Key Measurement Points Optimization
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Efforts have been underway for many years on the development of safeguards for fast neutron reactors. The efforts have been primarily for sodium-cooled fast reactors (SFRs) in cooperation with Japan, to properly implement these safeguards as the reactors are conceptualized and ultimately commercialized. The primary challenge to development of adequate safeguard approaches for fast reactors has been in understanding how to apply different aspects of safeguards when facilities widely vary from one type of fast reactor to another and is largely dependent on several key aspects of the specific reactor type. For this report, the following key areas were identified for further analysis to determine where Lead-cooled Fast Reactors (LFRs) and Gas-Cooled Fast Reactors (GCFR) fuel material flow and key measurement points would differ from previously studied SFR safeguards.
Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.
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.
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.
Sensitivity and uncertainty methods are quintessential for nuclear criticality safety and experiment design. This type of analysis relies on calculations of sensitivity coefficients; sensitivity coefficients of the effective neutron multiplication factor with respect to some nuclear data are predominantly calculated and used. As a part of the Laboratory Directed Research & Development project EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) at Los Alamos National Laboratory, sensitivity coefficients of many radiation detector measurement responses with respect to nuclear data were investigated. Specifically, this paper outlines a method to calculate point-kinetics parameters relative sensitivity coefficients with respect to nuclear data. Point-kinetics parameters such as the prompt neutron decay constant, effective delayed neutron fraction, and neutron generation time are especially important to experimenters and reactor operators designing systems with dynamic neutron populations. This method couples capabilities of the ACE (A Compact ENDF) nuclear data file toolkit, ACEtk, with the ability to load cross sections into the radiation transport code Monte Carlo N-Particle (MCNP). In conclusion, key aspects of optimizing this method for a particular application and sensitivity profiles of the Jezebel criticality experiment are examined and discussed.
This work evaluated the use of scintillating fiber (SciFi) radiation sensors as a cost-effective, unattended monitoring technique for uranium holdup accumulation in reprocessing equipment. With ruggedized packaging and an outer diameter of less than 1 cm, scintillating fibers offer deployment flexibility. Additionally, plastic scintillating fibers coupled with standard optical fibers provide an affordable option to monitor locations kilometers away from a localized control room or data acquisition (DAQ) system. These combinations of characteristics along with their relatively low manufacturing costs make scintillating fibers ideal for installation at key measurement points such as pipes, valves, and filtration systems.
A tool was developed to rapidly generate synthetic gamma-ray spectra to evaluate safeguards material control and accounting methods for liquid-fueled molten salt reactors. Molten salt reactor operations pose unique challenges to nuclear safeguards methods and protocols compared to deployed reactors designs (e.g., light water reactors). This research evaluates the use of gamma-ray spectroscopy to monitor fission product isotopic flow through a reactor model to understand expected operations and investigate changes to the spectra with material diversion scenarios. The large design space of molten salt reactors (e.g., liquid-fueled, liquid-cooled, online separations) could potentially lead to many measurement points within the reactor system. The developed analytical tool generates and evaluates synthetic gamma-ray spectra from dynamic reactor simulations by extracting isotopic inventory to generate source terms. An implementation in the Gamma Detector Response and Analysis Software (GADRAS) Application Program Interface (API) uses the source terms, a model of the reactor component, a detector response function, and measurement plan to quickly generate and analyze spectra. Prospective measurements are then evaluated in the more accurate but slower Geant4 simulations. Utilization of the developed modeling tool and analysis of the subsequent spectra enables optimization of collimation, shielding requirements, and expected count rates that are used to determine key measurement points in the modeled reactor design.
Sabotage of nuclear plants and theft of special nuclear material are different from many other issues potentially affecting public health and safety, and some of those differences drive the content of the present report. A high-level indication of these differences is provided in the US Nuclear Regulatory Commission’s Safety Goal Policy. Promulgated in the mid-1980’s, when it had become reasonably clear that risk analysis had improved to the point where it was possible to understand the risks associated with plant operation, the Safety Goal Policy articulates qualitative safety goals and quantitative health objectives that are meant to guide regulatory and risk management activities, with the following key exceptions noted in the original policy statement: The possible effects of sabotage or diversion of nuclear material are also not presently included in the safety goals. At present there is no basis on which to provide a measure of risk on these matters. It is the Commission’s intention that everything that is needed will be done to keep these types of risks at their present very low level; and it is the Commission’s expectation that efforts on this point will continue to be successful. With these exceptions, it is the Commission’s intent that the risks from all the various initiating mechanisms be considered to the best of the capability of current evaluation techniques. The present report discusses extensions of classical risk management to address some of the special issues that arise in the context of security. Although the present emphasis is on physical security, some attention will be paid to cyber security. A particular focus of the report is on quantitative framework to manage and address uncertainties. This framework is demonstrated via a couple of hypothetical examples.