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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Experimental demonstration and modeling of a robotic neutron detector with spectral and directional sensitivity for treaty verification

International safeguards and arms control agreements often require labor-intensive, intrusive onsite inspections to perform verification tasks. The ability to localize a neutron source and/or characterize a neutron field may be imperative for identifying anomalies. We are interested in the role of autonomous mobile robots, which, if designed properly, may be more effective and efficient and less intrusive than their human counterparts. Toward developing such a capability, we, for this study, have constructed the N-SpecDir Bot, comprised of three boron-coated straw detectors azimuthally-distributed within a cylinder of high-density polyethylene, which is mounted on an omni-directional robotic platform. Our N-SpecDir Bot is specifically designed to provide spectral and directional sensitivity, in addition to gross counts, by utilizing the signals from the three detectors. The detection system has been extensively characterized by MCNP modeling, which has been benchmarked to experiments conducted at the Princeton Plasma Physics Laboratory. We demonstrate the spectral and directional sensitivity experimentally and in simulation, and provide a simple yet robust method for directional measurements.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Neutron spectroscopy of plutonium using a handheld detection system

The ability to distinguish multiple forms of plutonium from one another, such as oxide and metal, is paramount in areas of nuclear nonproliferation and international safeguards. In its metal form, plutonium can be readily used in a nuclear weapon, while oxide forms are associated with nuclear reactor fuel. Oxide-based plutonium forms emit neutrons with an energy spectrum that is significantly different from the fission neutrons that are emitted from plutonium metal. Organic scintillation detectors output pulses that are proportional to the neutron energy deposited, and therefore present a means of distinguishing these plutonium forms based on their energy spectra. In this work, metal and oxide forms of plutonium were measured using a handheld detection system based on an organic glass scintillator. Monte Carlo modeling of these experiments was performed to provide insight into the origin of the features in the observed light output spectra. Through analysis of multiple regions of these spectra, in a matter of minutes we were able to unambiguously discriminate oxide and metal plutonium forms from one another and from a plutonium-beryllium neutron source, which was considered for comparison because these sources are commonly used in industrial applications. The ability to discriminate weapons-usable material from nuclear reactor fuel has applications in nuclear treaty verification and safeguards.

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

Total Measurement Uncertainty in Neutron Coincidence Multiplicity Analysis

Neutron multiplicity counting is the most commonly used nondestructive assay technique for determining the plutonium mass within containers of scrap PuO 2 or mixed oxide (MOX). In multiplicity analysis, the 240 Pu eff mass, leakage multiplication, and alpha ratio (the ratio of [α, n]-to-spontaneous fission neutron production) are the three primary unknown sample properties. They must be determined simultaneously. To solve for these three unknowns in a multiplicity assay, three measured values are needed: the singles, doubles, and triples neutron count rates. While the analysis is limited to solving for three unknowns, there are many additional factors that impact the observed count rates and contribute to the measurement uncertainty. In this study we investigate the various uncertainty contributors for the multiplicity analysis through a combination of traditional uncertainty propagation techniques supplemented by Monte Carlo simulations to address the dependences not explicitly expressed by the point source model. Uncertainties arising from counting statistics, calibration parameters, calibration method, nuclear data, and various material characteristics (isotopic abundances, chemical form, density, and impurities) are considered. A Total Measurement Uncertainty (TMU) estimate is then developed from these uncertainty contributors. This study is confined to multiplicity analysis of items commonly encountered in international safeguards applications. That is, the study focused on Pu oxides and MOX materials for the masses ranging up to 4000 grams total Pu. Multiplicity measurements were simulated using MCNP V6 based on the Plutonium Scrap Multiplicity Counter (PSMC), Epithermal Multiplicity Counter (ENMC), Pyrochemical Multiplicity Counter, and Large Epithermal Multiplicity Counter (LEMC) for this study; however, this report focuses on the parameterization of the uncertainties for the PSMC. The performance differences between the PSMC and the other multiplicity counting systems are relatively small, primarily manifesting in the impact on measurement precision so that the evaluation developed for the PSMC can be applied to the other multiplicity counting systems. Finally an analysis tool, the Multiplicity TMU Estimator, was developed from this study to serve as an aid for evaluation of the total measurement uncertainty of multiplicity assay results obtained from the commonly used INCC acquisition and analysis software.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

FRAM v.7.1

Fixed-energy Response-function Analysis with Multiple efficiency (FRAM) is a software code designed primarily for plutonium and uranium isotopic analysis. It is widely used in both the domestic and international safeguards community. FRAM can quickly and accurately determine the isotopic compositions of plutonium, uranium, and mixed oxides (MOX) when measuring with a high-purity germanium (HPGe), cadmium zinc telluride (CZT), or lanthanum bromide (LaBr3) detector. The capabilities of FRAM have been enhanced to analyze the data of the pixelated CZT detector (made by H3D) and to measure the mass of plutonium, uranium, and MOX. Both the isotopic composition and mass of the item can be quickly determined with one measurement using a gamma detector with FRAM v.7.1.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

24.1.3.4 Upgraded Fixed Energy Response Function Analysis with Multiple Efficiencies (FRAM) Software for U/Pu/MOX Mass Measurements

Fixed-energy Response-function Analysis with Multiple efficiency (FRAM) is a software code designed primarily for plutonium and uranium isotopic analysis. It is widely used in both the domestic and international safeguards community. FRAM can quickly and accurately determine the isotopic compositions of plutonium, uranium, and mixed oxides (MOX) when measuring with a high-purity germanium (HPGe), cadmium zinc telluride (CZT), or lanthanum bromide (LaBr 3 ) detector. The capabilities of FRAM have been enhanced to analyze the data of the pixelated CZT detector (made by H3D) and to measure the mass of plutonium, uranium, and MOX. Both the isotopic composition and mass of the item can be quickly determined with one measurement using a gamma detector with FRAM v.7.1.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Field Scoping Study of Gamma-Ray Measurement of UF 6 Cristillini Samples with CZT and HPGe Detectors

A field scoping study was conducted to evaluate the ability of Cd-Zn-Te (CZT) detectors and High Purity Germanium (HPGe) detectors to quantify the U-235 enrichment in UF 6 samples collected on alumina in P-10 tubes, similar to the Cristillini method of sample collection for analysis by mass spectrometry. With in-field gamma-ray spectroscopy methods, international safeguards and verification missions could benefit from quick in-field confirmation of declared enrichment values prior to or without the need for additional shipping, sample preparation and analysis. Samples of depleted (DU), low-enriched (LEU) and high-assay low-enriched (HALEU) uranium were collected on Cristillini-type alumina in P-10 tubes and measured with two H3D, Inc. M400 CZT detectors as well as a Canberra Falcon HPGe detector. The M400 detectors were able to confirm enrichment in the DU and LEU samples but not the HALEU sample in measurements 8-40 hrs after collection. The Falcon system was able to confirm enrichment in all three samples. Improvements in detector geometry, increase in number of detectors as well as increase sample mass loading may allow measurement of U-235 enrichment in these types of samples within 4-8hrs of collection with 5-10% uncertainty.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2023

Quantification of LEU Holdup using gamma ray imaging and inverse transport solver

Holdup is the residual amount of special nuclear material (SNM) remaining in a processing facility after the bulk materials have been cleaned out. In commercial uranium processing facilities, quantification of holdup is a major challenge because of the highly variable shapes and sizes of the deposits. Any method that attempts to generalize and calibrate deposit shapes in order to quantify holdup will be prone to high uncertainties. Uncertainties on the order of ±50% are typical in holdup results. In international safeguards applications, a ±50% uncertainty can result in a large amount of material unaccounted for (MUF) thereby increasing the difficulty of detecting material diversion and facility misuse. An imaging-based methodology has been developed with the objective of significantly reducing this uncertainty by using the true deposit shape, instead of relying on oversimplified geometric assumptions. The project is a collaboration between ORNL, Y-12, and the University of Tennessee, Knoxville, TN. Uranium sources of known masses were measured using the Germanium Gamma-ray Imager (GeGI), a high-resolution imaging spectrometer, creating a pixelated map for each spectral bin. Two different gamma imaging methods are employed in this work: coded aperture imaging and Compton imaging. A validated MonteCarlo model of the detector has been developed using the GEANT4 code for determining the intrinsic response of the detector, its enclosure, and the coded aperture mask. An inverse transport solver based on the Markov Chain Monte-Carlo approach known as Differential Evolution Adaptive Metropolis (DREAM) is employed to use the measurement data from the image pixels (coded aperture or Compton) to solve for the mass of 235 U in the deposit. A reliable method based on the DREAM solver has been developed to flag the infinite thickness condition of a uranium deposit. The project team is working towards improving the image reconstruction for Compton imaging so that a better localization of the source can be achieved. Besides treating the coded aperture and Compton imaging methods independently, the project is also evaluating a combined method that uses the Compton scatter data from a coded aperture measurement. GEANT4 simulations are being performed to evaluate the combined approach. The impact on the DREAM optimization as the source thickness progressively approaches infinite thickness is being evaluated. A number of uranium sources available at ORNL have been measured, and the DREAM results have been tested and validated for the coded aperture imaging. A similar effort will be carried out to validate the Compton based method once the development of algorithms for better localization are complete. The imaging based quantification is very amenable to unattended monitoring of holdup accumulation at key measurement points. A proof of concept measurement has been completed to demonstrate this capability The current work used the high energy resolution imager GeGI. However, the approach and methodologies are applicable to other imagers such as the cadmium zin telluride (CZT) based imager manufactured by H3D, Inc.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
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 OSTI2022

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets

The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Responsibly Harnessing the Power of AI

Artificial Intelligence (AI) based applications are on the cusp of offering the public and private sectors tools of tremendous potential that will likely transform the world in much the same way that previous technological revolutions have. To harness these tools, a vast and rapidly increasing assortment of ethical AI guidelines and principles are being developed to manage the myriad of risks these tools pose. Documents, frameworks, standards, and regulations have been developed by private industry, research institutions, governments and nongovernmental organizations, international standards bodies and more. This work details representative approaches to ethically developing, managing, and operating current AI technologies. The various approaches are analyzed for commonality, divergence, and implementation strategy to help develop approaches for managing AI tools in the nuclear landscape. This work will support the creation of a common vocabulary and a deeper understanding of the ethical/ responsible AI landscape and advance the considerations required for structured frameworks in the international safeguards domain.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

AGN-201 Digital Twin Concept of Operations

The AGN-201 is a nuclear reactor at Idaho State University (ISU) and is currently being used in the development of a digital twin (DT) with Idaho National Laboratory (INL). The goal is to create a DT (called the AGN-201 DT) which can monitor an operating nuclear reactor to determine when the reactor is being operated normally; normal operations are any operations that is declared by the ISU staff. To accomplish this, researchers at INL developed a DT ecosystem which can ingest data from the AGN-201 reactor. This data is fed into a series of machine learning (ML) and reactor physics models. The ML and reactor physics models assess the data and determine if the reactor is operating normally. Event(s) that are flagged as anomalies are investigated by the INL staff to determine if any undeclared experiments were conducted. The AGN-201 DT was verified in July/August 2023, when the ISU staff performed undeclared experiments and the INL staff were able to assess the anomalous events and determine what likely caused each event. This project was a steppingstone to promote the use of DTs for international safeguards and marks the first time a DT was able to monitor a nuclear reactor for this purpose. This document provides the concept of operations (CONOPS) for the AGN-201 digital twin (DT). The CONOPS describes how the AGN-201 and AGN-201 DT are expected to operate and details how each are established.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Utilizing Advanced Statistics to Determine Anomalistic Conditions in Pebble-Bed Reactors

Pebble-bed reactors (PBRs) utilize hundreds of thousands of fuel pebbles, which continuously circulate through the core, in lieu of traditional fuel assemblies to generate fissions and produce power. The use of unmarked fuel pebbles presents a challenge for international safeguards verification that nuclear material is not being diverted. To ensure pebble diversion is not taking place, new methods for accounting for and monitoring the pebbles should be examined to determine an appropriate methodology for performing bulk accountancy with pebbles. Here, this work examines the use of statistical methods for determining if the reactor is within a declared range of operation by examining the statistical distribution of pebble burnup as they are discharged from the core. Using this methodology, we created a model that detects diversion over 95% of the time, over multiple diversion pathways, if the reactor core maintains a constant power density during the diversion process and only falsely labels a diversion case nominal 2% of the time. For a diversion scenario where the reactor is maintained at a constant power, the statistical analysis can correctly identify if diversion is occurring over 80% of the time; however, nearly 20% of specific diversion pathways are mislabeled nominal. These results provide a basis and framework for exploring the further use of statistical methods to determine where these methods could be most useful and where additional methods, such as machine learning, could be used to capture if diversion is occurring in pebble-bed reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Performance Testing of Software Upgrade for SRNL CPC Instrument at the Nuclear Material Laboratory, Office of Safeguards Analytical Services, International Atomic Energy Agency

This report summarizes the performance testing for the Software upgrade for the Savannah River National Laboratory (SRNL) Controlled Potential Coulometry (CPC) instrument. This upgrade is specifically to meet the needs of the Nuclear Material Laboratory, Office of Safeguards Analytical Services, International Atomic Energy Agency at Seibersdorf, Austria. The instrument hardware was upgraded in 2010, but the software continued to operate on a Windows platform using a High Tech Basic (HT Basic) application. HT Basic usage is declining. The IAEA requested an upgrade to the existing coulometer software to Laboratory Virtual Instrument Engineering Workbench (LabVIEW), an object-oriented programming language in wide use and one the NML staff is familiar with. The software was developed by SRNL Software Engineer, with assistance from Electrical Engineer and Scientist. Once developed, the new LabVIEW software was validated, tested with Iron (surrogate for plutonium) and plutonium. Once testing was completed, the software was installed at the International Atomic Energy Agency Nuclear Material Laboratory (IAEA NML) coulometer, and SRNL provided hands on training to NML staff on using the new software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Blockchain Applications for Nuclear Safeguards

This chapter discusses the benefits and challenges of using distributed ledger technology (DLT) for international nuclear safeguards purposes. The chap-ter introduces the international safeguards system and the role of the Inter-national Atomic Energy Agency. The chapter describes the evolution of the safeguards system, the IAEA’s core functions and the technical objectives it seeks to achieve. The chapter explains the benefits and drawbacks of deploying DLT for safeguards and presents potential use cases where the technology could be deployed. It ends with a description of the international com-munity’s current perspectives on deployment.

DLT, blockchain technology, international safeguar↗
DOE OSTI2024

Challenges and Technology-Driven Opportunities for Safeguarding Microreactors

Nuclear microreactors (MRs) represent a new class of reactors characterized by their compactness, portability, and low power output. These features enable MRs to supply electricity and process heat to remote areas like military bases; inaccessible locations; small grids, such as on islands; or disaster impacted areas. Compared to traditional light water reactors, MRs have a unique set of attributes that need to be considered for the implementation of safeguard strategies. Current safeguard methodologies are reactor technology specific and are employed on large, stationary reactors where there is easy access by safeguards inspectors and where safeguard equipment can be easily installed and retrofitted. While there are numerous benefits to MRs, their compact size, portability, scalability, and operational lifetime create challenges to the traditional safeguard approaches, thus needing novel safeguard strategies. Here, this paper addresses the unique challenges posed by MRs to the international nuclear safeguards regime, including limited human resources, and explores how technology advancements can help mitigate these challenges. Specifically, it examines novel technologies that could contribute to establishing a comprehensive safeguards framework for MRs. These safeguards-enabling technologies encompass safeguards by design, remote sensing and monitoring technologies, applications of artificial intelligence and machine learning algorithms, utilization of digital twins, and system of systems assessments. While each of these safeguards-enabling technologies offers partial solutions to the challenges posed by MRs for the international safeguards regime, none of them alone can entirely address these challenges. Consequently, a combination of the safeguards-enabling technologies outlined in this paper is recommended to establish a robust safeguards regime for MRs.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗