate lead (Pb) isotopes with a focus on improving predictive capabilities of fast systems. Additionally, the project sets out to benchmark available critical and shielding experiments with sensitivity to Pb and utilize RPI Quasi-Differential Scattering experiment for scattering reactions. This project will perform Resolved Resonance Region (RRR) and fast region evaluations for natural Pb isotopes with differential data on EXchange FORmat for experimental numerical nuclear reaction data (EXFOR) and ultimately will submit evaluations to the ENDF/B-VIII.1 library.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2013

A triggerless digital data acquisition system for nuclear decay experiments

In nuclear decay experiments an important goal of the Data Acquisition (DAQ) system is to allow the reconstruction of time correlations between signals registered in different detectors. Classically DAQ systems are based in a trigger that starts the event acquisition, and all data related with the event of that trigger are collected as one compact structure. New technologies and electronics developments offer new possibilities to nuclear experiments with the use of sampling ADC-s. This type of ADC-s is able to provide the pulse shape, height and a time stamp of the signal. This new feature (time stamp) allows new systems to run without an event trigger. Later, the event can be reconstructed using the time stamp information. In this work we present a new DAQ developed for {beta}-delayed neutron emission experiments. Due to the long moderation time of neutrons, we opted for a self-trigger DAQ based on commercial digitizers. With this DAQ a negligible acquisition dead time was achieved while keeping a maximum of event information and flexibility in time correlations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗