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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 811 records · Page 45
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DOE OSTI2020

Safeguards Technical Objectives for Thorium Molten Salt Reactor Fuel Cycles

China, Denmark, France, India, Indonesia, the United Kingdom, and the United States, among others, are exploring commercial thorium fuel cycles for domestic power production and/or international export. For any thorium fuel cycle facilities built and/or operated in a non-nuclear weapon State with a comprehensive safeguards agreement, those facilities and any associated nuclear material would be subject to international nuclear safeguards by the International Atomic Energy Agency (IAEA). Thorium fuel cycle facilities offered for the application of safeguards under a voluntary offer agreement by a nuclear weapon State would also be subject to safeguards, if selected by the IAEA. Compared to uranium/plutonium fuel cycle facilities, thorium fuel cycle facilities are comparatively diverse from an inspection and accounting perspective, differing greatly in material type, form, and design from uranium/plutonium fuel cycle facilities and also from each other. Of these, the most complex thorium fuel cycles – and therefore likely the most challenging to the implementation of international nuclear safeguards – are those that include thorium molten salt reactors. With thorium fuel cycles approaching near-term commercial deployment timelines, it is imperative that the international nuclear safeguards community begins to think about how to adapt existing international nuclear safeguards concepts and approaches to thorium fuel cycles, as well as mitigate any gaps that may exist. This paper focuses on the development of recommended safeguards technical objectives for efficient and effective application of international nuclear safeguards measures (e.g., containment / surveillance, non-destructive assay, destructive assay, environmental sampling) to thorium molten salt reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Gamma ray Imaging Based Methods to Improve the Accuracy of Uranium Holdup Quantification

Deposits in commercial uranium processing facilities of interest to nuclear safeguards are predominantly low enriched uranium (LEU). The shapes and sizes of holdup deposits can vary significantly, thus making it difficult to model the deposits accurately for calibration purposes. This presents a challenge to currently employed methods such as the Generalized Geometry Holdup (GGH) that rely on quantifying 235U mass by simplifying deposit shapes as a point, a line, or an area. In this work, gamma ray imaging using high energy resolution Germanium Gamma Imagers (GeGI™) is employed to determine the distribution of uranium inside the source containment. Two types of imaging methods are employed; coded-aperture imaging and Compton imaging. Gamma-ray emissions seen in different regions of the image are quantified using an inverse gamma-ray transport solver being developed at ORNL. Using measured data, the inverse method will be used to solve for unknown source parameters such as source matrix thickness, density, and attenuation due to container wall and shielding. The intrinsic efficiency of the detector is determined based on a library of built-in response functions and key dimensions of the detector (e.g., thickness and radius of the detector crystal). Measurements are performed in both imaging modalities using uranium sources of well-known masses and enrichments configured inside mocked-up holdup fixtures. Results from the two imaging modalities are intercompared. The paper presents preliminary results of the imaging measurements and the progress made in the computational methods to quantify uranium mass.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Production of National Nuclear Material Archive Subsamples for High Precision Chemical Analysis

With its historical mission as the focal point for production of uranium components for the NNSA nuclear weapons program, the National Nuclear Materials Archive (NNMA) makes use of Y-12's wide range of uranium materials processing knowledge and onsite materials to identify, collect, and preserve sample materials for nuclear forensics purposes. In 2019, Y-12 took on the responsibility for identifying, subsampling, and shipping a total of 24 NNMA samples to the Lawrence Livermore National Laboratory (LLNL) for further forensics analysis. These were highly enriched uranium metal pieces representative of different weapon systems components produced by Y-12 from approximately 1963 to 1993. The purpose was to provide the NNMA program with higher precision analyses of these materials than what is currently available from historical Y-12 production stream data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Phase I Final Report

Explore the source record for details and available documents.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Applications and Deployment of Neutron Scatter Cameras in Nuclear Safeguards Scenarios

Neutron scatter cameras (NSCs) are a type of directionally sensitive neutron detector that rely on two consecutive neutron scattering events to localize a source of neutrons. NSCs can be used to locate, image, and identify unknown neutron sources or verify the geometry and identity of known sources. Much technical progress has been made in improving NSC designs, but little literature exists exploring the full range of practical application of neutron scatter cameras. This paper seeks to identify scenarios related to nuclear security and non-proliferation where deployment of NSCs may be useful. These situations could include: limited searches for sources during cargo screening, counting nuclear warheads for treaty verification, verification of special nuclear material during inspections, imaging nuclear contamination, imaging nuclear reactor cores, searching for lost sources, and matching neutron images in shipper/receiver or inventory management scenarios. These scenarios are examined with respect to existing NSC designs and proposed designs in terms of usefulness and practicality of deployment when compared to currently used detection systems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Using Machine Learning to Track Objects Across Cameras

Video surveillance is one of the most important technologies used by the International Atomic Energy Agency in international safeguards. At large, complicated facilities, multiple surveillance cameras are deployed to monitor the transfer of safeguards-relevant objects across the site. During inspections, all surveillance videos are reviewed to ensure the objects are not manipulated or diverted during transfer, a laborious, time-consuming task. This work describes using deep machine learning algorithms to track objects automatically across multiple cameras, greatly improving the efficiency of the review process. The fundamental problem in this object tracking task across multiple cameras is how to associate the same object, which may show extreme intra-class variations, such as viewpoints, occlusions, and various scales, in different and even non-overlapped cameras. Object re-identification (Re-ID) in nuclear facility video surveillance is even more challenging than classic person or vehicle Re-ID problems because different instances in the same category may display an identical appearance. One observation from nuclear facility surveillance videos is that all objects must be carted (e.g., via forklift) to move. Therefore, the spatial context information of an object, which provides the feature from the carrier, is critical for the object Re-ID task. This work proposes a two-stream convolutional neural networks model that takes features of objects and their surrounding regions into account. Moreover, the custom videos usually are gleaned from different scenes from the training data, which may have extreme variations in illumination changes and/or cluttered backgrounds. Directly applying the trained model to custom videos will dramatically decrease the performance. To tackle this problem, an advanced domain adaptation technique is proposed to mitigate the gap between the data taken from different scenes. The proposed framework will track objects of interest across a nuclear complex. The resulting tracks can be used in further analyses, such as event/activity recognition, anomaly detection, etc.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

IAEA Facility-Level Safeguards and Implementation and Advanced Verification Technologies

IAEA Facility-Level Safeguards and Implementation - This presentation is intended to introduce the audience to the application of international safeguards by the International Atomic Energy Agency at nuclear facilities around the world. It covers the State Level Approach, random statistical sampling processes, material balance area structure, non-destructive assay techniques, and inspection areas. Addition of advanced verification technologies for the IAEA

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Evaluating Safeguards Statistical Assumptions via Stochastic Simulation

Herein, the authors built and tested a stochastic simulation to estimate achieved detection probabilities (DPs) on a stratum basis, over a tailorable range of diverted amounts from 0 to 2 SQ, using typical IAEA inspection data: i.e., SQ in stratum, number of items, number of gross/partial/bias defect measurements conducted, and realistic relative standard deviation (RSD) values for typical IAEA verification measurements. For bulk strata, the model calculates achieved DP at 0.01 SQ diversion increments; for item strata, the model calculates DP using the smallest realistic diversion increment (e.g., a plate, pin, or coupon). After successfully benchmarking against IAEA deterministic models, the simulation was used to test the sensitivity of DP to certain standard assumptions and selected input parameters. First, the equal defect assumption was tested; the results suggest significant complexity in the effectiveness of partial defect measurements. Next, the authors explored the sensitivity of DP to the assumed RSD of attribute tests. Then, the authors compared non-normal models for instrument performance (e.g., logistic, step, or arbitrary functions) to the typical results from a normal distribution (characterized by RSD). This last comparison was supplemented with experimentally derived performance data for an HM-5. The HM-5 was used to make enrichment measurements on both LEU and HEU MTR fuel elements as plates were removed, and the results fit with logistic and step curves and applied in the simulation. These stochastic DP results were compared to DP estimates from a deterministic model assuming a normal curve and typical RSD, yielding insights that could improve effectiveness in the field. These early results illustrate the potential of stochastic models to better understand achieved DP and to improve safeguards effectiveness.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗